Optimal Motion Strategies for Range-Only Constrained Multisensor Target Tracking

Zhou, Ke; Roumeliotis, Stergios I.

doi:10.1109/tro.2008.2004488

Cited by 80 publications

(52 citation statements)

References 27 publications

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“…The proof is shown in [13]. The importance of this lemma is that the optimization algorithm presented in Section IV can be derived based on (12) for the position covariance update -instead of (11) for the entire state covariance update -regardless of the stochastic process model employed for describing the target's motion.…”

Section: Proofmentioning

confidence: 99%

Multirobot Active Target Tracking With Combinations of Relative Observations

2011

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Abstract-In this paper, we study the problem of optimal trajectory generation for a team of mobile robots tracking a moving target using distance and bearing measurements. Contrary to previous approaches, we explicitly consider limits on the robots' speed and impose constraints on the minimum distance at which the robots are allowed to approach the target. We first address the case of a single sensor and show that although this problem is non-convex with non-convex constraints, in general, its optimal solution can be determined analytically. Moreover, we extend this approach to the case of multiple sensors and propose an iterative algorithm, Gauss-Seidel-relaxation (GSR), for determining the set of feasible locations that each sensor should move to in order to minimize the uncertainty about the position of the target. Extensive simulation results are presented demonstrating that the performance of the GSR algorithm, whose computational complexity is linear in the number of sensors, is indistinguishable of that of a grid-based exhaustive search, with cost exponential in the number of sensors, and significantly better than that of a random, towards the target, motion strategy.

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Section: Proofmentioning

confidence: 99%

Multirobot Active Target Tracking With Combinations of Relative Observations

2011

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“…In future work, we plan to incorporate active sensing (optimal sensor motion control) [26] into the current estimation framework to further improve tracking performance. In addition, the extension of current work to target tracking in 3D will be of practical importance.…”

Section: Discussionmentioning

confidence: 99%

Bearing-only target tracking using a bank of MAP estimators

Huang

Zhou

Trawny

et al. 2011

2011 IEEE International Conference on Robotics and Automation

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Abstract-Nonlinear estimation problems, such as bearingonly tracking, are often addressed using linearized estimators, e.g., the extended Kalman filter (EKF). These estimators generally suffer from linearization errors as well as the inability to track multimodal probability density functions (pdfs). In this paper, we propose a bank of batch maximum a posteriori (MAP) estimators as a general estimation framework that provides relinearization of the entire state history, multi-hypothesis tracking, and an efficient hypothesis generation scheme. Each estimator in the bank is initialized using a locally optimal state estimate for the current time step. Every time a new measurement becomes available, we convert the nonlinear cost function corresponding to this relaxed one-step subproblem into polynomial form, allowing to analytically and efficiently compute all stationary points. This local optimization generates highly probable hypotheses for the target trajectory and greatly improves the quality of the overall MAP estimate. Additionally, pruning and marginalization are employed to control the computational cost. Monte Carlo simulations and real-world experiments show that the proposed approach significantly outperforms the EKF, the standard batch MAP estimator, and the particle filter (PF), in terms of accuracy and consistency.

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“…Zhou et al [8] present optimal centralized motion strategies for solving the problem of multiple mobile sensors cooperatively for tracking a moving target using distance-only measurements. The authors employ the trace of the targets position estimate covariance matrix as the object function.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, the authors also account for the velocity constraints on mobile sensors. Different from precious work [8], the authors [9] impose constraints on the minimum distance at which the mobile sensors are allowed to be close to the target. Besides, the authors adopt measurements extend to a mixture of relative observations, including distance-only, bearing-only, and distance-and-bearing measurements.…”

Section: Related Workmentioning

confidence: 99%

“…In order to guarantee the target can be detected in each step, it is important to determine the uncertainty for the estimated position of the target. According to [8], Zhou et al consider that target's uncertainty position is depicted by the 3σ ellipse. It is proved that if the sensor keep still and measures the distance to the target, the sensor believes that the target is within the uncertainty region with high probability.…”

Section: A Calculate the Possible Region For Mobile Targetmentioning

confidence: 99%

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Target Tracking with Limited Sensing Range in Autonomous Mobile Sensor Networks

Bai

Cheng

Chen

et al. 2012

2012 IEEE 8th International Conference on Distributed Computing in Sensor Systems

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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.

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Optimal Motion Strategies for Range-Only Constrained Multisensor Target Tracking

Cited by 80 publications

References 27 publications

Multirobot Active Target Tracking With Combinations of Relative Observations

Multirobot Active Target Tracking With Combinations of Relative Observations

Bearing-only target tracking using a bank of MAP estimators

Target Tracking with Limited Sensing Range in Autonomous Mobile Sensor Networks

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