On Non-Cooperative Multiple-Target Tracking with Wireless Sensor Networks

In this paper, we propose an approach to track multiple non-cooperative targets with wireless sensor networks. Most existing tracking algorithms can not be directly applied to non-cooperative target tracking because they assume the access to signals from individual targets for tracking by assuming that: 1) there is only one target in a field; 2) signals from different co-operative targets can be differentiated; or 3) interference caused by signals from other targets is negligible because of attenuation. We propose a general approach for tracking non-cooperative targets. The tracking algorithm first separates the aggregate signals from multiple indistinguishable targets via the blind source separation (BSS) algorithms. Through the analysis on both the temporal and spatial correlation of the separated individual signals, the tracking algorithm determines the location of a target and its moving track. A voting scheme based on the spatial information is designed to better estimate the moving track. Furthermore, we analyze and discuss the influence of signal attenuation and the tracking resolution of the proposed tracking approach. Our experiments show that the proposed approach can both accurately and precisely track multiple indistinguishable moving targets

Cooperative Localization on Computationally Constrained Devices

Cooperative localization is a useful way for nodes within a network to share location information in order to better arrive at a position estimate. This is handy in GPS contested environments (indoors and urban settings). Most systems exploring cooperative localization rely on special hardware, or extra devices to store the database or do the computations. Research also deals with specific localization techniques such as using Wi-Fi, ultra-wideband signals, or accelerometers independently opposed to fusing multiple sources together. This research brings cooperative localization to the smartphone platform, to take advantage of the multiple sensors that are available. The system is run on Android powered devices, including the wireless hotspot. In order to determine the merit of each sensor, analysis was completed to determine successes and failures. The accelerometer, compass, and received signal strength capability were examined to determine their usefulness in cooperative localization. Experiments at meter intervals show the system detected changes in location at each interval with an average standard deviation of 0.44m. The closest location estimates occurred at 3m, 4m and 6m with average errors of 0.15m, 0.11m, and 0.07m respectively. This indicates that very precise estimates can be achieved with an Android hotspot and mobile nodes

Doctor of Philosophy

dissertationIn wireless sensor networks, knowing the location of the wireless sensors is critical in many remote sensing and location-based applications, from asset tracking, and structural monitoring to geographical routing. For a majority of these applications, received signal strength (RSS)-based localization algorithms are a cost effective and viable solution. However, RSS measurements vary unpredictably because of fading, the shadowing caused by presence of walls and obstacles in the path, and non-isotropic antenna gain patterns, which affect the performance of the RSS-based localization algorithms. This dissertation aims to provide efficient models for the measured RSS and use the lessons learned from these models to develop and evaluate efficient localization algorithms. The first contribution of this dissertation is to model the correlation in shadowing across link pairs. We propose a non-site specific statistical joint path loss model between a set of static nodes. Radio links that are geographically proximate often experience similar environmental shadowing effects and thus have correlated shadowing. Using a large number of multi-hop network measurements in an ensemble of indoor and outdoor environments, we show statistically significant correlations among shadowing experienced on different links in the network. Finally, we analyze multihop paths in three and four node networks using both correlated and independent shadowing models and show that independent shadowing models can underestimate the probability of route failure by a factor of two or greater. Second, we study a special class of algorithms, called kernel-based localization algorithms, that use kernel methods as a tool for learning correlation between the RSS measurements. Kernel methods simplify RSS-based localization algorithms by providing a means to learn the complicated relationship between RSS measurements and position. We present a common mathematical framework for kernel-based localization algorithms to study and compare the performance of four different kernel-based localization algorithms from the literature. We show via simulations and an extensive measurement data set that kernel-based localization algorithms can perform better than model-based algorithms. Results show that kernel methods can achieve an RMSE up to 55% lower than a model-based algorithm. Finally, we propose a novel distance estimator for estimating the distance between two nodes a and b using indirect link measurements, which are the measurements made between a and k, for k ? b and b and k, for k ? a. Traditionally, distance estimators use only direct link measurement, which is the pairwise measurement between the nodes a and b. The results show that the estimator that uses indirect link measurements enables better distance estimation than the estimator that uses direct link measurements