N. Petrov
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5 records found
1
Doctoral thesis
(2025)
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A. Correas Serrano, Alexander Yarovoy , Nikita Petrov
This dissertation pushes orthogonal time‑frequency space (OTFS) from a communications-centric innovation to a radar‑ready waveform family suitable for future 6G integrated sensing and communications (ISAC). It is driven by the need to retain OTFS’s Doppler resilience while overcoming three main drawbacks: a lack of a fair comparison study with orthogonal frequency‑division multiplexing (OFDM) for radar tasks, (ii) its rigid, fully populated time‑frequency (TF) structure that hinders multiple‑access flexibility, and (iii) its high peak‑to‑average‑power ratio (PAPR), which obstructs high‑power operation, crucial for many sensing applications. The dissertation delivers a coherent toolkit that clarifies the true origin of OTFS sensing gains, provides the waveform with OFDM‑like scheduling freedom via the novel and well-researched NU‑OTFS, and offers an alternative to remove the PAPR bottleneck through CM‑OTFS. Collectively, these contributions support OTFS in becoming a practically deployable candidate for next‑generation ISAC networks that demand dense MIMO operation, fragmented spectral access, and high‑power front‑ends.
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This dissertation pushes orthogonal time‑frequency space (OTFS) from a communications-centric innovation to a radar‑ready waveform family suitable for future 6G integrated sensing and communications (ISAC). It is driven by the need to retain OTFS’s Doppler resilience while overcoming three main drawbacks: a lack of a fair comparison study with orthogonal frequency‑division multiplexing (OFDM) for radar tasks, (ii) its rigid, fully populated time‑frequency (TF) structure that hinders multiple‑access flexibility, and (iii) its high peak‑to‑average‑power ratio (PAPR), which obstructs high‑power operation, crucial for many sensing applications. The dissertation delivers a coherent toolkit that clarifies the true origin of OTFS sensing gains, provides the waveform with OFDM‑like scheduling freedom via the novel and well-researched NU‑OTFS, and offers an alternative to remove the PAPR bottleneck through CM‑OTFS. Collectively, these contributions support OTFS in becoming a practically deployable candidate for next‑generation ISAC networks that demand dense MIMO operation, fragmented spectral access, and high‑power front‑ends.
The accurate estimation of the pose, i.e. position and heading, of a vehicle while driving is of high importance in autonomous driving applications. Right now the main tool to estimate the location of a vehicle is its GPS sensor. However, GPS data is known to be of very low accuracy, especially in urban environments, and is thus not ideal for this application. In this report, the research into possible improvements to an existing vehicle pose estimation technique are presented, with the aim of making it applicable for automotive radar data. The existing technique is a scan-matching technique known as the Normal Distributions Transform (NDT), which was originally designed for LIDAR measurements. By adapting the technique to accommodate radar measurements some of the drawbacks of LiDAR, such as the high cost and poor performance in certain weather conditions, can be overcome. Some of the main disadvantages of using radar as compared to LiDAR, e.g. lower resolutions, are addressed in the presented techniques. The lower resolution results in significant spreading of the target response, this is especially prominent in the azimuth domain for a standard 3 Tx × 4 Rx MIMO automotive radar system. By addressing the scanmatching problem in the polar domain, this spreading of the target response is better captured in the distribution used to perform the scan-matching. Further, the implementation in polar coordinates allows for incorporation of the Doppler measurements, which contain knowledge about the angles of arrival of targets
and are generally measured at a much higher resolution than the angular measurements themselves. Moreover, the use of radar measurements introduces the availability of knowledge about the radar cross-section of individual targets, this can be used to reduce the influence of false alarms. The incorporation of Doppler additionally allows the exploitation of the relation between the Doppler measurements and the angle of arrival to estimate a bias in the angle measurements, this can be used for sensor calibration while driving. The influence of the presented improvements to the NDT are examined through simulations and experiments. These results show significant reduction in the estimation errors. The sensor bias estimation technique also proves to provide extremely accurate estimates. Finally, a small extension is worked out to perform trajectory estimation using the individual poses by means of the Extended Kalman Filter. ...
and are generally measured at a much higher resolution than the angular measurements themselves. Moreover, the use of radar measurements introduces the availability of knowledge about the radar cross-section of individual targets, this can be used to reduce the influence of false alarms. The incorporation of Doppler additionally allows the exploitation of the relation between the Doppler measurements and the angle of arrival to estimate a bias in the angle measurements, this can be used for sensor calibration while driving. The influence of the presented improvements to the NDT are examined through simulations and experiments. These results show significant reduction in the estimation errors. The sensor bias estimation technique also proves to provide extremely accurate estimates. Finally, a small extension is worked out to perform trajectory estimation using the individual poses by means of the Extended Kalman Filter. ...
