A consistent estimation criterion for multisensor bearings-only tracking
A consistent estimation criterion for multisensor bearings-only tracking
复制标题
DOI：
10.1109/7.481253
复制
发表时间：
1996-01-01
影响因子：
4.4
通讯作者：
Anderson, KL
中科院分区：
文献类型：
--
作者：
Iltis, RA;Anderson, KL
研究了目标个数先验未知时，利用纯方位测量进行多目标跟踪的问题。首先选择Rissanen的最小描述长度（MDL）准则作为先验分布不可用时确定目标数量的自然方法。然而，MDL会导致目标数目的不一致估计，因此提出了一种改进的估计准则，该准则能得到无偏的目标估计，所得到的渐近无偏目标识别（AUTI）算法对应于目标状态和关联的联合最大似然（ML）估计的计算，并增加了一个惩罚项以防止过度参数化.数据关联的问题，解决了一组并行模拟退火算法的传感器和扫描。由于关联是通过退火形成的，因此传统的非线性规划算法同时估计目标状态（位置和速度）。这种划分是合理的，通过检查结构的透视协会下的轴承只有跟踪问题。证明了目标状态误差向量的范数平方是梯度下降微分方程的一个李雅普诺夫函数，从而证明了理想化的非线性规划算法（具有无穷小步长的最速下降法）是全局收敛的.一个实用的算法，然后开发识别的目标数量，它结合了模拟退火的关联，和高斯-牛顿算法的目标状态估计。仿真结果比较MDL和AUTI算法的跟踪性能。
The problem of multitarget tracking using bearings-only measurements is addressed, when the number of targets Is unknown a priori. The minimum description length (MDL) criterion of Rissanen is first chosen as a natural way to determine the number of targets when a prior distribution is unavailable. However, it is shown that MDL results in inconsistent estimates of the number of targets, and hence a modified estimation criterion, which is shown to yield unbiased target estimates, is proposed.The resulting asymptotically unbiased target identification (AUTI) algorithm corresponds to the computation of joint maximum likelihood (ML) estimates of target states and associations, with an additional penalty term to prevent overparameterization. The problem of data association is solved using a set of parallel simulated annealing algorithm over the sensors and scans. As the associations are formed by annealing, a conventional nonlinear programming algorithm simultaneously estimates the target states (position and velocity). This partitioning is justified by examining the structure of the bearings-only tracking problem under clairvoyant associations. It is shown that the norm squared of the target state error vector is a Lyapunov function for a gradient descent differential equation As a consequence, an idealized nonlinear programming algorithm (steepest descent with infinitesimal step size) is globally convergent. A practical algorithm is then developed for identification of the number of targets, which combines simulated annealing for associations, and the Gauss-Newton algorithm for target state estimation. Simulation results are presented which compare the tracking performance of the MDL and AUTI algorithms.