Signal processing in radar and non-radar sensor networks

This dissertation studies six topics within the area of radar and non-radar sensor networks from a signal processing perspective: radar sensor networks (RSN) wave-form design and performance analysis (chapter 2), blind speed alleviation using RSN (chapter 3), target detection in foliage using Ultra-Wideband (UWB) RSN (chapter 4), sense-through-foliage&wall channel modeling (chapter 5), channel selection algorithms in virtual multiple-input-multiple-output (MIMO) sensor networks (chapter 6) and RF emitter passive geolocation using unmanned aerial vehicles (UAVs) and sensors (chapter 7).;In RSN, distributed radar sensors work in an ad hoc fashion but are grouped together by an intelligent clusterhead that combines waveform diversity. RSN not only provide spatial resilience for target detection and tracking compared to traditional radars, but also alleviate inherent radar defects such as the blind speed problem. This interdisciplinary area offers a new paradigm for signal processing research. In this dissertation, orthogonal constant frequency (CF) pulse waveforms are designed for both coherent and noncoherent RSN detection systems. To what extend RSN outperform single radar and how Doppler shift degrades the performance are analyzed in terms of probability of detection and probability of false alarm.;As blind speed problem can turn out to be a catastrophe to moving target detection, RSN design with equal gain combination (EGC) algorithm is proposed to tremendously alleviate this problem. A fuzzy logic system (FLS) is also designed to optimize the number of radars in RSN, making the FLS-based RSN achieve somehow constant probability of miss detection even with different system configuration.;In foliage, UWB RSN are employed for target detection. On a basis of pragmatic measurements, a RSN Rake structure and two signal processing schemes are proposed to improve the target detection performance. One is differential-based approach that accounts for the channel effect and analyzes the "defoliated" signal. Another applies short-time Fourier transform (STFT) that uses a slide window to determine the sinusoidal frequency and phase content. Both schemes are able to detect the target successfully.;Based on these real radar data, new sense-through-foliage channel model is proposed and parameters are statistically analyzed. The amplitude can be characterized by log-logistic distribution while the time arrival of multi-path contributions can be modeled as a Poisson process. Another statistical model for sense-through-wall channel is also proposed based on experimental measurement using UWB noise radar. These results provide an improved understanding of wireless propagation in foliage and wall.;In non-radar virtual MIMO wireless sensor networks (WSN), two practical algorithms to select a subset of channels are presented to balance the MIMO advantage and the energy consumption of sensor cooperation. If intra-cluster node-to-node multi-hop needs be taken into account, Maximum Spanning Tree Searching (MASTS) algorithm in respect of cross-layer design always provides a path connecting all sensors. When WSN is organized in a manner of cluster-to-cluster multi-hop, Singular-Value Decomposition-QR with Threshold (SVD-QR-T) approach selects the best subset of transmitters while keeping all receivers active. Simulations show that both algorithms provide satisfying performances with reduced resource consumption.;Finally, a network of UAVs is designed for passive location of RF emitters. Each UAV is equipped with multiple electronic surveillance (ES) sensors to provide local mean distance estimation based on received signal strength indicator (RSSI). Fusion center will determine the location of the target through UAV triangulation. Different with previous existing studies, this method is on a basis of an empirical path loss and log-normal shadowing model, from a wireless communication and signal processing vision to offer an effective solution.