纯方位目标运动分析的自适应算法

基于Lainiotis算法的基本原理，使用贝叶斯估计理论和半马尔科夫过程的概念，利用一组并行的、且是自适应加权的卡尔曼滤波器对纯方位目标运动分析问题进行求解，对本算法与伪线性卡尔曼滤波算法的估计结果进行了比较，结果表明，这种估算方法在大的环境噪声、远距离和小目标速度等不利条件下仍具有较好的估计性能。

An Effective Adaptive Algorithm for Bearings-Only Target Motion Analysis

When bearing measurements obtained by a single moving observation platform are corrupted by noise and other unfavorable factors, estimating the position and velocity of a target becomes rather ineffective. To address this problem, we propose an adaptive algorithm, which is shown schematically in Fig.2. Our algorithm, employing the Bayesian theory and the semi-Markovian concept, divides the four-dimensional vector into two lower dimensional vectors: bias and state. We assume the bias vector as a semi-Markovian process. Using the Bayesian theory to estimate the state vector and dividing the bias area into several subareas, we develop an estimator consisting of a bank of parallel, adaptively weighted Kalman filters. For verifying the effectiveness of our algorithm, section 4 gives computer simulation results for two different ownship-target patterns (Figs.5 and 6) under unfavorable conditions. Fig.5 shows that the estimated values converge very quickly to the true values. Fig.6 shows the comparison of the simulation results obtained by our method with those obtained by the pseudolinear estimation (PLE) Kalman filter. The comparison shows that our adaptive algorithm performs quite well under unfavorable conditions of very noisy environment, large range and small target velocity.