摘要
针对传统多帧目标检测算法的似然比指标函数在目标信号分布参数未知的非高斯噪声环境下难以计算的问题,以目标状态的条件概率之比最大为最优准则建立了递归积累模型,并通过Taylor级数展开对递归积累方程进行局部线性化处理,建立了基于条件概率比积累模型的动态规划多帧检测新算法。通过仿真实验,验证了该算法在非高斯背景下的检测及跟踪性能的优越性。
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 tar- get 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. Problems in the traditional MFTD method are analyzed. With the maximum of the tar- getrs 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 linea- rized approximate function is adopted instead of the likelihood ratio, during the recursive accumula- tion, so the clutter outliers can be restrained by making use of clutter^s feature of distribution, and the recursive accumulation equations of MFTD algorithm based on local linearization are derived under different non-Gaussian distribution. Through simulation experiments, comparisons between the algo- rithm and the traditional ones are made, which proves that such an algorithm has better detection and tracking performances in the non-Gaussian clutter background.
出处
《海军工程大学学报》
CAS
北大核心
2017年第3期11-17,共7页
Journal of Naval University of Engineering
基金
国家自然科学基金资助项目(61671479)
关键词
非高斯噪声
动态规划
目标检测
多帧检测
红外预警卫星
non-Gaussian clutter
dynamic programming
target detection
multi-frame detection
IR early warning satellite
