An improved two-phase robust distributed Kalman filter

To enhance the efficacy of the distributed filter in mitigating heavy-tailed non-Gaussian noise and accommodating intricate environments, this study investigates the utilization of absolute and relative measurement information for the realization of multi-target cooperative positioning and proposes an improved robust distributed Kalman filter in this paper. The method is divided into two phases. Firstly, absolute measurement information undergoes processing via the standard Kalman filter, yielding the Kalman estimation value for the first phase, subsequently serving as the time update value for the second phase. Next, a robust Kalman filter based on Student's t distribution is used to process relative measurement information for measurement updates, resulting in more accurate estimates. Simulation results demonstrate that the proposed approach effectively approximates the posterior probability distribution, leading to enhanced stability and accuracy in positioning within environments characterized by both Gaussian and heavy-tailed non-Gaussian noise.