In a complex environment, a multi-sensor fusion algorithm can compensate for the limitations of a single sensor’s performance. In a distributed fusion algorithm, sensors need to transmit local estimates to a central coordinate system, and the existence of coordinate transformation uncertainty can undermine the performance of data transmission. Therefore, this paper proposes a multi-sensor distributed fusion method based on trustworthiness. Firstly, considering the presence of non-Gaussian conversion errors, a credibility-based multi-sensor fusion framework is constructed. Secondly, to address the difficulty in estimating conversion errors when measurement errors follow a non-Gaussian distribution, an optimization model is constructed based on actual measurement information to estimate the distribution of non-Gaussian conversion errors. Then, in response to the non-linear and non-Gaussian characteristics of the target optimization function, a particle swarm optimization algorithm based on trustworthiness adaptive weights is proposed to estimate the coordinate transformation errors. Finally, given the inconsistency in local estimates due to missing sensor measurements or significant errors in a non-Gaussian complex environment, a maximum correntropy consensus algorithm is proposed to avoid the trustworthiness calculation being affected by the current measurement errors, thereby improving the accuracy of the global estimation.

中文翻译：

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基于可信度的多传感器融合，用于减少非高斯转换误差


在复杂的环境中，多传感器融合算法可以补偿单个传感器性能的限制。在分布式融合算法中，传感器需要将局部估计值传输到中央坐标系，而坐标变换不确定性的存在会破坏数据传输的性能。因此，本文提出了一种基于可信度的多传感器分布式融合方法。首先，考虑到存在非高斯转换误差，构建了基于可信度的多传感器融合框架;其次，针对测量误差服从非高斯分布时转换误差估计困难的问题，基于实际测量信息构建优化模型来估计非高斯转换误差的分布。然后，针对目标优化函数的非线性和非高斯特性，提出一种基于可信自适应权重的粒子群优化算法来估计坐标变换误差。最后，针对在非高斯复杂环境中由于传感器测量值缺失或显著误差而导致的局部估计不一致的问题，该文提出一种最大相关熵一致性算法，以避免可信度计算受当前测量误差的影响，从而提高全局估计的准确性。