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The paper presents human motion measurements with the experimental Frequency Modulated Continuous Wave(FMCW) radar at TNO-FEL. The aim of these measurements is to analyse the Doppler velocity spectrum of humans. These analysis give insight in measuring human behaviour with radar for security applications. The measurements contain an outbound walking human in a tunnel. The human body Doppler velocity and the Doppler velocity caused by the upper legs, and feet are visible in the spectogram. The measurments also give sufficient information to estimate the human stride length.

Human motion estimation is an important issue in automotive, security or home automation applications. Radar systems are well suited for this because they are robust, are independent of day or night conditions and have accurate range and speed domain. The human response in a radar range-speed-time measurement behaves like an extended target where legs and arms coincide. A mutually non-coherent radar sensor network makes it possible to estimate additional information of the extended target response. To keep the system low cost the network uses commercial off-the-shelf (COTS) radar sensors without synchronisation of frequency or phase between the radar sensors. This article presents the results of human motion estimation with a mutually non-coherent radar sensor network. The calibration, radar processing, parameter estimation and classification of extended human objects are described. The swinging and rotating moving body parts give elliptical shapes in the differential range-speed responses. A model fit gives the legs and arms parameters on which classification is possible.

Radar observes targets, but they remain difficult to interpret due to the difficulty in analysing the radar range-speed sequences. The need for accurate analyses tools increases in case of extended target behaviour or multiple channel radars which give additional observation angles. Extended targets are targets with multiple scatterer responses which disturb each other and give a blurred target response. We investigate here the approach of deconvoluting the range-speed response with a point spread function and interpolate the range-speed positions to get the inner structure of the extended target. The deconvolution gives the individual elements of the extended target. The range-speed interpolation gives accurate position information. The positions and additional observation angle information are tracked with a filter. We demonstrate the approach with real radar measurements.

We discuss range and Doppler processing for FMCW radar using only a single pulse or frequency sweep. The first step is correlation processing, for which the range and Doppler resolution are limited by the ambiguity function. We show that this resolution can be optimized with an additional inverse filtering step. The method is demonstrated for sinusoidal FMCW radar measurements. Several regularized inverse filters were compared and the non-adaptive pseudo inverse filter gave the best results.

Radar can be used to observe humans that are obscured by objects such as walls. These humans cannot be visually observed. The radar measurements are used to animate an obscured human in virtual reality. This requires detailed information about the motion. The radar measurements give detailed information about the movements of the human body parts; the Doppler signatures are time-varying and observed in the spectrogram. The authors focus on the extraction of parameters and describe a method for estimating human walking parameters from radar measurements. The parameters are estimated by minimising the difference between a simulated model and real measurements. A human walking model is presented which can be used both to calculate the radar response and to visually animate a walking person. The method is applied to real radar measurements of inbound walking humans from a distance of 20 m. The results show that estimation of the walking parameters is possible. The animated walking human generated with estimated parameters is a realistic likeness of the real walking human.

Radars can be used to observe persons. Animation of an observed human on the basis of Frequency Modulated Continuous Wave (FMCW) radar measurements in virtual reality considerably facilitates the interpretation of the radar measurements. These radar measurements give detailed information of the motion of the human body parts. The radar reflections are time-varying in range and Doppler. This information is used to animate a human in virtual reality. The delay between the observation and animation must be short for real-time applications. The paper describes a fast method for estimating human walking parameters from radar measurements and a simulated model. The parameters are estimated by minimizing the difference between a database with simulated model data and the real measurements. An approximation technique is used to estimate the essential walking parameters of the walking motion. The error with the exact solution is less than 10%. The computation time is less than the measurement time. This opens possibilities for real-time applications

Radar can be an extremely useful sensing technique to observe persons. It perceives persons behind walls or at great distances and in situations where persons have no or poor visibility. Human motion modulates the radar signal which can be observed in the spectrogram of the received signal. Extraction of these movements enables the animation of a person in virtual reality. The authors focus on a fast feature-based approach to estimate human motion features for real-time applications. The human walking model of Boulic is used, which describe the human motion with three parameters. Personification information is obtained by estimating the individual leg and torso parameters. These motion parameters can be estimated from the temporal maximum, minimum and centre velocity of the human motion distribution. Three methods are presented to extract these velocities. Additionally, we extract an independent human motion repetition frequency estimate based on velocity slices in the spectrogram. Kalman filters smooth the parameters and estimate the global Boulic parameters. These estimated parameters are input to the human model of Boulic which forms the basis for animation. The methods are applied to real radar measurements. The animated person generated with the extracted parameters provides a realistic look-alike of the real motion of the person.

High resolution radar imaging techniques can be used in ballistic missile defence systems to determine the type of ballistic missile during the boost phase (threat typing) and to discriminate different parts of a ballistic missile after the boost phase. The applied radar imaging technique is 2D Inverse Synthetic Aperture Radar (2D-ISAR) in which the Doppler shifts of various parts of the ballistic missile are employed to obtain a high cross-range resolution while the resolution in downrange is achieved with a large radar bandwidth. For a 10 cm downrange resolution, a radar bandwidth of more than 1.5 GHz is required. However, this requirement is not compatible with EM frequency spectrum allocations for long range ballistic missile defence radars that operate in the L, S, and C frequency band. In this paper, a novel coherent multiband ISAR imaging technique is proposed that employs two or more narrowband radar systems that operate in different frequency bands. The coherent multiband imaging process uses an advanced interpolation technique to achieve a very high downrange resolution and produces little artifacts due to noise.

This paper presents methods for Coherent Multistatic Radar Imaging for Non Cooperative Target Recognition (NCTR) with a network of radar sensors. Coherent Multistatic Radar Imaging is based on an extension of existing monostatic ISAR algorithms to the multistatic environment. The paper describes the ISAR processing and fusion by incoherent summation, and coherent summation, with and without overlapping observation angles. We demonstrate the proposed methods on simulated measurements.

A light weight SAR, suitable for use on short range tactical UAV, has been designed and built. The system consists of a fully digital receive array, and a very compact active transmit antenna. The approximate weight of the complete system is 6 kg, with power consumption below 75 W, depending on the required data acquisition volume. This X-band system is designed to provide an image resolution down to 10-15 cm at a maximum range of approximately 5 km. The design is described, and initial ground test results are presented.

False plots and plots with inaccurate range and Doppler estimates may severely degrade the performance of tracking algorithms in radar systems. This paper describes how a multiple hypothesis clustering technique can be applied to mitigate the problems involved in plot extraction. The measures of confidence for each hypothesis are based on fuzzy sets and possibility distributions of plot attributes such as the radial velocity and the radar cross section. Some results of radar experiments are shown.

Area surveillance for guarding and intruder detection with a combined camera radar sensor is considered. This specific sensor combination is attractive since complementary information is provided by the respective elements. Thus, a more complete description of objects of interest can be obtained. Several strategies to fuse the data are discussed. Results obtained with 'live' experiments are presented. When compared to camera only, a significant reduction of the number of false tracks is achieved.