异步多基地雷达分布式批估计方法研究

A Study on Distributed Batch Estimation Method for Asynchronous Multi-static Radar Systems

在实际应用中，由于初始偏差、采样速率不同等原因，系统量测一般也非同步。多基地雷达数据通常面临异步数据融合问题。为了解决该问题，该文按照批处理的思路，联合一段时间内的多个异步数据对同一目标状态进行估计，并基于最优贝叶斯估计原理，提出了一种新的批估计数据融合准则；然后，依据该准则推导出了一种解析的分布式批估计方法；最后，针对非线性非高斯场景，提出了一套完备的粒子滤波实现方案。仿真结果表明，文中提出的方法相比现有方法具有跟踪精度高，计算量小等优点。

In practical applications, due to the different initial bias and sample rates of different muhi-static radars, usually, the ts of the system is non-synchronous. Therefore, there exists an annoying asynchronous data fusion problem in multi-radar data fusion. To address this problem, in this paper, we adopt the idea of batch processing, i.e. , utilizing the multiple asynchronous data during a period to estimate the same target state, and propose a batch estimation data fusion rule based on the optimal Bayesian estimation. Then, a distributed batch estimation approach is derived based on this rule. Finally, with regard to the nonlinear and non-Gaussian scenarios, a particle filtering based implementation of distributed batch estimation is derived. Simulation results show that our method has a better performance in tracking accuracy and computation cost comparing with the existing methods.