A Centralized Processing Framework for Foliage Penetration Human Tracking in Multistatic Radar

A complete centralized processing framework is proposed for human tracking using multistatic radar in the foliage-penetration environment. The configuration of the multistatic radar system is described. Primary attention is devoted to time of arrival (TOA) estimation and target localization. An improved approach that takes the geometrical center as the TOA estimation of the human target is given. The minimum mean square error paring (MMSEP) approach is introduced for multi-target localization in the multistatic radar system. An improved MMSEP algorithm is proposed using the maximum velocity limitation and the global nearest neighbor criterion, efficiently decreasing the computational cost of MMSEP. The experimental results verify the effectiveness of the centralized processing framework

Localization Capability of Cooperative Anti-Intruder Radar Systems

System aspects of an anti-intruder multistatic radar based on impulse radio ultrawideband (UWB) technology are addressed. The investigated system is composed of one transmitting node and at least three receiving nodes, positioned in the surveillance area with the aim of detecting and locating a human intruder (target) that moves inside the area. Such systems, referred to also as UWB radar sensor networks, must satisfy severe power constraints worldwide imposed by, for example, the Federal Communications Commission (FCC) and by the European Commission (EC) power spectral density masks. A single transmitter-receiver pair (bistatic radar) is considered at first. Given the available transmitted power and the capability of the receiving node to resolve the UWB pulses in the time domain, the surveillance area regions where the target is detectable, and those where it is not, are obtained. Moreover, the range estimation error for the transmitter-receiver pair is discussed. By employing this analysis, a multistatic system is then considered, composed of one transmitter and three or four cooperating receivers. For this multistatic system, the impact of the nodes location on area coverage, necessary transmitted power and localization uncertainty is studied, assuming a circular surveillance area. It is highlighted how area coverage and transmitted power, on one side, and localization uncertainty, on the other side, require opposite criteria of nodes placement. Consequently, the need for a system compromising between these factors is shown. Finally, a simple and effective criterion for placing the transmitter and the receivers is drawn

Ultra Wide Band localization and tracking hybrid technique using VRTs

This research presents hybrid radar tracking technique consisting of Time Of Arrival (TOA) and Received Signal Strength (RSS) techniques. This hybrid design increases efficiency, accuracy and sensitivity of radar system. The radar used in this research is multistatic radar with one transmitter and three receivers. One common drawback in RSS and TOA techniques is high level synchronization in transmitter and receivers. The hybrid design also suffers from transmitter-receiver synchronization. To overcome TX-RX synchronization problem Virtual Reference Tags (VRTs) are used. These tags are virtually mapped over the surveillance area giving radar design different reference points from which it can accurately locate intruder and monitor its movements. Also four cases of differen

Experimental path loss models for UWB multistatic radar systems

The use of Ultra-Wideband (UWB) radio technology in a multistatic radar system has recently gained interest to implement Wireless Sensor Networks (WSN) capable of detecting and tracking targets in indoor environments. Due to the increasing attention towards multistatic UWB systems, it is important to perform the radio channel characterization. In this thesis we focus on the characterization of the path loss exponent (α). To perform the present work, the followed methodology was to collect experimental data from the UWB devices using a suitable target, this information was processed with a clutter removal algorithm using the Empty Room (ER) approach, then the contribution of the target was isolated to produce a graph of energy as a function of the product between the target-to-transmitter and the target-to-receiver distances in a bistatic configuration. Finally, using this plot it was properly obtained the value of the path loss exponent. As as additional experimental result, the main statistical parameters associated to the residual clutter were calculated, which are expected to allow having a better understanding and characterization of the radar system performance in the experimental environments

Toward Deep Learning-Based Human Target Analysis

In this chapter, we describe methods toward deep learning-based human target analysis. Firstly, human target analysis in 2D and 3D domains of radar signal is introduced. Furthermore, range-Doppler surface for human target analysis using ultra-wideband radar is described. The construction of range-Doppler surface involves range-Doppler imaging, adaptive threshold detection, and isosurface extraction. In comparison with micro-Doppler profiles and high-resolution range profiles, range-Doppler surface contains range, Doppler, and time information simultaneously. An ellipsoid-based human motion model is designed for validation. Range-Doppler surfaces simulated for different human activities are demonstrated and discussed. With the rapid emergence of deep learning, the development of radar target recognition has been accelerated. We describe several deep learning algorithms for human target analysis. Finally, a few future research considerations are listed to spark inspiration

Target Tracking in UWB Multistatic Radars

Detection, localization and tracking of non-collaborative objects moving inside an area is of great interest to many surveillance applications. An ultra- wideband (UWB) multistatic radar is considered as a good infrastructure for such anti-intruder systems, due to the high range resolution provided by the UWB impulse-radio and the spatial diversity achieved with a multistatic configuration. Detection of targets, which are typically human beings, is a challenging task due to reflections from unwanted objects in the area, shadowing, antenna cross-talks, low transmit power, and the blind zones arised from intrinsic peculiarities of UWB multistatic radars. Hence, we propose more effective detection, localization, as well as clutter removal techniques for these systems. However, the majority of the thesis effort is devoted to the tracking phase, which is an essential part for improving the localization accuracy, predicting the target position and filling out the missed detections. Since UWB radars are not linear Gaussian systems, the widely used tracking filters, such as the Kalman filter, are not expected to provide a satisfactory performance. Thus, we propose the Bayesian filter as an appropriate candidate for UWB radars. In particular, we develop tracking algorithms based on particle filtering, which is the most common approximation of Bayesian filtering, for both single and multiple target scenarios. Also, we propose some effective detection and tracking algorithms based on image processing tools. We evaluate the performance of our proposed approaches by numerical simulations. Moreover, we provide experimental results by channel measurements for tracking a person walking in an indoor area, with the presence of a significant clutter. We discuss the existing practical issues and address them by proposing more robust algorithms