Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin 150001, China.
School of Astronautics, Harbin Institute of Technology, Harbin 150001, China.
Sensors (Basel). 2023 May 12;23(10):4687. doi: 10.3390/s23104687.
This paper is concerned with the problem of state estimation for nonlinear multi-sensor systems with cross-correlated noise and packet loss compensation. In this case, the cross-correlated noise is modeled by the synchronous correlation of the observation noise of each sensor, and the observation noise of each sensor is correlated with the process noise at the previous moment. Meanwhile, in the process of state estimation, since the measurement data may be transmitted in an unreliable network, data packet dropout will inevitably occur, leading to a reduction in estimation accuracy. To address this undesirable situation, this paper proposes a state estimation method for nonlinear multi-sensor systems with cross-correlated noise and packet dropout compensation based on a sequential fusion framework. Firstly, a prediction compensation mechanism and a strategy based on observation noise estimation are used to update the measurement data while avoiding the noise decorrelation step. Secondly, a design step for a sequential fusion state estimation filter is derived based on an innovation analysis method. Then, a numerical implementation of the sequential fusion state estimator is given based on the third-degree spherical-radial cubature rule. Finally, the univariate nonstationary growth model (UNGM) is combined with simulation to verify the effectiveness and feasibility of the proposed algorithm.
本文针对存在交叉相关噪声和数据包丢失补偿的非线性多传感器系统的状态估计问题展开研究。在这种情况下，通过各传感器观测噪声的同步相关来对交叉相关噪声进行建模，且各传感器的观测噪声与前一时刻的过程噪声相关。同时，在状态估计过程中，由于测量数据可能在不可靠的网络中传输，数据包丢失不可避免，导致估计精度降低。针对这种不良情况，本文提出了一种基于序贯融合框架的存在交叉相关噪声和数据包丢失补偿的非线性多传感器系统的状态估计方法。首先，使用预测补偿机制和基于观测噪声估计的策略来更新测量数据，同时避免噪声去相关步骤。其次，基于创新分析方法推导出序贯融合状态估计滤波器的设计步骤。然后，基于三阶球半径容积法则给出了序贯融合状态估计器的数值实现。最后，结合单变量非平稳增长模型（UNGM）进行仿真，验证了所提算法的有效性和可行性。
