中文摘要：

传统的基于幅度积累的动态规划检测前跟踪（DP-TBD）算法在非高斯杂波背景下检测性能和跟踪性能都显著下降,而基于似然比积累的DP-TBD算法由于缺少杂波的先验信息而难以准确计算似然比.对此提出了一种杂波自适应DP-TBD算法.该算法首先利用杂波检验的方法,对当前观测数据的杂波分布类型进行检验,并估计相应分布的参数,进而得到该分布杂波下的对数似然比,然后利用基于似然比积累的DP-TBD算法进行检测跟踪.最后分别以威布尔（Weibull）分布和K分布杂波模型为例进行了仿真分析.仿真结果表明,本文算法的检测和跟踪性能明显优于传统的基于幅度积累的DP-TBD算法.

英文摘要：

The classical dynamic programming track-before-detect （DP-TBD） algorithms based on amplitude accumulation has a very poor performance on detection and tracking under the non-Gaussian clutter environment. However, the DP-TBD algorithm based on likelihood ratio accumulation is difficult to compute accurately the likelihood ratio due to lacking of clutter’s prior information. For this problem, this paper proposes a clutter adaptive DP-TBD algorithm. This algorithm is firstly to implement the test of clutter distribution types of current observed data by employing the method of clutter, and estimate the parameters of the relevant distribution for obtaining the log-likelihood ratio under this distribution of clutter, and next, carry on detection and tracking using the DP-TBD algorithms based on the likelihood ratio accumulation, and finally, perform on simulation analysis by taking examples of clutter models with Weibull distribution and K distribution, respectively. Simulation results show that the performances on detection and tracking of this proposed algorithm are much better than those of the DP-TBD algorithms based on amplitude accumulation.