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In this paper the problem of tracking multiple spawning targets with multiple finite-resolution sensors is considered and a new algorithm for measurement-to-track association with possibly unresolved measurements is presented. The goal is to initialize new tracks of spawned targets before they are resolved from the mother platform so that one has the ability to carry out early discrimination when they become resolved. The multiple scan data association problem is first formulated as a multidimensional assignment problem with explicit new constraints for the unresolved measurements. Then the top M hypotheses tracking (TMHT) is presented where the state estimates and their covariances are modified based on the M best hypotheses through the assignment solutions. A modification to the assignment problem is developed that leads to a linear programming (LP) where the optimal solution can be a noninteger in [0, 1]. The fractional optimal solution is interpreted as (pseudo) probabilities over the N - 1 frame sliding window. The best hard (binary) decision assignment solution and the M best via TMHT are compared with the soft decision solution for 2-D tracking scenarios with various sensor configurations. Based on the simulation results, the soft assignment approach has better track maintenance capability than the single best hard assignment and a performance nearly as good as the TMHT. Its computational load is slightly higher than the single best hard assignment but much lighter than TMHT.

Prognostics, which refers to the inference of an expected time to failure for a system, is made difficult by the need to track and predict the trajectories of real-valued system parameters over essentially unbounded domains and by the need to prescribe a subset of these domains in which an alarm should be raised. In this paper, we propose an idea, one whereby these problems are avoided: Instead of physical system or sensor parameters, a vector corresponding to the failure probabilities of the system's sensors (which of course are bounded within the unit hypercube) is tracked. With the help of a system diagnosis model, the corresponding fault signatures can be identified as terminal states for these probability vectors. To perform tracking, Kalman filters and interacting multiple-model estimators are implemented for each sensor. The work that has been completed thus far shows promising results in both large-scale and small-scale systems, with the impending failures being detected quickly and the prediction of the time until this failure occurs being determined accurately. Index Terms--Condition monitoring, fault detection, interacting multiple model (IMM), Kalman filter, prognostics, tracking.

A novel approach to implement the robust minimum variance distortionless response (MVDR) beamformer is presented. Simulation results have validated excellent robustness and performance of the proposed Kalman filter-based robust beamformers in stationary and nonstationary environments.

Track-to-track fusion is an important part in distributed multisensor-multitarget tracking. The centralized and distributed tracking configurations were studied in [7] using simulated air-to-air scenarios, and in [5] with analytical results based on [alpha]-[beta] filters. The current work generalizes the results in [5] to the cases with more than 2 sensors. As the number of sensors increases, the performance of the distributed tracker is shown to degrade compared with the centralized estimation even when the optimal track-to-track fusion is used. An approximate track-to-track fusion is presented and compared with the optimal track-to-track fusion with performance curves for various numbers of sensors. These performance curves can be used in designing a fusion system where certain trade-offs need to be considered. Finally, these results are compared with simulation results for a realistic air-to-air encounter scenario.