A Bayesian Network for the Classification of Human Motion as Observed by Distributed Radar

In this article, a statistical model of human motion as observed by a network of radar sensors is presented where knowledge on the position and heading of the target provides information on the observation conditions of each sensor node. Sequences of motions are estimated from measurements of instantaneous Doppler frequency, which captures...

Domain adaptation for target classification using micro-Doppler spectra in radar networks

In this paper, the classification of human activity from micro-Doppler spectrograms measured by a radar network is considered. To cope with differences between the training and test datasets due to changes in the set of participants, signal-to-noise ratio and polarimetry, domain adaptation is proposed. To realize this, linear mapping between the...

Distributed Radar Information Fusion for Gait Recognition and Fall Detection

This paper discusses a fusion framework with data from multiple, distributed radar sensors based on conventional classifiers, and transfer learning with pre-trained deep networks. The application considered is the classification of gait styles and the detection of critical accidents such as falls. The data were collected from a network comprised...

Cross-frequency training with adversarial learning for radar micro-Doppler signature classification

Deep neural networks have become increasingly popular in radar micro-Doppler classification; yet, a key challenge, which has limited potential gains, is the lack of large amounts of measured data that can facilitate the design of deeper networks with greater robustness and performance. Several approaches have been proposed in the literature...

Radar network topology optimization for joint target position and velocity estimation

In this paper, we tackle the problem of selecting the radar node positions to provide an estimate of the target state vector with a prescribed accuracy. The topology optimization problem is formulated as selection of a fixed number of radar node positions from a set of available ones, where the radar observations are modeled by a general non...

Radar networks performance analysis and topology optimization

The ultimate goal of any sensing system is to build situation awareness. Existing<br/>solutions for a single radar node that have to assure extended areas of coverage with<br/>high resolution measurements (in range, cross-range, and Doppler) are physically<br/>cumbersome (large antenna size) and typically require large operational resources ...

2D Modeling and Classification of Extended Objects in a Network of HRR Radars

In this thesis, the modeling of extended objects with low-dimensional representations of their 2D geometry is addressed. The ultimate objective is the classification of the objects using libraries of such compact 2D object models that are much smaller than in the state-of-the-art classification schemes based on (High Range Resolution) HRR data....

De-ghosting of tomographic images in a radar network with sparse angular sampling

Taking into account sparsity of the reflectivity function of several radar targets of interest, efficient low-complexity automatic target recognition (ATR) systems can be designed. Such ATR systems would be based on two-dimensional (2D) spatial target models of low dimensionality, where critical information on the radar target signature is...