FU
F. Uysal
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12 records found
1
Semi Bistatic Radar
Subgroup - Receiver Antenna
In this thesis, a design of a passive radar for use on a drone used in inhospitable areas where air traffic control is not available due to circumstances. The thesis focusses on the receiver chain and angle of arrival algorithm.
...
In this thesis, a design of a passive radar for use on a drone used in inhospitable areas where air traffic control is not available due to circumstances. The thesis focusses on the receiver chain and angle of arrival algorithm.
Bi-Static Sense and Avoid System for Drones
Signal Design
Bachelor thesis
(2020)
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M. Abo Alainein, O. El Boustani, F. Uysal, D. Cavallo, G.J.T. Leus, O. Dogan
In the context of the Bachelor Graduation Project at the Delft University of Technology Department of Electrical Engineering, we have been tasked with a project to design prototype of a bi-static radar. This thesis describes the waveform design of a bi-static radar system and its implementation with Software Design Radio, SDR for short. The radar system can be mounted on delivery drones and used to detect other drones and obstacles to avoid collisions. The thesis focuses mainly on the radar waveform design. In addition, the thesis provides analysis of the radar system performance and system implementation with commercial off the shelf SDR. A proof of concept has been realised by means of simulations using the open source software GNU Radio Companion.
...
In the context of the Bachelor Graduation Project at the Delft University of Technology Department of Electrical Engineering, we have been tasked with a project to design prototype of a bi-static radar. This thesis describes the waveform design of a bi-static radar system and its implementation with Software Design Radio, SDR for short. The radar system can be mounted on delivery drones and used to detect other drones and obstacles to avoid collisions. The thesis focuses mainly on the radar waveform design. In addition, the thesis provides analysis of the radar system performance and system implementation with commercial off the shelf SDR. A proof of concept has been realised by means of simulations using the open source software GNU Radio Companion.
Wings for Aid is a company which develops an airborne aid-delivery system in emergency-struck areas and wants to use the radar system of Selfly to make an autonomously flying system. The goal of this project is to optimise the current ground-based radar system of Selfly for this application. This is done through the design of a Bi-static radar system, which can be mounted on, and used for drones to detect and avoid other obstacles in the airspace.\\
This thesis describes the design process and results of a sub-system of the Bi-static Sense and Avoid System for Drones (BiSAD). This a Bi-static radar system, consisting of three sub-systems that together form the base of the radar system. There will be one transmitter drone and several smaller receiver drones which are designed. Also, an optimal waveform is created for this purpose.\\
The report gives a proof of concept and will focus on a proof of concept for the transmitter chain as well as highlight some total system design choices and results. ...
This thesis describes the design process and results of a sub-system of the Bi-static Sense and Avoid System for Drones (BiSAD). This a Bi-static radar system, consisting of three sub-systems that together form the base of the radar system. There will be one transmitter drone and several smaller receiver drones which are designed. Also, an optimal waveform is created for this purpose.\\
The report gives a proof of concept and will focus on a proof of concept for the transmitter chain as well as highlight some total system design choices and results. ...
Wings for Aid is a company which develops an airborne aid-delivery system in emergency-struck areas and wants to use the radar system of Selfly to make an autonomously flying system. The goal of this project is to optimise the current ground-based radar system of Selfly for this application. This is done through the design of a Bi-static radar system, which can be mounted on, and used for drones to detect and avoid other obstacles in the airspace.\\
This thesis describes the design process and results of a sub-system of the Bi-static Sense and Avoid System for Drones (BiSAD). This a Bi-static radar system, consisting of three sub-systems that together form the base of the radar system. There will be one transmitter drone and several smaller receiver drones which are designed. Also, an optimal waveform is created for this purpose.\\
The report gives a proof of concept and will focus on a proof of concept for the transmitter chain as well as highlight some total system design choices and results.
This thesis describes the design process and results of a sub-system of the Bi-static Sense and Avoid System for Drones (BiSAD). This a Bi-static radar system, consisting of three sub-systems that together form the base of the radar system. There will be one transmitter drone and several smaller receiver drones which are designed. Also, an optimal waveform is created for this purpose.\\
The report gives a proof of concept and will focus on a proof of concept for the transmitter chain as well as highlight some total system design choices and results.
Exploiting a multi channel receiver array in ISAR imaging
Concerning clutter suppression for maritime targets in an airborne setup
Master thesis
(2020)
-
Niels de Jong, Faruk Uysal, Alexander Yarovoy, Paco Lopez Dekker, J.J.M. de Wit
ISAR imaging is a well known technique which uses range and Doppler information from a radar to generate an image of a moving target. Research performed on the combination of a large receive array with airborne ISAR is scarce. This thesis is aimed to reach the highest possible image quality for AMBER, a radar with a 24 element receive array which was developed by TNO. Optimizing the image quality was split up in two parts, motion compensation and clutter suppression. A literature study is performed to research the existing methods. A performance evaluation is performed on different motion compensation techniques based on a simulation which is set up to resemble the radar parameters of AMBER. Based on the results of this evaluation, keystone formatting combined with image contrast maximization is concluded to be the best fitting approach. For clutter suppression a similar approach is followed with real measured data. Apart from the existing techniques, MVDR, DPCA and ODPCA, a new technique is proposed which filtered a target from clutter based on motion. It is referenced to as the IDPCA. The techniques show better image quality than regular beam forming. To enhance the image quality more, a method is searched to combine different clutter suppression techniques. This leads to a unique method which is proposed in this thesis. The new technique exploits the circular phase variance of pixels in the range Doppler image between different sub arrays. With this approach, strong clutter can be filtered in the range Doppler domain based on its angle of arrival. The newly proposed technique is applied as a mask, as it does not contain amplitude information of the target. Combined with the ODPCA method, the final image is generated. The combination of techniques shows clear improvements from the other discussed approaches.
