Sensor Management for Multitarget tracking with Less Computational Effort using the Probability Hypothesis Density Filter
Pages 3454 - 3461
Abstract
The performance of tracking algorithms that use bearing measurements is dependent on the sensor-target geometry. As a result, a sensor can maneuver to maximize the accuracy of estimates. The Rényi divergence has been shown to be an effective metric for selecting guidance commands that maximize information gain, thereby increasing the accuracy of estimates. However, there is no closed form solution to the Rényi divergence in applications with nonlinear measurements. As a result, the Rényi divergence is computed for a set of discretized guidance commands. Computing the Rényi divergence for each admissible command is computationally expensive, and there is no clear guidance on how many or which discrete guidance commands should be evaluated. We address multitarget tracking scenarios where a mobile sensor estimates the trajectory of one and three targets using the probability hypothesis density (PHD) filter. We show that for a PHD filter implementation the Rényi divergence need be computed only for guidance commands (heading) in a specific closed subset. The results from our analysis greatly reduce computational time, as commands outside of the subset need not be examined.
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Published: 14 December 2021
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