In this position paper, we investigate the use of wireless sensor network (WSN) technology for ground surveillance. The goal of our project is to develop a prototype of WSN for outdoor deployment. We aim to design a system, which can detect and classify multiple targets (e.g., vehicles and troop movements), using inexpensive off-the-shelf wireless sensor devices, capable of sensing acoustic and magnetic signals generated by different target objects. In order to archive our goals, we intend to design a system, which is capable of automatic selforganization and calibration. Such a system would need to be capable of performing detection and tracking of targets as well as sending the real time enemy mobility information to a command centre. Real-time tacking with WSN is extremely challenging since it requires high system robustness, real time decision making, high frequency sampling, multi-modality of sensing, complex signal processing and data fusion, distributed coordination and wide area coverage. We propose a Hybrid Sensor Network architecture (HSN), tailored specifically to meet these challenges. We investigate data fusion technologies such as particle filters, to handle both environmental and sensing noises of inexpensive sensors.

Newly developed resampling algorithms for particle filters suitable for real-time implementation are described and their analysis is presented. The new algorithms reduce the complexity of both hardware and DSP realization through addressing common issues such as decreasing the number of operations and memory access. Moreover, the algorithms allow for use of higher sampling frequencies by overlapping in time the resampling step with the other particle filtering steps. Since resampling is not dependent on any particular application, the analysis is appropriate for all types of particle filters that use resampling. The performance of the algorithms is evaluated on particle filters applied to bearings-only tracking and joint detection and estimation in wireless communications. We have demonstrated that the proposed algorithms reduce the complexity without performance degradation.

Particle filters are sequential Monte Carlo methods that are used in numerous problems where time-varying signals must be presented in real time and where the objective is to estimate various unknowns of the signal and/or detect events described by the signals. The standard solutions of such problems in many applications are based on the Kalman filters or extended Kalman filters. In situations when the problems are nonlinear or the noise that distorts the signals is non-Gaussian, the Kalman filters provide a solution that may be far from optimal. Particle filters are an intriguing alternative to the Kalman filters due to their excellent performance in very di#cult problems including communications, signal processing, navigation, and computer vision. Hence, particle filters have been the focus of wide research recently and immense literature can be found on their theory. Most of these works recognize the complexity and computational intensity of these filters, but there has been no e#ort directed toward the implementation of these filters in hardware. The objective of this dissertation is to develop, design, and build e#cient hardware for particle filters, and thereby bring them closer to practical applications. The fact that particle filters outperform most of the traditional filtering methods in many complex practical scenarios, coupled with the challenges related to decreasing their computational complexity and improving real-time performance, makes this work worthwhile. The main

Modern graphics cards for computers, and especially their graphics processing units (GPUs), are designed for fast ren-dering of graphics. In order to achieve this GPUs are equipped with a parallel architecture which can be exploited for general-purpose computing on GPU (GPGPU) as a com-plement to the central processing unit (CPU). In this paper GPGPU techniques are used to make a parallel GPU imple-mentation of state-of-the-art recursive Bayesian estimation using particle filters (PF). The modifications made to obtain a parallel particle filter, especially for the resampling step, are discussed and the performance of the resulting GPU im-plementation is compared to one achieved with a traditional CPU implementation. The resulting GPU filter is faster with the same accuracy as the CPU filter for many particles, and it shows how the particle filter can be parallelized. 1.

Video cameras are among the most commonly used sensors in a large number of applications, ranging from surveillance to smart rooms for videoconferencing. There is a need to develop algorithms for tasks such as detection, tracking, and recognition of objects, specifically using distributed networks of cameras. The projective nature of imaging sensors provides ample challenges for data association across cameras. We first discuss the nature of these challenges in the context of visual sensor networks. Then, we show how realworld constraints can be favorably exploited in order to tackle these challenges. Examples of real-world constraints are a) the presence of a world plane, b) the presence of a threedimiensional scene model, c) consistency of motion across cameras, and d) color and texture properties. In this regard, the main focus of this paper is towards highlighting the efficient use of the geometric constraints induced by the imaging devices to derive distributed algorithms for target detection, tracking, and recognition. Our discussions are supported by several examples drawn from real applications. Lastly, we also describe several potential research problems that remain to be addressed.

