In the bearings-only tracking context, the source state is only partially observed through nonlinear measurements which are the estimated bearings. For a manoeuvring Markovian source, the source trajectory is estimated by means of classical dynamic programming. However, the quality of the estimation is strongly dependent of the observer trajectory, thus mixing estimation and control. But, in this context, the separation principle (for estimation and control) does not hold. In fact, the problem consists in controlling a partially observable Markov decision process. Application of this framework to search theory is mentioned. However, even if the problem presents strong similarities with an approach used in the optimisation of the search effort for a (Markovian) moving source, it is focused on the estimation of the whole source trajectory instead of its detection at the end of the scenario. To this intrinsic difficulty, the observation is richer. Consequently also, the optimisation problem presents important difficulties, i.e. memory and computation requirements. Thus the authors aim to develop a feasible framework, based on the Smallwood and Sondik (1973) approach, capable of handling real problems. To attain this objective, a specific algorithm is developed and the dimension of the bearings-only tracking is drastically reduced. The applicability of the approach is demonstrated on realistic sonar scenarios.
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机译:在仅轴承的跟踪环境中,仅通过非线性测量(即估计的轴承)部分观察源状态。对于机动马尔可夫源,源轨迹是通过经典动态规划估算的。但是,估计的质量在很大程度上取决于观察者的轨迹,因此混合了估计和控制。但是,在这种情况下,分离原理(用于估计和控制)不成立。实际上,问题在于控制部分可观察的马尔可夫决策过程。提到了该框架在搜索理论中的应用。但是,即使该问题与优化(Markovian)移动源的搜索工作量中使用的方法存在很大相似性,它也将重点放在估计整个源轨迹上,而不是在场景结束时进行检测。对于这个固有的困难,观察更加丰富。因此,优化问题也提出了重要的困难,即存储器和计算要求。因此,作者旨在基于Smallwood and Sondik(1973)方法开发一种可行的框架,能够处理实际问题。为了达到这个目的,开发了一种特定的算法,并且大大减少了仅轴承跟踪的尺寸。在现实的声纳场景中证明了该方法的适用性。
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