Application of a Monte Carlo method for tracking maneuvering target in clutter
Mathematics and Computers in Simulation - IMACS sponsored Special issue on the second IMACS seminar on Monte Carlo methods
Distributed Detection and Data Fusion
Distributed Detection and Data Fusion
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Particle filters for maneuvering target tracking problem
Signal Processing
Tracker-aware adaptive detection: An efficient closed-form solution for the Neyman--Pearson case
Digital Signal Processing
Integration of Bayes detection with target tracking
IEEE Transactions on Signal Processing
Particle filters for state estimation of jump Markov linear systems
IEEE Transactions on Signal Processing
Tracking in a cluttered environment with probabilistic data association
Automatica (Journal of IFAC)
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In this paper, we consider a tracker-aware radar detector threshold optimization formulation for tracking maneuvering targets in clutter. The formulation results in an online method with improved transient performance. In our earlier works, the problem was considered in the context of the probabilistic data association filter (PDAF) for non-maneuvering targets. In the present study, we extend the ideas in the PDAF formulation to the multiple model (MM) filtering structures which use PDAFs as modules. Although our results are general for the MM filters, our simulation experiments apply the proposed solution in particular for the interacting multiple model PDAF (IMM-PDAF) case. It is demonstrated that the suggested formulation and the resulting optimization method exhibits notable improvement in transient performance in the form of track loss immunity. We believe the method is promising as a detector-tracker jointly-optimal filter for the IMM-PDAF structure for tracking maneuvering targets in clutter.