The problem of tracking targets in clutter narurally leads to a Gaussian mixture representation of the probability densityfunction of the target state vectoI: Srateofthe-art Multiple Hypothesis Tracking (MHT) techniques maintain the mean, covariance and probability weight corresponding to each hypothesis, yet they rely on ad hoc merging and pruning rules to control the growth of hypotheses. This paper proposes a structured cost-functionbased appmach to the hypothesis control problem, utilizing rhe newly defined Integral Square Difference (ISD) cost measure. The performance of the ISD-based algorithm for tracking o single target in heavy clutter is compared to that of Salmond's joining flteer; which previously had provided the highest performance in the scenario examined. For a larger number of mirture components; it is shown that the ISD algorithm outperforms the joining filter remarkably, yielding an average track life more than double that achievable using the joiningfilter: Furrhermore, it appears that the performance of the algorithm will continue to grow exponentially as the number of mixture components is increased, hence the performance achievable is limited only by the computational resources available.

No abstract

We propose a modified multiple model adaptive estimation (MMAE) algorithm that uses the time correlation of the Kalman filter residuals, in place of their scaled magnitude, to assign conditional probabilities for each of the modeled hypotheses. This modified algorithm, denoted the residual correlation Kalman filter bank (RCKFB), uses the magnitude of an estimate of the correlation of the residual with a slightly modified version of the usual MMAE hypothesis testing algorithm to assign the conditional probabilities to the various hypotheses that are modeled in the Kalman filter bank within the MMAE. This concept is used to detect flight control actuator failures, where the existence of a single frequency sinusoid (which is highly time correlated) in the residual of an elemental filter within an MMAE is indicative of that filter having the wrong actuator failure status hypothesis. This technique results in a delay in detecting the flight control actuator failure because several samples of the residual must be collected before the residual correlation can be estimated. However, it allows a significant reduction of the amplitude of the required system inputs for exciting the various system modes to enhance identifiability, to the point where they may possibly be subliminal, so as not to be objectionable to the pilot and passengers.

No abstract