2009 Special Issue: Language and cognition
Neural Networks
Brief paper: Bearings only single-sensor target tracking using Gaussian mixtures
Automatica (Journal of IFAC)
Spatio-temporal target-measure association using an adaptive geometrical approach
Pattern Recognition Letters
Brief paper: A detection-estimation scheme for state estimation in switching environments
Automatica (Journal of IFAC)
Brief paper: Bayesian adaptive filter for tracking with measurements of uncertain origin
Automatica (Journal of IFAC)
Brief paper: Consistency and robustness of PDAF for target tracking in cluttered environments
Automatica (Journal of IFAC)
Cost-function-based hypothesis control techniques for multiple hypothesis tracking
Mathematical and Computer Modelling: An International Journal
Advanced sensing issues for UAS collision avoidance
Proceedings of the 2nd International Conference on Application and Theory of Automation in Command and Control Systems
Multiple hypothesis tracking for data association in vehicular networks
Information Fusion
Hi-index | 754.84 |
When tracking targets in dense environments, sensor reports originating from sources other than the target being tracked (i.e., from clutter, thermal false alarms, other targets) are occasionally incorrectly used in track updating. As a result tracking performance degrades, and the error covariance matrix calculated on-line by the usual types of tracking filters becomes extremely unreliable for estimating actual accuracies. This paper makes three contributions in this area. First, a new tracking filter is developed that incorporates, in an a posteriori statistical fashion, all data available from sensor reports located in the vicinity of the track, and that provides both optimal performance and reliable estimates of this performance when operating in dense environments. The optimality of and the performance equations for this filter are verified by analytical and simulation results. Second, several computationally efficient classes of suboptimal tracking filters based on the optimal filter developed in this paper and on an optimal filter of another class that appeared previously in the literature are developed. Third, using an extensive Monte Carlo simulation, the various optimal and suboptimal filters as well as the Kalman filter are compared, with regard to the differences between the on-line calculated and experimental covariances of each filter, and with regard to relative accuracies, computational requirements, and numbers of divergences or lost tracks each produces.