We address the problem of jointly tracking and classifying several targets within a sensor network where false detections are present. In order to meet the requirements inherent to sensor networks such as distributed processing and low-power consumption, a collaborative signal processing algorithm is presented. At any time, for a given tracked target, only one sensor is active. This leader node is focused on a single target but takes into account the possible existence of other targets. It is assumed that the motion model of a given target belongs to one of several classes. This class-target dynamic association is the basis of our classification criterion. We propose an algorithm based on the sequential Monte Carlo (SMC) filtering of jump Markov systems to track the dynamic of the system and make the corresponding estimates. A novel class-based resampling scheme is developed in order to get a robust classification of the targets. Furthermore, an optimal sensor selection scheme based on the maximization of the expected mutual information is integrated naturally within the SMC target tracking framework. Simulation results are presented to illustrate the excellent performance of the proposed multitarget tracking and classification scheme in a collaborative sensor network.

We address the problem of jointly tracking and classifying several targets within a sensor network where false detections are present. A collaborative signal processing algorithm where multiple targets are dynamically associated with leader nodes is presented. It is assumed that each target belongs to one of several classes and that the class information leads to the motion model of a target. We propose an algorithm based on sequential Monte Carlo (SMC) filtering of jump Markov systems to jointly track the system dynamic and classify the targets. Furthermore, an optimal sensor selection scheme based on the maximization of the expected mutual information is integrated naturally within the SMC tracking framework. Simulation results have illustrated the excellent performance of the proposed scheme.

We consider the application of sequential Monte Carlo (SMC) methods for Bayesian inference to the problem of information-driven dynamic sensor collaboration in clutter environments for sensor networks. The dynamics of the system under consideration are described by nonlinear sensing models within randomly deployed sensor nodes. The exact solution to this problem is prohibitively complex due to the nonlinear nature of the system. The SMC methods are, therefore, employed to track the probabilistic dynamics of the system and to make the corresponding Bayesian estimates and predictions. To meet the specific requirements inherent in sensor network, such as low-power consumption and collaborative information processing, we propose a novel SMC solution that makes use of the auxiliary particle filter technique for data fusion at densely deployed sensor nodes, and the collapsed kernel representation of the a posteriori distribution for information exchange between sensor nodes. Furthermore, an efficient numerical method is proposed for approximating the entropy-based information utility in sensor selection. It is seen that under the SMC framework, the optimal sensor selection and collaboration can be implemented naturally, and significant improvement is achieved over existing methods in terms of localizing and tracking accuracies.

In large-scale dynamic communication networks, endsystems can not rely on the network itself to cooperate in characterizing its own behavior. This has prompted research activities on methods for inferring internal network behavior based on the external end-to-end network measurements. In particular, knowledge of the link losses inside the network is important for network management. However it is impractical to directly measure packet losses or delays at every router. On the other hand, measuring end-to-end (from sources to receivers) losses is relatively easy. We formulate the problems of link in a network as Bayesian inference problems and develop several Markov chain Monte Carlo (MCMC) algorithms to solve them. We then apply the proposed link loss algorithms to data generated by the Network Simulator (NS2) software, and obtain good agreements between the theoretical results and the actual measurements.

We provide a novel sequential estimation and detection approach for multiple-input-multiple-output (MIMO) systems. The basic idea is to design a probabilistic approximation method for the computation of the maximum a posterior distribution (MAP) via the sequential Monte Carlo methods (SMC). The SMC method has two advantages over the other methods in that it is a blind method and can be computed in parallel. Furthermore, the SMC has characteristics of soft-input and soft-output in nature and, thus, it can be employed as the first stage demodulator in a turbo receiver for a coded MIMO system. Such a turbo receiver successively improves the receiver performance by iteratively exchanging the so-called extrinsic information with the MAP outer channel decoder. Finally, the performance of the proposed sequential Monte Carlo receiver is demonstrated through computer simulations for MIMO systems over single-path and multipath fading channels.

An adaptive Bayesian receiver for blind detection in flat-fading channels was developed in Chen et al. (2000), based on the sequential Monte Carlo methodology. That work is built on a parametric modelling of the fading process in the form of a state-space model, and assumes the knowledge of the second-order statistics of the fading channel. In this paper, we develop a nonparametric approach to the problem of blind detection in fading channels, without assuming any knowledge of the channel statistics. The basic idea is to decompose the fading process using a wavelet basis, and to use the sequential Monte Carlo technique to track both the wavelet coefficients and the transmitted symbols. A novel resampling-based wavelet shrinkage technique is proposed to dynamically choose the number of wavelet coefficients to best fit the fading process. Under such a framework, blind detectors for flat-fading channels are developed. Simulation results are provided to demonstrate the excellent performance of the proposed blind adaptive receivers.