Feature diversity for optimized human micro-doppler classification using multistatic radar

This paper investigates the selection of different combinations of features at different multistatic radar nodes, depending on scenario parameters, such as aspect angle to the target and signal-to-noise ratio, and radar parameters, such as dwell time, polarisation, and frequency band. Two sets of experimental data collected with the multistatic radar system NetRAD are analysed for two separate problems, namely the classification of unarmed vs potentially armed multiple personnel, and the personnel recognition of individuals based on walking gait. The results show that the overall classification accuracy can be significantly improved by taking into account feature diversity at each radar node depending on the environmental parameters and target behaviour, in comparison with the conventional approach of selecting the same features for all nodes

Radar detection and identification of human signatures using moving platforms

Radar offers unique advantages over other sensors for the detection of humans, such as remote operation during virtually all weather and lighting conditions, increased range, and better coverage. Many current radar-based human detection systems employ some type of Fourier analysis, such as Doppler processing. However, in many environments, the signal-to-noise ratio (SNR) of human returns is quite low. Furthermore, Fourier-based techniques assume a linear variation in target phase over the aperture, whereas human targets have a highly nonlinear phase history. The resulting phase mismatch causes significant SNR loss in the detector itself. In this work, human target modeling is used to derive a more accurate non-linear approximation to the true target phase history. Two algorithms are proposed: a parameter estimation-based optimized non-linear phase (ONLP) detector, and a dictionary search-based enhanced optimized non-linear phase (EnONLP) detector. The ONLP algorithm optimizes the likelihood ratio over the unknown model parameters to derive a more accurate approximation to the expected human return. The EnONLP algorithm stores expected target signatures generated for each possible combination of model parameters in a dictionary, and then applies Orthogonal Matching Pursuit (OMP) to determine the optimal linear combination of dictionary entries that comprises the measured radar data. Thus, unlike the ONLP, the EnONLP algorithm also has the capability of detecting the presence of multiple human targets. Cramer-Rao bounds (CRB) on parameter estimates and receiver operating characteristics (ROC) curves are used to validate analytically the performance of both proposed methods to that of conventional, fully adaptive STAP. Finally, application of EnONLP to target characterization is illustrated.Ph.D.Committee Chair: Williams, Douglas B.; Committee Co-Chair: Melvin, William L.; Committee Member: Howard, Ayanna; Committee Member: Lanterman, Aaron D.; Committee Member: McClellan, James H.; Committee Member: Vidakovic, Bran

Application and Modeling of a Magnetic WSN for Target Localization

The aim of this study is modeling ferromagnetic targets for localization and identification of such objects by a wireless sensor network (WSN). MICAz motes were used for setting up a wireless sensor network utilizing a centralized tree-based system. The detection and tracking of ferromagnetic objects is an important application of WSNs. This research focuses on analyzing the sensing limitations of magnetic sensors via tests conducted on small-scale targets which are moving within a 30 cm radius around the sensors. To detect target pres-ence and determine direction of motion, changes in magnetic field intensity are measured by the magnetic sensors. Target detection, identification and sequential localization (DISL) were accomplished using a minimum distance algorithm. The effect of environmental variations, such as temperature and power supply variations and magneticnoise, on DISL performance is examined based on experimental tests

Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities

Radar-based activity recognition is a problem that has been of great interest due to applications such as border control and security, pedestrian identification for automotive safety, and remote health monitoring. This paper seeks to show the efficacy of micro-Doppler analysis to distinguish even those gaits whose micro-Doppler signatures are not visually distinguishable. Moreover, a three-layer, deep convolutional autoencoder (CAE) is proposed, which utilizes unsupervised pretraining to initialize the weights in the subsequent convolutional layers. This architecture is shown to be more effective than other deep learning architectures, such as convolutional neural networks and autoencoders, as well as conventional classifiers employing predefined features, such as support vector machines (SVM), random forest, and extreme gradient boosting. Results show the performance of the proposed deep CAE yields a correct classification rate of 94.2% for micro-Doppler signatures of 12 different human activities measured indoors using a 4 GHz continuous wave radar-17.3% improvement over SVM

Kinematic Model-Based Human Detectors for Multi-Channel Radar

Humans are difficult targets to detect because they have small radar cross sections (RCS) and move at low velocities. Consequently, they are masked by Doppler spread ground clutter generated by the radar bearing platform motion. Furthermore, conventional radar-based human detection systems employ some type of linear-phase matched filtering, whereas most human targets generate a highly nonlinear phase history. This work proposes an enhanced, optimized, nonlinear phase (EnONLP) matched filter that exploits knowledge of human gait to improve the radar detection performance of human targets. A parametric model of the expected human response is derived for multi-channel radar systems and used to generate a dictionary of human returns for a range of possible parameter variations. The best linear combination of projections in this dictionary is computed via orthogonal matching pursuit (OMP) to detect and extract features for multiple targets. Performance of the proposed EnONLP method is compared with that of traditional space-time adaptive processing (STAP) and a previously derived parameter estimation-based ONLP detector. Results show that EnONLP exhibits a detection probability of about 0.8 for a clutter-to-noise (CNR) ratio of 20 dB and input signal-to-noise ratio (SNR) of 0 dB, while ONLP yields a 0.3 and STAP yields a 0.18 probability of detection for the same false alarm rate