The accurate estimation of the pose, i.e. position and heading, of a vehicle while driving is of high importance in autonomous driving applications. Right now the main tool to estimate the location of a vehicle is its GPS sensor. However, GPS data is known to be of very low accuracy, especially in urban environments, and is thus not ideal for this application. In this report, the research into possible improvements to an existing vehicle pose estimation technique are presented, with the aim of making it applicable for automotive radar data. The existing technique is a scan-matching technique known as the Normal Distributions Transform (NDT), which was originally designed for LIDAR measurements. By adapting the technique to accommodate radar measurements some of the drawbacks of LiDAR, such as the high cost and poor performance in certain weather conditions, can be overcome. Some of the main disadvantages of using radar as compared to LiDAR, e.g. lower resolutions, are addressed in the presented techniques. The lower resolution results in significant spreading of the target response, this is especially prominent in the azimuth domain for a standard 3 Tx × 4 Rx MIMO automotive radar system. By addressing the scanmatching problem in the polar domain, this spreading of the target response is better captured in the distribution used to perform the scan-matching. Further, the implementation in polar coordinates allows for incorporation of the Doppler measurements, which contain knowledge about the angles of arrival of targets
and are generally measured at a much higher resolution than the angular measurements themselves. Moreover, the use of radar measurements introduces the availability of knowledge about the radar cross-section of individual targets, this can be used to reduce the influence of false alarms. The incorporation of Doppler additionally allows the exploitation of the relation between the Doppler measurements and the angle of arrival to estimate a bias in the angle measurements, this can be used for sensor calibration while driving. The influence of the presented improvements to the NDT are examined through simulations and experiments. These results show significant reduction in the estimation errors. The sensor bias estimation technique also proves to provide extremely accurate estimates. Finally, a small extension is worked out to perform trajectory estimation using the individual poses by means of the Extended Kalman Filter.
and are generally measured at a much higher resolution than the angular measurements themselves. Moreover, the use of radar measurements introduces the availability of knowledge about the radar cross-section of individual targets, this can be used to reduce the influence of false alarms. The incorporation of Doppler additionally allows the exploitation of the relation between the Doppler measurements and the angle of arrival to estimate a bias in the angle measurements, this can be used for sensor calibration while driving. The influence of the presented improvements to the NDT are examined through simulations and experiments. These results show significant reduction in the estimation errors. The sensor bias estimation technique also proves to provide extremely accurate estimates. Finally, a small extension is worked out to perform trajectory estimation using the individual poses by means of the Extended Kalman Filter.
This project develops and tests algorithms for joint signal processing of data from two radars located on the rooftop of EWI (PARSAX and MESEWI). The particular tasks consist of automatic alignment of radar data in space (2D map) and time by observing moving targets of opportunity in the high-resolution mode. After the data alignment procedure, the detection algorithms for optimal fusing of dual-polarization and multi-frequency data are proposed. The detection results are considered the input for moving target (an auto) tracking and its signature extraction. The developed techniques were tested on the data records in experimental scenarios.
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This project develops and tests algorithms for joint signal processing of data from two radars located on the rooftop of EWI (PARSAX and MESEWI). The particular tasks consist of automatic alignment of radar data in space (2D map) and time by observing moving targets of opportunity in the high-resolution mode. After the data alignment procedure, the detection algorithms for optimal fusing of dual-polarization and multi-frequency data are proposed. The detection results are considered the input for moving target (an auto) tracking and its signature extraction. The developed techniques were tested on the data records in experimental scenarios.
There is an ever-growing demand for vital signs monitoring for a variety of occasions. Non-contact vital signs monitoring can be achieved by detecting the displacement of the human chest using Doppler radar. This method is non-invasive, environment-independent and suitable for long-term monitoring. However, the real-time detection of cardiopulmonary parameters extraction with radar needs to address the challenges of the limited time duration of the signal for the extraction of cardiopulmonary signals, accuracy of vital signs parameters estimation and signal processing algorithm complexity. Here we show that empirical and variational signal decomposition methods can be performed to extract respiration and heartbeat signals in radar system. Hilbert-Huang transform is applied in conjunction with the signal decomposition methods to display the time-frequency-energy distribution of decomposed signals, thus the instantaneous frequencies and amplitudes of vital signs can be obtained. Besides, online signal decomposition approaches are illustrated to achieve the dynamic estimation of vital signs from radar data stream. The results of our experimental verification demonstrate that Online-VMD has an accuracy of 99.56% and a variance of estimated frequencies of 1.81×10−3 when it is applied in FMCW radar system, providing a reliable, accurate and real-time parameter estimation result in vital signs monitoring.
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There is an ever-growing demand for vital signs monitoring for a variety of occasions. Non-contact vital signs monitoring can be achieved by detecting the displacement of the human chest using Doppler radar. This method is non-invasive, environment-independent and suitable for long-term monitoring. However, the real-time detection of cardiopulmonary parameters extraction with radar needs to address the challenges of the limited time duration of the signal for the extraction of cardiopulmonary signals, accuracy of vital signs parameters estimation and signal processing algorithm complexity. Here we show that empirical and variational signal decomposition methods can be performed to extract respiration and heartbeat signals in radar system. Hilbert-Huang transform is applied in conjunction with the signal decomposition methods to display the time-frequency-energy distribution of decomposed signals, thus the instantaneous frequencies and amplitudes of vital signs can be obtained. Besides, online signal decomposition approaches are illustrated to achieve the dynamic estimation of vital signs from radar data stream. The results of our experimental verification demonstrate that Online-VMD has an accuracy of 99.56% and a variance of estimated frequencies of 1.81×10−3 when it is applied in FMCW radar system, providing a reliable, accurate and real-time parameter estimation result in vital signs monitoring.
