The probability of detection can be thought of as the probability that a given measurement scan contains a valid, target-oriented return. In most systems, the detection probability is inherently a function of the state, which can make forming closed-form filtering solutions exceedingly difficult. Oftentimes, closed-form filters will either neglect the probability of detection outright, or in some cases may approximate it as state-independent. Both assumptions simplify calculations, yet bar the filter from extracting any state-information from the probability of detection. This work seeks to reevaluate current estimation practices by testing and comparing several probability of detection models of varying fidelity. This is done by proposing a filter update with intrinsic fault resistance capable of processing multiple sensor returns contained within a single measurement scan. Three different methods of detection probability modeling are described, which are subsequently used to form three distinct Gaussian mixture filters. To test the filters, Monte Carlo results are taken from two different simulations: the first a simple falling body tracking scenario, and the second a more complex orbit determination scenario. The results from both simulations indicate that including detection probabilities, even when modeled incorrectly, can increase filter robustness, and will improve estimate uncertainty if modeled as state-dependent.
Intrinsic Fault Resistance for Nonlinear Filters with State-Dependent Probability of Detection
J Astronaut Sci
The Journal of the Astronautical Sciences ; 69 , 6 ; 1821-1854
2022-12-01
34 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch