Paper
25 August 2003 Intelligent sensor management to minimize detection error
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Abstract
This paper analyzes the impact on target detection of several alternative sensor management schemes. Past work in this area has shown that myopic discrimination optimization can be a useful heuristic. In this paper we compare the performance obtained using discrimination with direct optimization of the detection error rate using both myopic and non-myopic optimization techniques. Our model consists of a gridded region containing a set of targets with known priors. Each grid location contains at most one target. At each time step, the sensor can sample a grid location, returning sample values that may or may not be thresholded. The sensor output distribution conditioned on the content of the location is known. Bayesian methods are used to recursively update the posterior probability that each location contains a target. These probabilities can then in turn be used to classify each location as either containing a target or not. At each time step, sensor management is used to determine which location to test next. For non-myopic optimization, graph search techniques are used. When the sensor output is thresholded, the performance obtained using myopic optimization of the expected error rate is worse then that obtained using our other three approaches. Interestingly, we find that for non-thresholded measurements on symmetric distributions, the performance is the same for the four cases tested (myopic/non-myopic discrimination gain/expected error rate). This supports that discrimination is a useful heuristic that provides near-optimal performance under the given assumptions.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
John W. Wegrzyn and Keith D. Kastella "Intelligent sensor management to minimize detection error", Proc. SPIE 5096, Signal Processing, Sensor Fusion, and Target Recognition XII, (25 August 2003); https://doi.org/10.1117/12.501108
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Sensors

Signal to noise ratio

Target detection

Intelligent sensors

Error analysis

Monte Carlo methods

Sensor performance

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