This article studies the performance evaluation of distributed linear regression Kalman filtering fusion for nonlinear systems. Sufficient conditions are established for the convergence of the centralized fusion (CF) under the assumption of bounded estimation error covariance, and a measure of performance is derived from the convergence conditions. By the performance analysis, it can be found that the CF has a better performance than the distributed fusion with feedback, especially at the beginning of the estimation. Moreover, the performance of the local estimator can be improved by receiving the fused estimate from the fusion center, which is different from the fusion estimation in linear systems. Finally, by simulations of a target tacking example, the comparisons of the centralized fusion and the distributed fusion with and without feedback are presented to show the accuracy of the performance analysis.

中文翻译：

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分布式线性回归卡尔曼滤波融合的性能评估

本文研究了分布式线性回归卡尔曼滤波融合对非线性系统的性能评价。在有界估计误差协方差的假设下，为集中式融合（CF）的收敛建立了充分的条件，并且从收敛条件导出了性能的度量。通过性能分析可以发现，CF比带反馈的分布式融合具有更好的性能，尤其是在估计开始时。此外，局部估计器的性能可以通过从融合中心接收融合估计来提高，这与线性系统中的融合估计不同。最后，通过对目标跟踪示例的模拟，