...
ISAR imaging is a well known technique which uses range and Doppler information from a radar to generate an image of a moving target. Research performed on the combination of a large receive array with airborne ISAR is scarce. This thesis is aimed to reach the highest possible image quality for AMBER, a radar with a 24 element receive array which was developed by TNO. Optimizing the image quality was split up in two parts, motion compensation and clutter suppression. A literature study is performed to research the existing methods. A performance evaluation is performed on different motion compensation techniques based on a simulation which is set up to resemble the radar parameters of AMBER. Based on the results of this evaluation, keystone formatting combined with image contrast maximization is concluded to be the best fitting approach. For clutter suppression a similar approach is followed with real measured data. Apart from the existing techniques, MVDR, DPCA and ODPCA, a new technique is proposed which filtered a target from clutter based on motion. It is referenced to as the IDPCA. The techniques show better image quality than regular beam forming. To enhance the image quality more, a method is searched to combine different clutter suppression techniques. This leads to a unique method which is proposed in this thesis. The new technique exploits the circular phase variance of pixels in the range Doppler image between different sub arrays. With this approach, strong clutter can be filtered in the range Doppler domain based on its angle of arrival. The newly proposed technique is applied as a mask, as it does not contain amplitude information of the target. Combined with the ODPCA method, the final image is generated. The combination of techniques shows clear improvements from the other discussed approaches.
Estimating 3D motion from radar data
Exploiting an omnidirectional radar array for motion estimation in the context of SAR imaging on agile platforms
Acquiring an accurate estimate of position is a challenging problem in coherent radar processing techniques such as Synthetic Aperture Radar (SAR). Even more so, for light and agile platforms such as multi-copters. Due to their unpredictable flight path, their motion must be accurately measured during data acquisition, and compensated for during processing. To obtain a focused SAR image, each pulse location must be accurately known to ensure that each pulse is added coherently to the imaging grid. Traditionally, this is achieved with an Inertial Navigation System (INS). While an INS can provide reasonable performance, its weight and size are often a constraint for agile platforms, limiting the available options and attainable accuracy.
In this study, we perform an analysis on the applicability of an omnidirectional radar array for explicitly estimating the motion of a multi-copter platform, and improving on the positioning accuracy achieved by the on board INS. Building on existing 1D SAR motion compensation techniques, we develop new methods for estimating the 3D motion of the radar platform by estimating its height and velocity. In addition, we also present a novel 3D autofocus technique termed multi-beam autofocus. This technique allows for the correction of 3D trajectory errors from pulse to pulse by exploiting the beamforming capabilities of the array, and focusing multiple regions as the image is created.
Using an Extended Kalman Filter (EKF), we obtain position estimates from the radar velocity measurements based on the last known INS position. We experimentally verify that using our velocity estimation method alone, the positioning performance is already improved compared to that of a state-of-the-art INS, allowing for INS-free imaging using arbitrary flight paths. This enables imaging in GNSS-denied environments, and has the potential to further reduce the weight of the platform. We also show that fusing the estimates obtained from our method with the existing INS output yields an additional performance increase in terms of SAR image focus, improving the resolvability and detectability of weak targets. The presented results open further avenues of research, not only in agile SAR imaging but also in autonomous GNSS-denied navigation. ...
In this study, we perform an analysis on the applicability of an omnidirectional radar array for explicitly estimating the motion of a multi-copter platform, and improving on the positioning accuracy achieved by the on board INS. Building on existing 1D SAR motion compensation techniques, we develop new methods for estimating the 3D motion of the radar platform by estimating its height and velocity. In addition, we also present a novel 3D autofocus technique termed multi-beam autofocus. This technique allows for the correction of 3D trajectory errors from pulse to pulse by exploiting the beamforming capabilities of the array, and focusing multiple regions as the image is created.
Using an Extended Kalman Filter (EKF), we obtain position estimates from the radar velocity measurements based on the last known INS position. We experimentally verify that using our velocity estimation method alone, the positioning performance is already improved compared to that of a state-of-the-art INS, allowing for INS-free imaging using arbitrary flight paths. This enables imaging in GNSS-denied environments, and has the potential to further reduce the weight of the platform. We also show that fusing the estimates obtained from our method with the existing INS output yields an additional performance increase in terms of SAR image focus, improving the resolvability and detectability of weak targets. The presented results open further avenues of research, not only in agile SAR imaging but also in autonomous GNSS-denied navigation. ...
Acquiring an accurate estimate of position is a challenging problem in coherent radar processing techniques such as Synthetic Aperture Radar (SAR). Even more so, for light and agile platforms such as multi-copters. Due to their unpredictable flight path, their motion must be accurately measured during data acquisition, and compensated for during processing. To obtain a focused SAR image, each pulse location must be accurately known to ensure that each pulse is added coherently to the imaging grid. Traditionally, this is achieved with an Inertial Navigation System (INS). While an INS can provide reasonable performance, its weight and size are often a constraint for agile platforms, limiting the available options and attainable accuracy.
