Game theoretic sensor management for target tracking

Shen, Dan; Chen, Genshe; Blasch, Erik; Pham, Khanh; Douville, Philip; Yang, Chun; Kadar, Ivan

doi:10.1117/12.850870

Cited by 9 publications

(10 citation statements)

References 7 publications

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“…35 In contrast with the noise-free case, the observer in general will not be able to reconstruct x[0] perfectly from the observation sequence in the noisy case (4). From classical linear-systems and non-random-parameter estimation results, it follows that finite-variance estimation of x[0] is possible if and only if the system (4) is observable over [k 0 , .…”

Section: Estimating Diffusive Processes: Results and Simulationsmentioning

confidence: 95%

Space object surveillance using incidental measurements from vehicle-board sensors

Roy

2014

SPIE Proceedings

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This article envisions surveillance and estimation in a future data-rich space environment, wherein spacecraft and other systems sense and record myriad environmental parameters incidentally to their primary missions. In this future environment, a wide range of sensor data will be available, but much of the data may be incidental, and hence subject to fluctuation, gaps, and low fidelity. Here, we explore estimation using such incidentalmeasurement data streams. Specifically, two canonical incidental-measurement-based estimation problems are posed -one concerned with recovering diffusive processes from an incidentally-mobile sensor, the other concerned with object (target) tracking using incidental measurements. Basic formal analyses of these estimation problems are pursued, and simulation results are also presented.

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Section: Estimating Diffusive Processes: Results and Simulationsmentioning

confidence: 95%

Space object surveillance using incidental measurements from vehicle-board sensors

Roy

2014

SPIE Proceedings

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“…It is an extension of our previous work [7]. In our negotiation framework, each negotiation agent represents a sensor.…”

Section: Trust-based Negotiation Game Model For Sensor Managementmentioning

confidence: 98%

“…In [7], a game-theoretic approach to distributed sensor-network management for target tracking via sensor-based negotiation has been developed. If every agent or sensor is rational and cooperative (i.e.…”

Section: Introductionmentioning

confidence: 99%

A trust-based sensor allocation algorithm in cooperative space search problems

Shen¹,

Chen²,

Pham³

et al. 2011

Sensors and Systems for Space Applications IV

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Sensor allocation is an important and challenging problem within the field of multi-agent systems. The sensor allocation problem involves deciding how to assign a number of targets or cells to a set of agents according to some allocation protocol. Generally, in order to make efficient allocations, we need to design mechanisms that consider both the task performers' costs for the service and the associated probability of success (POS). In our problem, the costs are the used sensor resource, and the POS is the target tracking performance. Usually, POS may be perceived differently by different agents because they typically have different standards or means of evaluating the performance of their counterparts (other sensors in the search and tracking problem). Given this, we turn to the notion of trust to capture such subjective perceptions. In our approach, we develop a trust model to construct a novel mechanism that motivates sensor agents to limit their greediness or selfishness. Then we model the sensor allocation optimization problem with trust-in-loop negotiation game and solve it using a sub-game perfect equilibrium. Numerical simulations are performed to demonstrate the trust-based sensor allocation algorithm in cooperative space situation awareness (SSA) search problems.

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“…A radar in a network tries to maximize the overall tracking accuracy criterion and a best response based dynamics is presented to find an equilibrium. In [26], the sensor management assigns moving sensors to targets through a distributed sensor-based negotiation game. Each sensor makes a decision locally that maximizes its utility by negotiating with the neighboring sensors.…”

Section: Introductionmentioning

confidence: 99%

Potential Game-Based Non-Myopic Sensor Network Planning for Multi-Target Tracking

Park¹,

Choi²

2018

IEEE Access

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This paper presents a potential game-based method for non-myopic planning of mobile sensor networks in the context of target tracking. The planning objective is to select the sequence of sensing points over more than one future time steps to maximize information about the target states. This multi-step lookahead scheme is studied to overcome getting trapped at local information maximum when there are gaps in sensing coverage due to constraints on the sensor platform mobility or limitations in sensing capabilities. However, the long-term planning becomes computationally intractable as the length of planing horizon increases. This work develops a gametheoretic approach to address the computational challenges. The main contributions of this paper are twofold: (a) to formulate a non-myopic planning problem for tracking multiple targets into a potential game, the size of which linearly increases as the number of planning steps (b) to design a learning algorithm exploiting the joint strategy fictitious play and dynamic programming, which overcomes the gaps in sensing coverage. Numerical examples of multi-target tracking demonstrate that the proposed method gives better estimation performance than myopic planning and is computationally tractable.

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Game theoretic sensor management for target tracking

Cited by 9 publications

References 7 publications

Space object surveillance using incidental measurements from vehicle-board sensors

Space object surveillance using incidental measurements from vehicle-board sensors

A trust-based sensor allocation algorithm in cooperative space search problems

Potential Game-Based Non-Myopic Sensor Network Planning for Multi-Target Tracking

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