Abstract – We present a distributed particle filtering algorithm for target tracking in sensor networks. Several existing algorithms rely on the establishment and maintenance of a spanning path or tree. This is challenging in networks with dynamic topologies induced by mobile nodes and changing wireless conditions; the algorithms are vulnerable to link or node failure. More recent algorithms employ consensus algorithms to improve robustness but they adopt suboptimal fusion rules leading to a significant deterioration in performance. In our algorithm, nodes run local particle filters and then approximate their local posteriors using Gaussian approximations. A global posterior approximation is then computed using a novel gossiping approach that implements the optimal fusion rule. The resultant protocol is simple, robust and efficient. We present simulation results demonstrating a significant performance improvement over the best-performing existing algorithm.

Abstract—The work reported in this paper investigates the performance of the Particle Filter (PF) algorithm for tracking a moving object using a wireless sensor network (WSN). It is well known that the PF is particularly well suited for use in target tracking applications. However, a comprehensive analysis on the effect of various design and calibration parameters on the accuracy of the PF has been overlooked. This paper outlines the results from such a study. In particular, we evaluate the effect of various design parameters (such as the number of deployed nodes, number of generated particles, and sampling interval) and calibration parameters (such as the gain, path loss factor, noise variations, and nonlinearity constant) on the tracking accuracy and computation time of the particle-filter-based tracking system. Based on our analysis, we present recommendations on suitable values for these parameters, which provide a reasonable trade-off between accuracy and complexity. We also analyze the theoretical Cramér-Rao Bound as the benchmark for the best possible tracking performance and demonstrate that the results from our simulations closely match the theoretical bound. In this paper, we also propose a novel technique for calibrating off-the-shelf sensor devices. We implement the tracking system on a real sensor network and demonstrate its accuracy in detecting and tracking a moving object in a variety of scenarios. To the best of our knowledge, this is the first time that empirical results from a PF-based tracking system with off-the-shelf WSN devices have been reported. Finally, we also present simple albeit important building blocks that are essential for field deployment of such a system. Index Terms—Wireless sensor networks, simulations, experiments, performance attributes, measurements. Ç 1

Abstract—We describe distributed tracking of a nonlinear dynamical system via networked sensors. The sensors communicate with each other by means of a multihop protocol over a communication network.We derive in-network processing algorithms to deal with arbitrary network topology and then extend these results to account for communication delays and packet losses. We show that these algorithms are optimal in the linear setting and achieve centralized performance. The proposed techniques differ from existing techniques in two important aspects: a) there is no designated leader/fusion node and each sensor attempts to optimally track the system trajectory based on its local observations and time-dependent information available from other sensors in the network; b) the message computation at each sensor is structurally identical. Consequently, the sensor network can be queried at any time and at any node to obtain optimal estimates for the state of the dynamical system. We then present two multihop protocols—one based on gossip and another token-based—for distributed implementation of the in-network processing techniques. We show several advantages of token-based schemes over gossip protocols: a) message complexity is significantly smaller for achieving the same performance; b) they are well-suited for situations where target and network data aggregation time-scales are comparable; and c) they are well-suited for random geometric graphs with nodes having small communication-connectivity radius — a scenario typical of ad-hoc wireless networks. This is because they can fuse data only from the set of nodes that can be visited in any time period. Index Terms—Ad-hoc wireless networks, energy efficiency, gossip, token-based protocols, link losses and delays, target tracking. I.

In this paper, we analyze the computational challenges in implementing particle filtering especially to video sequences. Particle filtering is a technique used for filtering non-linear dynamical systems driven by non-Gaussian noise processes. It has found wide-spread applications in detection, navigation and tracking problems. Although, in general, particle filtering methods yield improved results, it is difficult to achieve real time performance. In this paper, we analyze the computational drawbacks of traditional particle filtering algorithms, and present a method for implementing the particle filter using the Independent Metropolis Hastings sampler, that is highly amenable to pipelined implementations and parallelization. We analyze the implementations of the proposed algorithm, and in particular concentrate on implementations that have minimum processing times. It is shown that the design parameters for the fastest implementation can be chosen by solving a set of convex programs. The proposed computational methodology was verified over a cluster of PCs for the application of visual tracking. We demonstrate a linear speedup of the algorithm using the methodology proposed in the paper.

We describe an algorithm for tracking an object using particle filtering in a sensor network comprised of smart dusttype motes. We investigate the situation where the motes are equipped with binary proximity sensors, low-power lasers and optical receivers for communication with nearby motes, and corner-cube arrays for communication with a central transceiver. The particle filter we describe is largely decentralized; a central transceiver performs no processing beyond a summation and weighted average. Individual motes act as the particles in that they represent candidate positions of the object. Propagation of the particle filter is performed through activation of appropriate neighbouring nodes with “weighted ” messages. We provide simulation results of tracking a maneuvering object, comparing performance with a centralized particle filter. 1.