In this study, we perform an analysis on the applicability of an omnidirectional radar array for explicitly estimating the motion of a multi-copter platform, and improving on the positioning accuracy achieved by the on board INS. Building on existing 1D SAR motion compensation techniques, we develop new methods for estimating the 3D motion of the radar platform by estimating its height and velocity. In addition, we also present a novel 3D autofocus technique termed multi-beam autofocus. This technique allows for the correction of 3D trajectory errors from pulse to pulse by exploiting the beamforming capabilities of the array, and focusing multiple regions as the image is created.
Using an Extended Kalman Filter (EKF), we obtain position estimates from the radar velocity measurements based on the last known INS position. We experimentally verify that using our velocity estimation method alone, the positioning performance is already improved compared to that of a state-of-the-art INS, allowing for INS-free imaging using arbitrary flight paths. This enables imaging in GNSS-denied environments, and has the potential to further reduce the weight of the platform. We also show that fusing the estimates obtained from our method with the existing INS output yields an additional performance increase in terms of SAR image focus, improving the resolvability and detectability of weak targets. The presented results open further avenues of research, not only in agile SAR imaging but also in autonomous GNSS-denied navigation.
In this study, we perform an analysis on the applicability of an omnidirectional radar array for explicitly estimating the motion of a multi-copter platform, and improving on the positioning accuracy achieved by the on board INS. Building on existing 1D SAR motion compensation techniques, we develop new methods for estimating the 3D motion of the radar platform by estimating its height and velocity. In addition, we also present a novel 3D autofocus technique termed multi-beam autofocus. This technique allows for the correction of 3D trajectory errors from pulse to pulse by exploiting the beamforming capabilities of the array, and focusing multiple regions as the image is created.
Using an Extended Kalman Filter (EKF), we obtain position estimates from the radar velocity measurements based on the last known INS position. We experimentally verify that using our velocity estimation method alone, the positioning performance is already improved compared to that of a state-of-the-art INS, allowing for INS-free imaging using arbitrary flight paths. This enables imaging in GNSS-denied environments, and has the potential to further reduce the weight of the platform. We also show that fusing the estimates obtained from our method with the existing INS output yields an additional performance increase in terms of SAR image focus, improving the resolvability and detectability of weak targets. The presented results open further avenues of research, not only in agile SAR imaging but also in autonomous GNSS-denied navigation.
Localization of Autonomous Vehicle using 1D Automotive Radar Sensor
Car localization based on Radar and LIDAR Fusion
The overall purpose of this study is to establish novel methods of vehicle localization and mapping using a 1D linear automotive radar array in conjuncture with pre-existing lidar maps, and to test if the generated radar map can be made to be 3 dimensional. The reason for creating a novel localization method was to try and alleviate some of the weaknesses of existing localization techniques, such as GPS'(Global Positioning System) inability to localize in tunnels and other large structures, or lidar based positionings' low quality of service in certain weather conditions. The basic design of this study was to implement a SLAM(Simultaneous Localization And Mapping) system that co-registers radar data to radar data, and to then attempt to register radar data to lidar data, all taken from experiments with cars with these sensors driving around the TU Delft campus. After the execution of experiments, both on simulations and on real world data, it was established that it is in fact possible to localize the car by relating observed radar data to premade lidar maps, and to continually add to a cumulative map made with the radar data that can further aid the localization process. Furthermore, it can be observed that the radar map created using the 1D linear automotive array can be made to be 3D, though more experiments to establish the full potential of this capability are recommended.
...
The overall purpose of this study is to establish novel methods of vehicle localization and mapping using a 1D linear automotive radar array in conjuncture with pre-existing lidar maps, and to test if the generated radar map can be made to be 3 dimensional. The reason for creating a novel localization method was to try and alleviate some of the weaknesses of existing localization techniques, such as GPS'(Global Positioning System) inability to localize in tunnels and other large structures, or lidar based positionings' low quality of service in certain weather conditions. The basic design of this study was to implement a SLAM(Simultaneous Localization And Mapping) system that co-registers radar data to radar data, and to then attempt to register radar data to lidar data, all taken from experiments with cars with these sensors driving around the TU Delft campus. After the execution of experiments, both on simulations and on real world data, it was established that it is in fact possible to localize the car by relating observed radar data to premade lidar maps, and to continually add to a cumulative map made with the radar data that can further aid the localization process. Furthermore, it can be observed that the radar map created using the 1D linear automotive array can be made to be 3D, though more experiments to establish the full potential of this capability are recommended.
With the ever-expanding need for accuracy in the world of navigation, Global Navigation Satellite Systems(GNSS) such as GPS and Galileo have become the primary option around the world. As such, the potential damage that can be caused by malicious tampering with the receivers continues to grow. One such threat, known as Spoofing, comprises of transmission of altered GNSS signals to a target receiver(s). This process leads to false positional and/or time data on the receiver's end. Spoofing has been a topic of discussion for roughly two decades. With many theoretical approaches to its detection, this work focuses on the Angle of Arrival technique via the construction of a synthetic array from a single moving element, aided by positional information provided by Inertial Measurement Unit (IMU). This method relies on the periodic nature of the L1 signal, and focuses primarily on the case of GPS as an example. Using simulation and sample data, the possibility and limitations of constructing virtual antenna arrays is explored. It is shown that despite being viable for low number of sources during the simulation, the complexity of signal propagation within the real world implementation of GNSS system renders this technique inoperable in its first iteration.
