Intelligent Sensor Management to Minimize Detection Error

摘要

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 (myopicon-myopic discrimination gain/expected error rate). This supports that discrimination is a useful heuristic that provides near-optimal performance under the given assumptions.