An optimization model for target tracking of mobile sensor network based on motion state prediction in emerging sensor networks

Hu, Fengjun; Tu, Chun Lei

doi:10.3233/jifs-169288

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…Another similar approach for modifying the particle filter with a modified importance density function and resampling method is Juan-Yi [47]. In Hu and Tu [48], the authors use a combination of stationary and mobile sensors to track a single target. For target estimation they use a particle filter, which they modify for low energy consumption through parallel processing and better anti-noise capability.…”

Section: Particle Filtermentioning

confidence: 99%

“…Both [83] Searching [140,144,118] Tracking [28,134,101,27,116,46,117] Single Target Both [] Searching [30,133] Tracking [24,26,42,47,48,70,125,111,110,112,113,124,115,135,107] Fig. 5: Classification of centralized approaches presented in this survey Distributed Multiple Targets Both [72,34,142,127,126,128,84] Searching [130,121,138] Tracking [53,119,129,82,122,94,95,29,81,106,…”

Section: Centralized Multiple Targetsmentioning

confidence: 99%

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Survey: mobile sensor networks for target searching and tracking

Albert

Imsland

2018

Cyber-Physical Systems

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Mobile sensor networks can be employed in multiple applications, such as search and rescue, border patrol, battle scenarios, and environmental monitoring. In this survey, we review the literature utilizing mobile sensor networks in applications classified as target searching and/or tracking. Our contribution is threefold. First, we focus on the diverse types of filters applied to estimating the state of the targets. Second, we present the most common approaches to high-level trajectory planning for the sensors in the network to do target searching and/or tracking. Finally, we classify the literature based on the problem formulation used and solution characteristics. At the end of the survey, we discuss the current state of the literature and possible directions for future research efforts.

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Section: Particle Filtermentioning

confidence: 99%

Section: Centralized Multiple Targetsmentioning

confidence: 99%

Survey: mobile sensor networks for target searching and tracking

Albert

Imsland

2018

Cyber-Physical Systems

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“…Due to the shortcomings of the traditional particle filter algorithm, an improved particle filter algorithm is designed in Ref. [26] for target tracking. First, the location model is constructed with the features of the target as the measurements.…”

Section: Related Workmentioning

confidence: 99%

A target group tracking algorithm based on a hybrid sensor network

Chun¹

2018

Chinese Phys. B

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Traditional tracking algorithms based on static sensors have several problems. First, the targets only occur in a part of the interested area; however, a large number of static sensors are distributed in the area to guarantee entire coverage, which leads to wastage of sensor resources. Second, many static sensors have to remain in active mode to track the targets, which causes an increase of energy consumption. To solve these problems, a target group tracking algorithm based on a hybrid sensor network is proposed in this paper, which includes static sensors and mobile sensors. First, an estimation algorithm is proposed to estimate the objective region by static sensors, which work in low-power sensing mode. Second, a movement algorithm based on sliding windows is proposed for mobile sensors to obtain the destinations. Simulation results show that this algorithm can reduce the number of mobile sensors participating in the tracking task and prolong the network lifetime.

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Multi-objective cooperative control based on evolutionary process optimization of immune algorithm

Tuzikov

2020

SOCA

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An optimization model for target tracking of mobile sensor network based on motion state prediction in emerging sensor networks

Cited by 5 publications

References 13 publications

Survey: mobile sensor networks for target searching and tracking

Survey: mobile sensor networks for target searching and tracking

A target group tracking algorithm based on a hybrid sensor network

Multi-objective cooperative control based on evolutionary process optimization of immune algorithm

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