...
With the ever-expanding need for accuracy in the world of navigation, Global Navigation Satellite Systems(GNSS) such as GPS and Galileo have become the primary option around the world. As such, the potential damage that can be caused by malicious tampering with the receivers continues to grow. One such threat, known as Spoofing, comprises of transmission of altered GNSS signals to a target receiver(s). This process leads to false positional and/or time data on the receiver's end. Spoofing has been a topic of discussion for roughly two decades. With many theoretical approaches to its detection, this work focuses on the Angle of Arrival technique via the construction of a synthetic array from a single moving element, aided by positional information provided by Inertial Measurement Unit (IMU). This method relies on the periodic nature of the L1 signal, and focuses primarily on the case of GPS as an example. Using simulation and sample data, the possibility and limitations of constructing virtual antenna arrays is explored. It is shown that despite being viable for low number of sources during the simulation, the complexity of signal propagation within the real world implementation of GNSS system renders this technique inoperable in its first iteration.
Multimodal Deep Learning for the Classification of Human Activity
Radar and Video data fusion for the classification of human activity
Persistent surveillance is an urgent proficiency. For security, surveillance cameras are a strong asset as they support the automatic tracking of people and are directly interpretable by a human operator. Radar on the other hand can be used under a broad range of circumstances: radar can penetrate mediums such as clouds, fogs, mist and snow, and it can be used when it gets dark.
However radar data, compared to an optical sensor as video, is not as easily interpretable by a human operator. This thesis explores the potential of multimodal deep learning with a radar and video sensor to improve the classification accuracy of human activity. A recorded and labelled dataset is created that contains three different human activities: walking, walking with a metal pole and walking with a backpack (10 kg). A Single Shot Detector is used to process the video data. The cropped frames are then associated with the start of a radar micro-Doppler signature with a duration of 1.28 seconds. The dataset is split in a training (80 %) and validation (20 %) set such that no data from a person in the training set is in the validation set. Implementations of convolutional neural networks for the video frames and micro-Doppler signatures obtain classification accuracies of 85.78 % and 63.12 % respectively for previously mentioned activities. It was not possible to distinguish a person walking and walking carrying a backpack on basis of the micro-Doppler signatures. The synchronised dataset is used to investigate different fusion methods. Both early and late fusion methods show an improvement in classification accuracy. The best obtained early fusion model achieves a classification accuracy of 90.60 %. Omitting the radar data however shows a drop in classification accuracy of just 0.9 %, identifying the video data as the dominant modality in this particular setup. ...
However radar data, compared to an optical sensor as video, is not as easily interpretable by a human operator. This thesis explores the potential of multimodal deep learning with a radar and video sensor to improve the classification accuracy of human activity. A recorded and labelled dataset is created that contains three different human activities: walking, walking with a metal pole and walking with a backpack (10 kg). A Single Shot Detector is used to process the video data. The cropped frames are then associated with the start of a radar micro-Doppler signature with a duration of 1.28 seconds. The dataset is split in a training (80 %) and validation (20 %) set such that no data from a person in the training set is in the validation set. Implementations of convolutional neural networks for the video frames and micro-Doppler signatures obtain classification accuracies of 85.78 % and 63.12 % respectively for previously mentioned activities. It was not possible to distinguish a person walking and walking carrying a backpack on basis of the micro-Doppler signatures. The synchronised dataset is used to investigate different fusion methods. Both early and late fusion methods show an improvement in classification accuracy. The best obtained early fusion model achieves a classification accuracy of 90.60 %. Omitting the radar data however shows a drop in classification accuracy of just 0.9 %, identifying the video data as the dominant modality in this particular setup. ...
Persistent surveillance is an urgent proficiency. For security, surveillance cameras are a strong asset as they support the automatic tracking of people and are directly interpretable by a human operator. Radar on the other hand can be used under a broad range of circumstances: radar can penetrate mediums such as clouds, fogs, mist and snow, and it can be used when it gets dark.
However radar data, compared to an optical sensor as video, is not as easily interpretable by a human operator. This thesis explores the potential of multimodal deep learning with a radar and video sensor to improve the classification accuracy of human activity. A recorded and labelled dataset is created that contains three different human activities: walking, walking with a metal pole and walking with a backpack (10 kg). A Single Shot Detector is used to process the video data. The cropped frames are then associated with the start of a radar micro-Doppler signature with a duration of 1.28 seconds. The dataset is split in a training (80 %) and validation (20 %) set such that no data from a person in the training set is in the validation set. Implementations of convolutional neural networks for the video frames and micro-Doppler signatures obtain classification accuracies of 85.78 % and 63.12 % respectively for previously mentioned activities. It was not possible to distinguish a person walking and walking carrying a backpack on basis of the micro-Doppler signatures. The synchronised dataset is used to investigate different fusion methods. Both early and late fusion methods show an improvement in classification accuracy. The best obtained early fusion model achieves a classification accuracy of 90.60 %. Omitting the radar data however shows a drop in classification accuracy of just 0.9 %, identifying the video data as the dominant modality in this particular setup.
