Tracking multiple targets with self-organizing distributed ground sensors

Brooks, Richard; Friedlander, David; Koch, John; Phoha, Shashi

doi:10.1016/j.jpdc.2003.12.005

Cited by 37 publications

(34 citation statements)

References 6 publications

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“…where H(K) is given by Equation (5). The set of all cliques C can be split into two disjoint sets C i and " C i such that C i is the set of all cliques that contain s i and " C i ¼ CnC i .…”

Section: B Equivalence Of Markov and Gibbs Random Fieldsmentioning

confidence: 99%

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Adaptive Sensor Activity Scheduling in Distributed Sensor Networks: A Statistical Mechanics Approach

Srivastav

Ray

Phoha

2009

International Journal of Distributed Sensor Networks

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This article presents an algorithm for adaptive sensor activity scheduling (A-SAS) in distributed sensor networks to enable detection and dynamic footprint tracking of spatial-temporal events. The sensor network is modeled as a Markov random field on a graph, where concepts of Statistical Mechanics are employed to stochastically activate the sensor nodes. Using an Ising-like formulation, the sleep and wake modes of a sensor node are modeled as spins with ferromagnetic neighborhood interactions; and clique potentials are defined to characterize the node behavior. Individual sensor nodes are designed to make local probabilistic decisions based on the most recently sensed parameters and the expected behavior of their neighbors. These local decisions evolve to globally meaningful ensemble behaviors of the sensor network to adaptively organize for event detection and tracking. The proposed algorithm naturally leads to a distributed implementation without the need for a centralized control. The A-SAS algorithm has been validated for resource-aware target tracking on a simulated sensor field of 600 nodes.

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Section: B Equivalence Of Markov and Gibbs Random Fieldsmentioning

confidence: 99%

“…Distributed sensor networks have been built for both military (e.g., target tracking in urban terrain) and commercial (e.g., weather, habitat and pollution monitoring, and structural health monitoring) applications [4][5][6][7][8]. Sensor network operations can be broadly classified as:…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sensor Activity Scheduling in Distributed Sensor Networks: A Statistical Mechanics Approach

Srivastav

Ray

Phoha

2009

International Journal of Distributed Sensor Networks

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“…Many target tracking algorithms developed for sensor networks are designed for single-target tracking [10][11][12][13][14][15][16][17][18][19][20][21] and some of these algorithms are applied to track multiple targets using classification [10][11][12] or heuristics, such as the nearest-neighbor filter (NNF) used in [13]. The NNF [6] processes the new measurements in some predefined order and associates each with the target whose predicted position is closest, thereby selecting a single association.…”

Section: Introductionmentioning

confidence: 99%

“…We can categorize a target tracking algorithm for sensor networks by its computational structure: centralized [11,14,15], hierarchical [16,17], or distributed [10,12,13,[18][19][20][21][22][23][24]. Since each sensor can only sense a small region around it, and its measurements are noisy and inconsistent, measurements only from a single sensor and its neighboring sensors are not sufficient to initiate, maintain, disambiguate, and terminate tracks of multiple targets.…”

Section: Introductionmentioning

confidence: 99%

A Scalable Multi-Target Tracking Algorithm for Wireless Sensor Networks

2012

International Journal of Distributed Sensor Networks

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Multi-target tracking is a representative real-time application of sensor networks as it exhibits different aspects of sensor networks such as event detection, sensor information fusion, multihop communication, sensor management, and real-time decision making. The task of tracking multiple objects in a wireless sensor network is challenging due to constraints on a sensor node such as short communication and sensing ranges, a limited amount of memory, and limited computational power. In addition, since a sensor network surveillance system needs to operate autonomously without human operators, it requires an autonomous realtime tracking algorithm which can track an unknown number of targets. In this paper, we develop a scalable real-time multi-target tracking algorithm that is autonomous and robust against transmission failures, communication delays, and sensor localization error. The algorithm is based on a rigorous probabilistic model and an approximation scheme for the optimal Bayesian filter. In particular, an extensive simulation study shows that there is no performance loss up to an average localization error of 0.7 times the separation between sensors and the algorithm tolerates up to 50% lost-to-total packet ratio and 90% delayed-to-total packet ratio. The proposed algorithm has been successfully applied to real-time multi-target tracking problems using wireless sensor networks.

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“…The everdecreasing cost of sensor networks will make them ubiquitous in many aspects of our lives [8] such as building comfort control [9], environmental monitoring [10], traffic control [11], manufacturing and plant automation [12], service robotics [13], and surveillance systems [14,15].…”

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Tracking and Coordination of Multiple Agents Using Sensor Networks: System Design, Algorithms and Experiments

et al. 2007

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Abstract-This paper considers the problem of pursuit evasion games (PEGs), where a group of pursuers is required to chase and capture a group of evaders in minimum time with the aid of a sensor network. We assume that a sensor network is previously deployed and provides global observability of the surveillance region, allowing an optimal pursuit policy. While sensor networks provide global observability, they cannot provide high quality measurements in a timely manner due to packet losses, communication delays, and false detections. This has been the main challenge in developing a real-time control system using sensor networks. We address this challenge by developing a real-time hierarchical control system which decouples the estimation of evader states from the control of pursuers via multiple layers of data fusion. While a sensor network generates noisy, inconsistent, and bursty measurements, the multiple layers of data fusion convert them into consistent and high quality measurements and forward them to the controllers of pursuers in a timely manner. For this control system, three new algorithms are developed: multi-sensor fusion, multi-target tracking and multi-agent coordination algorithms. The multi-sensor fusion algorithm converts correlated sensor measurements into position estimates, the multi-target tracking algorithm tracks an unknown number of targets, and the multi-agent coordination algorithm coordinates pursuers to capture all evaders in minimum time using a robust minimum-time feedback controller. The combined system is evaluated in simulation and tested in a sensor network deployment. To our knowledge, this paper presents the first demonstration of multi-target tracking using a sensor network without relying on classification.

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Tracking multiple targets with self-organizing distributed ground sensors

Cited by 37 publications

References 6 publications

Adaptive Sensor Activity Scheduling in Distributed Sensor Networks: A Statistical Mechanics Approach

Adaptive Sensor Activity Scheduling in Distributed Sensor Networks: A Statistical Mechanics Approach

A Scalable Multi-Target Tracking Algorithm for Wireless Sensor Networks

Tracking and Coordination of Multiple Agents Using Sensor Networks: System Design, Algorithms and Experiments

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