Automotive radar has an advantage over other sensors in that it is better at operating in bad weather conditions. To see the extent of the effect that adverse weather conditions might have on the statistics of the data a statistical analysis was performed on real measurement data. During heavy rain there is a shift that can be observed in the Radar Cross Section (RCS) of the target. The average RCS of the target increases slightly when it is raining. In (Multiple Input Multiple Output) MIMO radar it is important to calibrate the radar system as there can be both amplitude and phase distortions between the channels that can give unexpected results. These are usually estimated in a predetermined setting for known targets. However instead it might be feasible to estimate this from objects of opportunities that are regularly appearing in the radar field of view.
To tackle this problem a method is used that tries to estimate these calibration coefficients from measurement data. The method needs to know the angle at which the target is located, however the range of the target can remain unknown. It uses the ideal steering vector and one of the antenna elements as a reference element. The method can recreate the phase errors very well, but relies on the reference element for the amplitude estimation. Therefore the performance is based on what element is chosen as a reference. To choose the right reference element some pre-processing is done. Then the estimation of the calibration coefficients was implemented in a Simultaneous Localization And Mapping (SLAM) framework. This was solved by using an Extended Kalman Filter (EKF). The EKF is a nonlinear form of the normal Kalman filter that will be used to make an estimate for both the location of the radar, the location of the objects of opportunity and the estimation of the calibration coefficients based of these landmarks at the same time. The resulting algorithm proves that it is feasible to calibrate the radar while driving in this way. ...
To tackle this problem a method is used that tries to estimate these calibration coefficients from measurement data. The method needs to know the angle at which the target is located, however the range of the target can remain unknown. It uses the ideal steering vector and one of the antenna elements as a reference element. The method can recreate the phase errors very well, but relies on the reference element for the amplitude estimation. Therefore the performance is based on what element is chosen as a reference. To choose the right reference element some pre-processing is done. Then the estimation of the calibration coefficients was implemented in a Simultaneous Localization And Mapping (SLAM) framework. This was solved by using an Extended Kalman Filter (EKF). The EKF is a nonlinear form of the normal Kalman filter that will be used to make an estimate for both the location of the radar, the location of the objects of opportunity and the estimation of the calibration coefficients based of these landmarks at the same time. The resulting algorithm proves that it is feasible to calibrate the radar while driving in this way. ...
Automotive radar has an advantage over other sensors in that it is better at operating in bad weather conditions. To see the extent of the effect that adverse weather conditions might have on the statistics of the data a statistical analysis was performed on real measurement data. During heavy rain there is a shift that can be observed in the Radar Cross Section (RCS) of the target. The average RCS of the target increases slightly when it is raining. In (Multiple Input Multiple Output) MIMO radar it is important to calibrate the radar system as there can be both amplitude and phase distortions between the channels that can give unexpected results. These are usually estimated in a predetermined setting for known targets. However instead it might be feasible to estimate this from objects of opportunities that are regularly appearing in the radar field of view.
To tackle this problem a method is used that tries to estimate these calibration coefficients from measurement data. The method needs to know the angle at which the target is located, however the range of the target can remain unknown. It uses the ideal steering vector and one of the antenna elements as a reference element. The method can recreate the phase errors very well, but relies on the reference element for the amplitude estimation. Therefore the performance is based on what element is chosen as a reference. To choose the right reference element some pre-processing is done. Then the estimation of the calibration coefficients was implemented in a Simultaneous Localization And Mapping (SLAM) framework. This was solved by using an Extended Kalman Filter (EKF). The EKF is a nonlinear form of the normal Kalman filter that will be used to make an estimate for both the location of the radar, the location of the objects of opportunity and the estimation of the calibration coefficients based of these landmarks at the same time. The resulting algorithm proves that it is feasible to calibrate the radar while driving in this way.
To tackle this problem a method is used that tries to estimate these calibration coefficients from measurement data. The method needs to know the angle at which the target is located, however the range of the target can remain unknown. It uses the ideal steering vector and one of the antenna elements as a reference element. The method can recreate the phase errors very well, but relies on the reference element for the amplitude estimation. Therefore the performance is based on what element is chosen as a reference. To choose the right reference element some pre-processing is done. Then the estimation of the calibration coefficients was implemented in a Simultaneous Localization And Mapping (SLAM) framework. This was solved by using an Extended Kalman Filter (EKF). The EKF is a nonlinear form of the normal Kalman filter that will be used to make an estimate for both the location of the radar, the location of the objects of opportunity and the estimation of the calibration coefficients based of these landmarks at the same time. The resulting algorithm proves that it is feasible to calibrate the radar while driving in this way.