Target Tracking with Limited Sensing Range in Autonomous Mobile Sensor Networks

Abstract

Abstract-As technology advancements in robotics and wireless communication, tracking mobile targets using mobile sensors has aroused widespread concern in recent years. In this paper, we propose a novel coordinative moving strategy for autonomous mobile sensor networks to guarantee the target can be detected in each observed step while minimizing the amount of moving sensors. The proposed scheme consists of obtaining the current position of the target, which is then used to predict the next time-step location of the target. Once the uncertainty region of the target's position is defined, the proposed method allows the mobile sensors to cover it in an optimal way. Therefore, we can assign each mobile sensor to an optimal location to cover the uncertainty region while minimizing the total traveled distance of sensors. Extensive simulations are given to evaluated performance and demonstrate the efficiency of the proposed strategy.

Continuous tracking for mobile targets with mobility nodes in WSNs

“…They propose a strategy for managing sensors mobility, aiming at minimize the total traveled distance by the nodes. Bai et al [16] propose a coordinative moving strategy for autonomous mobile sensor networks to guarantee the target can be detected in each observed step while minimizing the amount of moving sensors. In these three literatures, all nodes have mobility in their scenarios, so their works focus on the selection and motion strategy of sensors, rather than the energy efficiency.…”

“…However, the tracking faults caused by coverage holes weaken the effectiveness of this kind of solutions. Recent years, as the energy of mobile nodes is unrestricted compared to ordinary fixed nodes, researchers began to introduce mobile nodes into WSNs [12], [13], [14], [15], [16], [17], [18], [19].…”

Survey: mobile sensor networks for target searching and tracking

“…Typically, one Kalman filter is used for each tracked target. For example, Cheng et al [24], Prabhavathi and Rajeshwari [25] and Bai et al [26] all utilize linear Kalman filters to estimate the target states. In addition, both Haugen and Imsland [27] and Albert et al [28] use Kalman filters to estimate the state of moving icebergs with UAVs acting as a mobile sensor network.…”

“…There are multiple papers that deal with single tracking, which uses an objective function in which the cost matrix consists of distances between current and new sensor positions. In Bai et al [26], the new sensor positions are found by minimizing target uncertainty. The assignment problem, equation (21), is solved using the Hungarian method [68].…”

Modified Extended Kalman Filtering for Tracking with Insufficient and Intermittent Observations

“…Prior work for tracking mobile target in WSNs can be roughly categorized into three types in the light of the mechanism adopted: the trilateration-based schemes [5], the modelbased methods like Kalman filter [6][7][8] and particle filtering [9][10][11], and techniques based on the additional hardware on the target [12]. It is noticed that all the tracking methods given in the aforementioned references are derived based on the assumption that the WSNs are reliable to get the satisfactory tracking results.…”