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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Constrained bearings-only target motion analysis via markov chain monte carlo methods
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Constrained bearings-only target motion analysis via markov chain monte carlo methods

机译：通过Markov链蒙特卡洛方法进行仅限轴承的目标运动分析

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摘要

The aim of this paper is to develop methods for estimating the range of a moving target from bearings-only observations and for weakly observable scenarios, by including general constraints about the target trajectories. Throughout this manuscript, it is assumed that the target motion is conditionally deterministic, which leads us to focus on batch algorithms. Another common assumption is poor observability, which results from another constraint: (very) limited amplitude of the observer maneuvers. Classical batch algorithms are based on iterative methods (such as gradient techniques), but they perform very poorly in this context and including constraints is not easy and not reliable in this way. Instead, we consider simulation-based methods, i.e., Monte Carlo Markov chain (MCMC) sampling, developed in a Bayesian context. In this way, it is possible to take into account any type of constraint, and to drastically improve estimation of weakly observable parameters. As a by-product, an estimate of confidence intervals is obtained. This is the aim of the highest probability density (HPD) intervals. This study is illustrated by simulation results, while the benefits of constraint inclusion are analyzed via geometric methods
机译：本文的目的是通过包含有关目标轨迹的一般约束条件，开发出一种方法，可以从仅方位角的观测值和弱可观察的场景中估算出移动目标的范围。在整个手稿中，假设目标运动是有条件的确定性的，这使我们将重点放在批处理算法上。另一个常见的假设是可观察性差，这是由另一个约束导致的：观察者操作的幅度非常有限。经典的批处理算法基于迭代方法（例如梯度技术），但是在这种情况下它们的性能非常差，并且以这种方式包含约束并不容易且不可靠。取而代之的是，我们考虑在贝叶斯环境下开发的基于仿真的方法，即蒙特卡洛马尔可夫链（MCMC）采样。以这种方式，可以考虑任何类型的约束，并大大改善对弱可观察参数的估计。作为副产品，可获得置信区间的估计值。这是最高概率密度（HPD）间隔的目标。仿真结果说明了这项研究，同时通过几何方法分析了约束包含的好处

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