一种改进的时变转移概率AIMM跟踪算法

An improved AIMM tracking algorithm based on adaptive transition probability

为了解决传统的交互式多模型目标跟踪算法中马尔可夫概率转移矩阵固定不变,造成的模型切换缓慢、跟踪精度不高的问题,提出了一种基于后验信息修正的时变转移概率自适应交互式多模型跟踪算法。算法定义了一种新的修正因子,利用后验信息对概率转移矩阵进行实时修正,提高匹配模型的概率,减小非匹配模型的影响,使得系统模型能够及时、准确地切换到匹配模型。蒙特卡洛仿真实验表明,该自适应交互式多模型算法能够应用于水下目标跟踪中,相比传统交互式多模型算法,模型匹配度更高,滤波效果也更好。

In order to solve the problem that the Markov probability transition matrix is constant in the traditional interactive multiple model(IMM)target tracking algorithm,resulting in the slow model switching speed and low tracking accuracy,an improved adaptive IMM(AIMM)tracking algorithm,based on adaptive transition probability,is proposed.The proposed algorithm introduces a new coefficient,which uses the posterior information to modify the probability transition matrix in real time,improve the probability of the matching model,and reduce the influence of the non-matching model,so that the system model can switch to the matching model in time and accurately.Monte Carlo simulation experiments show that the proposed algorithm has better model matching performance and better target tracking performance than the traditional IMM algorithm in underwater target tracking.