However radar data, compared to an optical sensor as video, is not as easily interpretable by a human operator. This thesis explores the potential of multimodal deep learning with a radar and video sensor to improve the classification accuracy of human activity. A recorded and labelled dataset is created that contains three different human activities: walking, walking with a metal pole and walking with a backpack (10 kg). A Single Shot Detector is used to process the video data. The cropped frames are then associated with the start of a radar micro-Doppler signature with a duration of 1.28 seconds. The dataset is split in a training (80 %) and validation (20 %) set such that no data from a person in the training set is in the validation set. Implementations of convolutional neural networks for the video frames and micro-Doppler signatures obtain classification accuracies of 85.78 % and 63.12 % respectively for previously mentioned activities. It was not possible to distinguish a person walking and walking carrying a backpack on basis of the micro-Doppler signatures. The synchronised dataset is used to investigate different fusion methods. Both early and late fusion methods show an improvement in classification accuracy. The best obtained early fusion model achieves a classification accuracy of 90.60 %. Omitting the radar data however shows a drop in classification accuracy of just 0.9 %, identifying the video data as the dominant modality in this particular setup.
Automotive radars play a crucial role in the reduction of traffic casualties and the realization of autonomous driving due to its robustness and adverse weather tolerance. However, as the penetration rate of automotive radars increases, concerns arise regarding the mutual interference. In contrast to the predominant Frequency-Modulated Continuous Wave (FMCW) radars, Phase-Modulated Continuous Wave (PMCW) radars might provide solutions for the interference problem by coding the waveforms, which brings an additional degree of freedom. In this thesis, a system level overview of the phase-coded radar is presented. Regarding the popular code families, particular attention was given to the Gold, Almost Perfect Autocorrelation Sequences (APAS), and Zero-Correlation Zone (ZCZ) sequences. This thesis has proposed three distinct designs for 16-TX-element MIMO Phase-Coded radar that drives the requirements of a Medium Range Radar (MRR). The conclusion can be drawn that APAS and ZCZ sequences have shown to provide a (sub)optimal design that is emphasized by their perfect correlation characteristics within the designated distance of interest. Furthermore, in full-transmit capacity MIMO, the proposed designs use semi-orthogonal waveforms to provide excellent performance in synchronous PMCW radars. However, performance degrades in case Doppler-shifted reflections are received, whereas sidelobes arise in range due to non-orthogonality. This thesis concludes that the APAS-coded waveforms are most tolerant to Doppler shift; besides, its Time Division Multiplexing (TDM) MIMO Transmission scheme is computationally more efficient than the Code Division Multiplexing (CDM) scheme used in Gold and ZCZ coded waveforms. Finally, the radar-to-radar interference investigation has proven that for uncorrelated PMCW- to-PMCW interference, the noise floor undergoes a consistent increase, which is according to the interference-plus-noise power level calculated from the link budget analysis. This thesis emphasizes these results, as the interference effect in FMCW-to-FMCW (provided by NXP Semiconductors) have seen similar phenomena. Therefore, results in this thesis support the conclusion that coded waveforms do not remove or suppress the energy levels of the interference. For correlated interference, ghost targets might be formed, but this depends on the starting time as well as the starting coded bit of the interferer, as the correlation peak can be formed outside the distance of interest.
...
Automotive radars play a crucial role in the reduction of traffic casualties and the realization of autonomous driving due to its robustness and adverse weather tolerance. However, as the penetration rate of automotive radars increases, concerns arise regarding the mutual interference. In contrast to the predominant Frequency-Modulated Continuous Wave (FMCW) radars, Phase-Modulated Continuous Wave (PMCW) radars might provide solutions for the interference problem by coding the waveforms, which brings an additional degree of freedom. In this thesis, a system level overview of the phase-coded radar is presented. Regarding the popular code families, particular attention was given to the Gold, Almost Perfect Autocorrelation Sequences (APAS), and Zero-Correlation Zone (ZCZ) sequences. This thesis has proposed three distinct designs for 16-TX-element MIMO Phase-Coded radar that drives the requirements of a Medium Range Radar (MRR). The conclusion can be drawn that APAS and ZCZ sequences have shown to provide a (sub)optimal design that is emphasized by their perfect correlation characteristics within the designated distance of interest. Furthermore, in full-transmit capacity MIMO, the proposed designs use semi-orthogonal waveforms to provide excellent performance in synchronous PMCW radars. However, performance degrades in case Doppler-shifted reflections are received, whereas sidelobes arise in range due to non-orthogonality. This thesis concludes that the APAS-coded waveforms are most tolerant to Doppler shift; besides, its Time Division Multiplexing (TDM) MIMO Transmission scheme is computationally more efficient than the Code Division Multiplexing (CDM) scheme used in Gold and ZCZ coded waveforms. Finally, the radar-to-radar interference investigation has proven that for uncorrelated PMCW- to-PMCW interference, the noise floor undergoes a consistent increase, which is according to the interference-plus-noise power level calculated from the link budget analysis. This thesis emphasizes these results, as the interference effect in FMCW-to-FMCW (provided by NXP Semiconductors) have seen similar phenomena. Therefore, results in this thesis support the conclusion that coded waveforms do not remove or suppress the energy levels of the interference. For correlated interference, ghost targets might be formed, but this depends on the starting time as well as the starting coded bit of the interferer, as the correlation peak can be formed outside the distance of interest.
