Tracking a moving object with a binary sensor network

Aslam, Javed A.; Butler, Zack; Constantin, F.; Crespi, Valentino; Cybenko, George; Rus, Daniela

doi:10.1145/958491.958509

Cited by 315 publications

(235 citation statements)

References 8 publications

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“…The constraints of network reliability and load permit sending only a limited amount of data over the network, and in some extreme cases, even a single bit of data. Such binary networks have been used in previous work for tracking [4][5][6]. Even classification schemes based on low dimensional decision fusion [7] require significant local signal processing.…”

Section: Related Workmentioning

confidence: 99%

A line in the sand: a wireless sensor network for target detection, classification, and tracking

et al. 2004

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Abstract. Intrusion detection is a surveillance problem of practical import that is well suited to wireless sensor networks. In this paper, we study the application of sensor networks to the intrusion detection problem and the related problems of classifying and tracking targets. Our approach is based on a dense, distributed, wireless network of multi-modal resource-poor sensors combined into loosely coherent sensor arrays that perform in situ detection, estimation, compression, and exfiltration. We ground our study in the context of a security scenario called "A Line in the Sand" and accordingly define the target, system, environment, and fault models. Based on the performance requirements of the scenario and the sensing, communication, energy, and computation ability of the sensor network, we explore the design space of sensors, signal processing algorithms, communications, networking, and middleware services. We introduce the influence field, which can be estimated from a network of binary sensors, as the basis for a novel classifier. A contribution of our work is that we do not assume a reliable network; on the contrary, we quantitatively analyze the effects of network unreliability on application performance. Our work includes multiple experimental deployments of over 90 sensors nodes at MacDill Air Force Base in Tampa, Florida, as well as other field experiments of comparable scale. Based on these experiences, we identify a set of key lessons and articulate a few of the challenges facing extreme scaling to tens or hundreds of thousands of sensor nodes.

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Section: Related Workmentioning

confidence: 99%

A line in the sand: a wireless sensor network for target detection, classification, and tracking

et al. 2004

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“…-In [12] and [13] the target location is approximated by the location of a sensor when the target comes within its range. The resulting resolution is the same as that of the sensors.…”

Section: Related Workmentioning

confidence: 99%

Sensor Selection Heuristic in Sensor Networks

Sadaphal

Jain

2005

Lecture Notes in Computer Science

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Abstract. We consider the problem of estimating the location of a moving target in a 2-D plane. In this paper, we focus attention on selecting an appropriate 3 rd sensor, given two sensors, with a view to minimize the estimation error. Only the selected sensors need to measure distance to the target and communicate the same to the central "tracker". This minimizes bandwidth and energy consumed in measurement and communication while achieving near minimum estimation error. In this paper, we have proposed that the 3 rd sensor be selected based on three measures viz. (a) collinearity, (b) deviation from the ideal direction in which the sensor should be selected, and (c) proximity of the sensor from the target. We assume that the measurements are subject to multiplicative error. Further, we use least square error estimation technique to estimate the target location. Simulation results show that using the proposed algorithm it is possible to achieve near minimum error in target location.

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“…Most of the previous researches focus on tracking a single target [25]- [27] or assume targets are distinguishable [28]. A string of researches on tracking targets with wireless sensor networks are based on binary proximity sensors which can only report whether there are targets within sensing areas.…”

Section: Related Workmentioning

confidence: 99%

“…A string of researches on tracking targets with wireless sensor networks are based on binary proximity sensors which can only report whether there are targets within sensing areas. The initial work [25]- [27] on binary proximity sensors focuses on tracking single target. Singh et al [29] extended the approach to track multiple indistinguishable targets by applying particle filtering algorithms.…”

Section: Related Workmentioning

confidence: 99%

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

Zhu

Vikram

et al. 2014

IEEE Trans. Wireless Commun.

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Abstract-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 cooperative 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.

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Tracking a moving object with a binary sensor network

Cited by 315 publications

References 8 publications

A line in the sand: a wireless sensor network for target detection, classification, and tracking

A line in the sand: a wireless sensor network for target detection, classification, and tracking

Sensor Selection Heuristic in Sensor Networks

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

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