DP-MFTD algorithm based on the conditional probability ratio accumulation model

In the environment of non-Gaussian background clutter without target signal distribution parameters, it is difficult to derive the likelihood ratio merit function of traditional multiple frame target detection algorithms. To solve this problem, a dynamic programming MFTD algorithm based on the accumulation model of conditional probability ration is proposed together with the analysis of its performance. In this thesis, problems in the traditional MFTD method have been analyzed. With the maximum of the target's state conditional PDF ratio as the optimal criteria, a recursive accumulation model is established according to this algorithm, which is then locally linearized by Taylor series expansion. And a linearized approximate function is adopted, instead of the likelihood ratio, during the recursive accumulation, so the clutter outliers can be restrained by making use of clutter's feature of distribution, the recursive accumulation equations of MFTD algorithm based on local linearization are derived, under different non-Gaussian distribution. Through simulation experiments, comparisons between the algorithm and the traditional ones are made, which proves that such an algorithm enjoys better detection and tracking performances in the non-Gaussian clutter background.