The automotive radars are significantly drifting towards the 77GHz electromagnetic spectrum band, and by 2020, they will no longer operate in the 24GHz band as per the regulations given by the European Telecommunications Standards Institute (ETSI) and Federal Communications Commission (FCC) in Europe and the U.S. With the communication systems also heaping towards the magnitude of radar functionality, the 77GHz spectrum has opened a broad scope and possibility to combine both communication and radar waveforms and offer joint radar sensing and communication functionality.The true aim of this thesis work is to implement one of the widely used digital modulation techniques namely binary phase shift keying to embed information bits in the frequency modulated continuous radar waveform (FMCW) via a microcontroller (MCU) to facilitate real-time radar processing and information exchange.The report starts with presenting a theoretical overview on FMCW automotive radar, waveforms for both sensing and communication and a brief review on the MCU's in the automotive radars. The next chapter of the report gives a detailed explanation of the hardware and software architectures on which the thesis implementation is carried out. The following chapter of the report focuses on the main adopted implementation to embed information bits and all the underlying methodologies related to it. Finally, the results of this thesis work are diversified into obtaining real-time 2D fast Fourier transform (FFT) range/doppler spectrum and beat signals, to perform reconstruction of the message embedded in the FMCW waveform and to validate the correctness of the phase coding.
...
The automotive radars are significantly drifting towards the 77GHz electromagnetic spectrum band, and by 2020, they will no longer operate in the 24GHz band as per the regulations given by the European Telecommunications Standards Institute (ETSI) and Federal Communications Commission (FCC) in Europe and the U.S. With the communication systems also heaping towards the magnitude of radar functionality, the 77GHz spectrum has opened a broad scope and possibility to combine both communication and radar waveforms and offer joint radar sensing and communication functionality.The true aim of this thesis work is to implement one of the widely used digital modulation techniques namely binary phase shift keying to embed information bits in the frequency modulated continuous radar waveform (FMCW) via a microcontroller (MCU) to facilitate real-time radar processing and information exchange.The report starts with presenting a theoretical overview on FMCW automotive radar, waveforms for both sensing and communication and a brief review on the MCU's in the automotive radars. The next chapter of the report gives a detailed explanation of the hardware and software architectures on which the thesis implementation is carried out. The following chapter of the report focuses on the main adopted implementation to embed information bits and all the underlying methodologies related to it. Finally, the results of this thesis work are diversified into obtaining real-time 2D fast Fourier transform (FFT) range/doppler spectrum and beat signals, to perform reconstruction of the message embedded in the FMCW waveform and to validate the correctness of the phase coding.
Target localization using MIMO-monopulse
Application on 79 GHz FMCW automotive radar
Automotive radar is widely used for driving safety support and it is a key element of future autonomous vehicles. Radar sensors have the property that the performance is not to be affected by low vision conditions compared with camera sensors or laser based radar sensors, which makes it crucial for the autonomous driving system.
Automotive radar system utilizes millimeter-wave band to detect the range, velocity and direction of arrival (DOA). Commercial 24 GHz and 77GHz radar have been well developed in vehicle applications at present, and the future trend is 79 GHz solution with wider 4 GHz bandwidth, which has been defined by the European Commission in 2004 as the frequency allocation for automotive shortrange radar systems. The main modulation method of automotive radar is Frequency-Modulated Continous-Wave(FMCW), which holds advantages including high-resolution distance measurement, quick updating and lower sampling frequency required on hardware.
%good functions in the various weather condition.
The resolution and accuracy of DOA estimation depend on the number of used antennas and their physical size. To improve the performance advanced algorithms and antenna configurations can be used. So-called super-resolution techniques can improve the estimation performance but suffer when few measurements are available and the number of targets is unknown, and the performance will also be degraded by correlated signals.
Monopulse is an established technique for radars for precision angle estimation. It enables DOA realized in only one pulse and requires quite less computation complexity. Conventional monopulse with one transmitter and two receivers and phased array monopulse have been well developed especially in tracking radar system. MIMO-monopulse has been studied in some literature, but most of them utilize distributed MIMO. The research and application of colocated MIMO-monopulse are still in progress.
In FMCW automotive radar application, clutter may have a strong effect on DOA detection and estimation of targets with low RCS, e.g. pedestrian. With Space-Time Adaptive Processing(STAP), it is possible to suppress the clutter in the angle-Doppler domain. MIMO-monopulse basically utilizes digital beamformer to generate sum and difference channel, which also allows STAP processing to obtain adaptive sum and difference weighting. With STAP clutter can be canceled, thus false detection of MIMO-monopulse will be avoided.
In this master thesis, we propose the solution of angle estimation algorithm using MIMO-monopulse based on an actual automotive radar provided by NXP using colocated MIMO antenna. The algorithm is simulated through Matlab and verified on real experimental data. The performance of the algorithm is compared with conventional DOA algorithm and the advantages and disadvantages are analyzed. A feasible extension to STAP will be discussed to suppress the clutter for preventing false DOA estimation.
