&lt;title&gt;Search for optimal sensor management&lt;/title&gt;

Kastella, Keith; Musick, Stanton

doi:10.1117/12.241194

Cited by 17 publications

(6 citation statements)

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“…Various techniques are used to determine how often to search a cell to detect a target. Blackman [48] divides the sensor management approaches into normative (objective optimization criteria) and descriptive (adaptive) approaches. Popular methods such as direct search (or myopic), alert/confirm (or cueing), hierarchical goal lattices [49] utilize a Bayesian technique.…”

Section: 4mentioning

confidence: 99%

Sensor-based allocation for path planning and area coverage using UGSs

Blasch

Maupin

Jousselme

2010

Proceedings of the IEEE 2010 National Aerospace &Amp; Electronics Conference

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The goal of the project is to incorporate situational awareness methods and performance evaluation using distributed unattended ground sensor (UGS) wireless sensor networks, area coverage, and path planning to monitor targets. Generalized measures of performance are utilized for determining which sensors to allocate to observe a moving target through obscurations. Using a variety of techniques in the literature, we propose a method of analysis for dynamic resource management for decision support. In this paper, we simulate a path planning object that moves to goal in the presence of obstacles. The distributed sensors must maintain area coverage and perform sensor management to maintain the path of the object.We investigated the sensor management functions of direct search, cueing, the sequential probability ratio test (SPRT), and information theoretical methods of KL divergence (or discrimination gain) and mutual information. These methods afford sensor hand-off and can be used to optimal place sensors to maintain are coverage and target tracking.

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Section: 4mentioning

confidence: 99%

Sensor-based allocation for path planning and area coverage using UGSs

Blasch

Maupin

Jousselme

2010

Proceedings of the IEEE 2010 National Aerospace &Amp; Electronics Conference

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“…The SOP agents deposit pheromones along the paths connecting the nodes indicating good selections as time progresses. Initially, all paths have an equal amount of pheromone or (3) where i is the path's initial sensor operating parameter set and k is where the path terminates. The probability of selecting path ik is the transition probability or (4) where is a parameter affecting convergence and is the pheromone for path ik.…”

Section: Detailed Algorithm Descriptionmentioning

confidence: 99%

Autonomous sensor manager agents (ASMA)

Osadciw

2004

SPIE Proceedings

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Autonomous sensor manager agents are presented as an algorithm to perform sensor management within a multisensor fusion network. The design of the hybrid ant system/particle swarm agents is described in detail with some insight into their performance. Although the algorithm is designed for the general sensor management problem, a simulation example involving 2 radar systems is presented. Algorithmic parameters are determined by the size of the region covered by the sensor network, the number of sensors, and the number of parameters to be selected. With straight forward modifications, this algorithm can be adapted for most sensor management problems.

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“…This section provides a brief overview of Interacting Multiple Model Kalman Filters [8][9] [10] (IMMKF). The IMMKF blends motion models matched to different flight regimes to achieve improved performance against maneuvering target and minimize latency due to model switching.…”

Section: A Interacting Multiple Model Kalman Filter (Imm-kf)mentioning

confidence: 99%

Multi-sensor management: Optimal allocation of tracking resources for P<inf>d</inf> &#60; 1 based on the interacting multiple model modified kalman filter

Tong

Shan

2011

2011 International Conference on Electric Information and Control Engineering

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This paper presents a novel sensor selection algorithm for optimal allocation of target tracking resources, based on the interacting multiple model modified kalman filter. The algorithm can be easily calculated and it is possible to include sensors with a probability of detection p d <1. The sensor selection measure function is the minimum trace of covariance matrix. The performance of the sensor selection algorithm is studied for single sensor and sensor collocation. And simulation shows the method is effective and feasible.

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<title>Search for optimal sensor management</title>

Cited by 17 publications

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Sensor-based allocation for path planning and area coverage using UGSs

Sensor-based allocation for path planning and area coverage using UGSs

Autonomous sensor manager agents (ASMA)

Multi-sensor management: Optimal allocation of tracking resources for P<inf>d</inf> &#60; 1 based on the interacting multiple model modified kalman filter

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<title>Search for optimal sensor management</title>

Cited by 17 publications

References 0 publications

Sensor-based allocation for path planning and area coverage using UGSs

Sensor-based allocation for path planning and area coverage using UGSs

Autonomous sensor manager agents (ASMA)

Multi-sensor management: Optimal allocation of tracking resources for P<inf>d</inf> &#38;#60; 1 based on the interacting multiple model modified kalman filter

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Multi-sensor management: Optimal allocation of tracking resources for P<inf>d</inf> < 1 based on the interacting multiple model modified kalman filter