Multi-signal detection and parameter estimation fusion with an improved utility function.

Joint detection and parameter estimation problems arise in a wide range of practical applications. For the multi-signal case, one needs to not only determine the presence of signals or not, but also to estimate the number of signals and the corresponding parameters. In this paper, we focus on modeling and solving the multi-signal joint detection and estimation fusion problem. Firstly, we present an extension of the original multi-signal joint detection and estimation model to the multi-sensor fusion scenarios. Secondly, an improved utility function is developed based on the optimal assignment, avoiding the possible many-to-one mapping when evaluating the difference between the true and estimated parameters. Moreover, the multi-sensor measurement likelihood function under clutter environments is evaluated by the Total Probability Formula, and the weighting probabilities are computed as done in the Joint Probability Data Association (JPDA) algorithm. To obtain the practical decision rule when given the constraint on the probability of false-positive (FP) events, the original constrained optimization model is solved efficiently by utilizing a series of model transformations. Simulation results demonstrate the efficiency of the proposed algorithm in the presence of an unknown number of signals, when compared with other competing algorithms. [ABSTRACT FROM AUTHOR]