Since monopulse estimator has been approved high performance in the field of single target tracking (such as low RCS pedestrian), our proposed algorithm is also validated in single and multiple target DOA estimations, which is an attractive scenario for a potential application using MIMO-monopulse. ...
Automotive radar system utilizes millimeter-wave band to detect the range, velocity and direction of arrival (DOA). Commercial 24 GHz and 77GHz radar have been well developed in vehicle applications at present, and the future trend is 79 GHz solution with wider 4 GHz bandwidth, which has been defined by the European Commission in 2004 as the frequency allocation for automotive shortrange radar systems. The main modulation method of automotive radar is Frequency-Modulated Continous-Wave(FMCW), which holds advantages including high-resolution distance measurement, quick updating and lower sampling frequency required on hardware.
%good functions in the various weather condition.
The resolution and accuracy of DOA estimation depend on the number of used antennas and their physical size. To improve the performance advanced algorithms and antenna configurations can be used. So-called super-resolution techniques can improve the estimation performance but suffer when few measurements are available and the number of targets is unknown, and the performance will also be degraded by correlated signals.
Monopulse is an established technique for radars for precision angle estimation. It enables DOA realized in only one pulse and requires quite less computation complexity. Conventional monopulse with one transmitter and two receivers and phased array monopulse have been well developed especially in tracking radar system. MIMO-monopulse has been studied in some literature, but most of them utilize distributed MIMO. The research and application of colocated MIMO-monopulse are still in progress.
In FMCW automotive radar application, clutter may have a strong effect on DOA detection and estimation of targets with low RCS, e.g. pedestrian. With Space-Time Adaptive Processing(STAP), it is possible to suppress the clutter in the angle-Doppler domain. MIMO-monopulse basically utilizes digital beamformer to generate sum and difference channel, which also allows STAP processing to obtain adaptive sum and difference weighting. With STAP clutter can be canceled, thus false detection of MIMO-monopulse will be avoided.
In this master thesis, we propose the solution of angle estimation algorithm using MIMO-monopulse based on an actual automotive radar provided by NXP using colocated MIMO antenna. The algorithm is simulated through Matlab and verified on real experimental data. The performance of the algorithm is compared with conventional DOA algorithm and the advantages and disadvantages are analyzed. A feasible extension to STAP will be discussed to suppress the clutter for preventing false DOA estimation.
Since monopulse estimator has been approved high performance in the field of single target tracking (such as low RCS pedestrian), our proposed algorithm is also validated in single and multiple target DOA estimations, which is an attractive scenario for a potential application using MIMO-monopulse. ...
Automotive radar is widely used for driving safety support and it is a key element of future autonomous vehicles. Radar sensors have the property that the performance is not to be affected by low vision conditions compared with camera sensors or laser based radar sensors, which makes it crucial for the autonomous driving system.
Automotive radar system utilizes millimeter-wave band to detect the range, velocity and direction of arrival (DOA). Commercial 24 GHz and 77GHz radar have been well developed in vehicle applications at present, and the future trend is 79 GHz solution with wider 4 GHz bandwidth, which has been defined by the European Commission in 2004 as the frequency allocation for automotive shortrange radar systems. The main modulation method of automotive radar is Frequency-Modulated Continous-Wave(FMCW), which holds advantages including high-resolution distance measurement, quick updating and lower sampling frequency required on hardware.
%good functions in the various weather condition.
The resolution and accuracy of DOA estimation depend on the number of used antennas and their physical size. To improve the performance advanced algorithms and antenna configurations can be used. So-called super-resolution techniques can improve the estimation performance but suffer when few measurements are available and the number of targets is unknown, and the performance will also be degraded by correlated signals.
Monopulse is an established technique for radars for precision angle estimation. It enables DOA realized in only one pulse and requires quite less computation complexity. Conventional monopulse with one transmitter and two receivers and phased array monopulse have been well developed especially in tracking radar system. MIMO-monopulse has been studied in some literature, but most of them utilize distributed MIMO. The research and application of colocated MIMO-monopulse are still in progress.
In FMCW automotive radar application, clutter may have a strong effect on DOA detection and estimation of targets with low RCS, e.g. pedestrian. With Space-Time Adaptive Processing(STAP), it is possible to suppress the clutter in the angle-Doppler domain. MIMO-monopulse basically utilizes digital beamformer to generate sum and difference channel, which also allows STAP processing to obtain adaptive sum and difference weighting. With STAP clutter can be canceled, thus false detection of MIMO-monopulse will be avoided.
In this master thesis, we propose the solution of angle estimation algorithm using MIMO-monopulse based on an actual automotive radar provided by NXP using colocated MIMO antenna. The algorithm is simulated through Matlab and verified on real experimental data. The performance of the algorithm is compared with conventional DOA algorithm and the advantages and disadvantages are analyzed. A feasible extension to STAP will be discussed to suppress the clutter for preventing false DOA estimation.
Since monopulse estimator has been approved high performance in the field of single target tracking (such as low RCS pedestrian), our proposed algorithm is also validated in single and multiple target DOA estimations, which is an attractive scenario for a potential application using MIMO-monopulse.
Automotive radar system utilizes millimeter-wave band to detect the range, velocity and direction of arrival (DOA). Commercial 24 GHz and 77GHz radar have been well developed in vehicle applications at present, and the future trend is 79 GHz solution with wider 4 GHz bandwidth, which has been defined by the European Commission in 2004 as the frequency allocation for automotive shortrange radar systems. The main modulation method of automotive radar is Frequency-Modulated Continous-Wave(FMCW), which holds advantages including high-resolution distance measurement, quick updating and lower sampling frequency required on hardware.
