Estimating Single and Multiple Target Locations Using K-Means Clustering with Radio Tomographic Imaging in Wireless Sensor Networks

Geolocation involves using data from a sensor network to assess and estimate the location of a moving or stationary target. Received Signal Strength (RSS), Angle of Arrival (AoA), and/or Time Difference of Arrival (TDoA) measurements can be used to estimate target location in sensor networks. Radio Tomographic Imaging (RTI) is an emerging Device-Free Localization (DFL) concept that utilizes the RSS values of a Wireless Sensor Network (WSN) to geolocate stationary or moving target(s). The WSN is set up around the Area of Interest (AoI) and the target of interest, which can be a person or object. The target inside the AoI creates a shadowing loss between each link being obstructed by the target. This research focuses on position estimation of single and multiple targets inside a RTI network. This research applies K-means clustering to localize one or more targets. K-means clustering is an algorithm that has been used in data mining applications such as machine learning applications, pattern recognition, hyper-spectral imagery, artificial intelligence, crowd analysis, and Multiple Target Tracking (MTT)

Automatic target detection and speed estimationusing forward scatter radar sensor

Forward Scatter Radar (FSR) is a subclass of the bistatic radar, where the received target signal occurs mainly due to the direct path signal shadowing by the target body. Employing a separate deployed transmitter and receiver at considerable distance, the FSR can achieve a number of advantages, such as enhanced radar cross section, inherent detection ability of stealth target, reasonably low complexity design of system, more than the conventional monostatic radar. All of these features are attractive to the modern remote sensing systems. This thesis presents the research results of the detection and speed estimation of the ground target in FSR, which is a vital procedure for automatic targets classification. The hardware was designed and assembled by the Microwave Integrated Systems Laboratory (MISL), University of Birmingham. The experimental data used in this thesis have been collected from real field environments at multiple locations and from various targets. The complex automatic target detection and speed estimation algorithm were integrated to achieve higher accuracy. The main problem investigated in this research and the appropriate results are dedicated to automatic target speed estimation in complex FSR operational scenario. The improved and originally proposed algorithms are discussed and shown throughout the chapters in great detail. The measurements are implemented in large load of work and the database is created for the validation of these algorithms

Multistatic radar optimization for radar sensor network applications

The design of radar sensor networks (RSN) has undergone great advancements in recent years. In fact, this kind of system is characterized by a high degree of design flexibility due to the multiplicity of radar nodes and data fusion approaches. This thesis focuses on the development and analysis of RSN architectures to optimize target detection and positioning performances. A special focus is placed upon distributed (statistical) multiple-input multipleoutput (MIMO) RSN systems, where spatial diversity could be leveraged to enhance radar target detection capabilities. In the first part of this thesis, the spatial diversity is leveraged in conjunction with cognitive waveform selection and design techniques to quickly adapt to target scene variations in real time. In the second part, we investigate the impact of RSN geometry, particularly the placement of multistatic radar receivers, on target positioning accuracy. We develop a framework based on cognitive waveform selection in conjunction with adaptive receiver placement strategy to cope with time-varying target scattering characteristics and clutter distribution parameters in the dynamic radar scene. The proposed approach yields better target detection performance and positioning accuracy as compared with conventional methods based on static transmission or stationary multistatic radar topology. The third part of this thesis examines joint radar and communication systems coexistence and operation via two possible architectures. In the first one, several communication nodes in a network operate separately in frequency. Each node leverages the multi-look diversity of the distributed system by activating radar processing on multiple received bistatic streams at each node level in addition to the pre-existing monostatic processing. This architecture is based on the fact that the communication signal, such as the Orthogonal Frequency Division Multiplexing (OFDM) waveform, could be well-suited for radar tasks if the proper waveform parameters are chosen so as to simultaneously perform communication and radar tasks. The advantage of using a joint waveform for both applications is a permanent availability of radar and communication functions via a better use of the occupied spectrum inside the same joint hardware platform. We then examine the second main architecture, which is more complex and deals with separate radar and communication entities with a partial or total spectrum sharing constraint. We investigate the optimum placement of radar receivers for better target positioning accuracy while reducing the radar measurement errors by minimizing the interference caused by simultaneous operation of the communication system. Better performance in terms of communication interference handling and suppression at the radar level, were obtained with the proposed placement approach of radar receivers compared to the geometric dilution of precision (GDOP)-only minimization metric

Target tracking for UWB multistatic radar sensor networks

A multistatic radar based on ultra-wideband (UWB), also known as a UWB radar sensor network, has been shown to represent a very promising solution to localize an intruder moving within a small surveillance area. In this paper, a tracking algorithm based on low-complexity particle filtering is proposed, specifically tailored to UWB radar sensor networks with one transmitter and several receivers. An expression to calculate the particle weights is first derived, combining observations from all receivers. The particle filter is then modified to solve problems caused by blind zones inherently associated with the use of the UWB technology and multistatic configuration. In the proposed improved algorithm, suitable low-complexity particle filtering is employed to estimate velocity. The proposed approach provides high accuracy even at low signal-to-noise ratios with either static or dynamic clutters and it can track complicated maneuvering trajectories