Minimal submodel-set algorithm for maneuvering target tracking

The variable structure multiple model (VSMM) approach to the maneuvering target tracking problem is considered. A new VSMM design, the minimal submodel-set switching (MSMSS) algorithm for tracking a maneuvering target is presented. The MSMSS algorithm adaptively determines the minimal set of models from the total model set and uses this to perform multiple models (MM) estimation. In addition, an iterative MSMSS algorithm with improved maneuver detection and termination properties is developed. Simulations results demonstrate that, compared with a standard interacting MM (IMM), the proposed algorithms require significantly lower computation while maintaining similar tracking performance. Alternatively, for a computational load similar to IMM, the new algorithms display significantly improved performance.     

Joint sensor registration and track-to-track fusion for distributed trackers

Sensor registration deals with the correction of registration errors and is an inherent problem in all multisensor tracking systems. Traditionally, it is viewed as a least squares or a maximum likelihood problem independent of the fusion problem. We formulate it as a Bayesian estimation problem where sensor registration and track-to-track fusion are treated as joint problems and provide solutions in cases 1) when sensor outputs (i.e., raw data) are available, and 2) when tracker outputs (i.e., tracks) are available. The solution to the latter problem is of particular significance in practical systems as band limited communication links render the transmission of raw data impractical and most of the practical fusion systems have to depend on tracker outputs rather than sensor outputs for fusion. We then show that, under linear Gaussian assumptions, the Bayesian approach leads to a registration solution based on equivalent measurements generated by geographically separated radar trackers. In addition, we show that equivalent measurements are a very effective way of handling sensor registration problem in clutter. Simulation results show that the proposed algorithm adequately estimates the biases, and the resulting central-level trucks are free of registration errors.     

IMM estimator versus optimal estimator for hybrid systems

The special feature of the interacting multiple model (TMM) estimator that distinguishes it from other suboptimal multiple model (MM) estimators is the "mixing/interaction" between its "mode-matched" base state filtering modules at the beginning of each cycle. This note shows that the same feature is exactly what it has in common with the optimal estimator for hybrid (MM) systems and this can be seen as the main reason for its success.     

Manoeuvring target tracking in clutter using particle filters

A particle filter (PF) is a recursive numerical technique which uses random sampling to approximate the optimal solution to target tracking problems involving nonlinearities and/or non-Gaussianity. A set of particle filtering methods for tracking and manoeuvering target in clutter from angle-only measurements is presented and evaluated. The aim is to compare PFs to a well-established tracking algorithm, the IMM-PDA-EKF (interacting multiple model, probabilistic data association, extended Kalman filter), and to provide an insight into which aspects of PF design are of most importance under given conditions. Monte Carlo simulations show that the use of a resampling scheme which produces particles with distinct values offers significant improvements under almost all conditions. Interestingly, under all conditions considered here,using this resampling scheme with blind particle proposals is shown to be superior, in the sense of providing improved performance for a fixed computational expense, to measurement-directed particle proposals with the same resampling scheme. This occurs even under conditions favourable to the use of measurement-directed proposals. The IMM-PDA-EKF performs poorly compared with the PFs for large clutter densities but is more effective when the measurements are precise.