%good functions in the various weather condition.
The resolution and accuracy of DOA estimation depend on the number of used antennas and their physical size. To improve the performance advanced algorithms and antenna configurations can be used. So-called super-resolution techniques can improve the estimation performance but suffer when few measurements are available and the number of targets is unknown, and the performance will also be degraded by correlated signals.
Monopulse is an established technique for radars for precision angle estimation. It enables DOA realized in only one pulse and requires quite less computation complexity. Conventional monopulse with one transmitter and two receivers and phased array monopulse have been well developed especially in tracking radar system. MIMO-monopulse has been studied in some literature, but most of them utilize distributed MIMO. The research and application of colocated MIMO-monopulse are still in progress.
In FMCW automotive radar application, clutter may have a strong effect on DOA detection and estimation of targets with low RCS, e.g. pedestrian. With Space-Time Adaptive Processing(STAP), it is possible to suppress the clutter in the angle-Doppler domain. MIMO-monopulse basically utilizes digital beamformer to generate sum and difference channel, which also allows STAP processing to obtain adaptive sum and difference weighting. With STAP clutter can be canceled, thus false detection of MIMO-monopulse will be avoided.
In this master thesis, we propose the solution of angle estimation algorithm using MIMO-monopulse based on an actual automotive radar provided by NXP using colocated MIMO antenna. The algorithm is simulated through Matlab and verified on real experimental data. The performance of the algorithm is compared with conventional DOA algorithm and the advantages and disadvantages are analyzed. A feasible extension to STAP will be discussed to suppress the clutter for preventing false DOA estimation.
Since monopulse estimator has been approved high performance in the field of single target tracking (such as low RCS pedestrian), our proposed algorithm is also validated in single and multiple target DOA estimations, which is an attractive scenario for a potential application using MIMO-monopulse.
Automotive radar is a key element in Advanced Driver Assistance Systems (ADAS). With the growth of Automotive industry, there is a high demand for the sensors used in assisting systems. As the number of sensors increases, probability that these systems being in close proximity will also increase. This will lead to situations where in multiple radar sensors will be operating in close proximity, this might lead to a sub-optimal performance of our radar system. Currently FMCW radar systems are most prevalent in the automotive radar market. For FMCW systems, interference mitigation techniques exist in time domain, frequency domain, polarization domain and etc. Most of these techniques insiston detecting and identifying the interference before mitigating it. In this thesis, we consider a FMCW radar system and first develop a MATLAB model for the three most important interference scenarios namely Continuous Wave(CW), FrequencyModulatedContinuousWave (FMCW) and PhaseModulatedContinuousWave (PMCW). We propose a signal model where interference can be localized over the beat signal, then we systematically study how interference can be detected. As a result, we will detect the interference even if the power level of the interferer is lower than the power level of the received reflected signal. Post detection of interference,we suggest a technique to identify the interference by estimating the slope from the detected interference samples. Starting with a simple existing mitigation technique, we look at how to mitigate the interference and suggest enhancements that can be done for these techniques post detection and identification. Considering a worst case scenario of interference being completely in band to the transmitted signal, we propose a novel avoidance technique which will also predict the bandwidth of the interferer. As a result we will be able to shift the center frequency of the transmitter to avoid the interferer. Finally we propose another novel time domain mitigation technique where in without detecting or identifying the interferer, we will mitigate the interference and compare the gain of Signal to Interference plus Noise Ratio (SINR) achieved by using this technique.
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Automotive radar is a key element in Advanced Driver Assistance Systems (ADAS). With the growth of Automotive industry, there is a high demand for the sensors used in assisting systems. As the number of sensors increases, probability that these systems being in close proximity will also increase. This will lead to situations where in multiple radar sensors will be operating in close proximity, this might lead to a sub-optimal performance of our radar system. Currently FMCW radar systems are most prevalent in the automotive radar market. For FMCW systems, interference mitigation techniques exist in time domain, frequency domain, polarization domain and etc. Most of these techniques insiston detecting and identifying the interference before mitigating it. In this thesis, we consider a FMCW radar system and first develop a MATLAB model for the three most important interference scenarios namely Continuous Wave(CW), FrequencyModulatedContinuousWave (FMCW) and PhaseModulatedContinuousWave (PMCW). We propose a signal model where interference can be localized over the beat signal, then we systematically study how interference can be detected. As a result, we will detect the interference even if the power level of the interferer is lower than the power level of the received reflected signal. Post detection of interference,we suggest a technique to identify the interference by estimating the slope from the detected interference samples. Starting with a simple existing mitigation technique, we look at how to mitigate the interference and suggest enhancements that can be done for these techniques post detection and identification. Considering a worst case scenario of interference being completely in band to the transmitted signal, we propose a novel avoidance technique which will also predict the bandwidth of the interferer. As a result we will be able to shift the center frequency of the transmitter to avoid the interferer. Finally we propose another novel time domain mitigation technique where in without detecting or identifying the interferer, we will mitigate the interference and compare the gain of Signal to Interference plus Noise Ratio (SINR) achieved by using this technique.