On Topology of Sensor Networks Deployed for Multi-Target Tracking

In this paper, we study topologies of sensor networks deployed for tracking multiple targets. Tracking multiple moving targets is a challenging problem. Most of the previously proposed tracking algorithms simplify the problem by assuming access to the signal from an individual target for tracking. Recently, tracking algorithms based on blind source separation (BSS), a statistical signal-processing technique widely used to recover individual signals from mixtures of signals, have been proposed. BSS-based tracking algorithms are proven to be effective in tracking multiple indistinguishable targets. The topology of a wireless sensor network deployed for tracking with BSS-based algorithms is critical to tracking performance because the topology affects separation performance, and the topology determines accuracy and precision of estimation on the paths taken by targets. We propose cluster topologies for BSS-based tracking algorithms. Guidelines on parameter selection for proposed topologies are given in this paper. We evaluate the proposed cluster topologies with extensive experiments. Our experiments show that the proposed topologies can significantly improve both the accuracy and the precision of BSS-based tracking algorithms

Tracking in Wireless Sensor Network Using Blind Source Separation Algorithms

This thesis describes an approach to track multiple targets using wireless sensor networks. In most of previously proposed approaches, tracking algorithms have access to the signal from individual target for tracking by assuming (a) there is only one target in a field, (b) signals from different targets can be differentiated, or (c) interference caused by signals from other targets is negligible because of attenuation. We propose a general tracking approach based on blind source separation, a statistical signal processing technique widely used to recover individual signals from mixtures of signals. By applying blind source separation algorithms to mixture signals collected from sensors, signals from individual targets can be recovered. By correlating individual signals recovered from different sensors, the proposed approach can estimate paths taken by multiple targets. Our approach fully utilizes both temporal information and spatial information available for tracking. We evaluate the proposed approach through extensive experiments. Experiment results show that the proposed approach can track multiple objects both accurately and precisely. We also propose cluster topologies to improve tracking performance in low-density sensor networks. Parameter selection guidelines for the proposed topologies are given in this Thesis. We evaluate proposed cluster topologies with extensive experiments. Our empirical experiments also show that BSS-based tracking algorithm can achieve comparable tracking performance in comparison with algorithms assuming access to individual signal

Tracking in Wireless Sensor Network Using Blind Source Separation Algorithms

This thesis describes an approach to track multiple targets using wireless sensor networks. In most of previously proposed approaches, tracking algorithms have access to the signal from individual target for tracking by assuming (a) there is only one target in a field, (b) signals from different targets can be differentiated, or (c) interference caused by signals from other targets is negligible because of attenuation. We propose a general tracking approach based on blind source separation, a statistical signal processing technique widely used to recover individual signals from mixtures of signals. By applying blind source separation algorithms to mixture signals collected from sensors, signals from individual targets can be recovered. By correlating individual signals recovered from different sensors, the proposed approach can estimate paths taken by multiple targets. Our approach fully utilizes both temporal information and spatial information available for tracking. We evaluate the proposed approach through extensive experiments. Experiment results show that the proposed approach can track multiple objects both accurately and precisely. We also propose cluster topologies to improve tracking performance in low-density sensor networks. Parameter selection guidelines for the proposed topologies are given in this Thesis. We evaluate proposed cluster topologies with extensive experiments. Our empirical experiments also show that BSS-based tracking algorithm can achieve comparable tracking performance in comparison with algorithms assuming access to individual signal

Localisation of mobile nodes in wireless networks with correlated in time measurement noise.

Wireless sensor networks are an inherent part of decision making, object tracking and location awareness systems. This work is focused on simultaneous localisation of mobile nodes based on received signal strength indicators (RSSIs) with correlated in time measurement noises. Two approaches to deal with the correlated measurement noises are proposed in the framework of auxiliary particle filtering: with a noise augmented state vector and the second approach implements noise decorrelation. The performance of the two proposed multi model auxiliary particle filters (MM AUX-PFs) is validated over simulated and real RSSIs and high localisation accuracy is demonstrated

Consistent Sensor, Relay, and Link Selection in Wireless Sensor Networks

In wireless sensor networks, where energy is scarce, it is inefficient to have all nodes active because they consume a non-negligible amount of battery. In this paper we consider the problem of jointly selecting sensors, relays and links in a wireless sensor network where the active sensors need to communicate their measurements to one or multiple access points. Information messages are routed stochastically in order to capture the inherent reliability of the broadcast links via multiple hops, where the nodes may be acting as sensors or as relays. We aim at finding optimal sparse solutions where both, the consistency between the selected subset of sensors, relays and links, and the graph connectivity in the selected subnetwork are guaranteed. Furthermore, active nodes should ensure a network performance in a parameter estimation scenario. Two problems are studied: sensor and link selection; and sensor, relay and link selection. To solve such problems, we present tractable optimization formulations and propose two algorithms that satisfy the previous network requirements. We also explore an extension scenario: only link selection. Simulation results show the performance of the algorithms and illustrate how they provide a sparse solution, which not only saves energy but also guarantees the network requirements.Comment: 27 pages, 17 figure