Gaussian Condensation Filter Based on Cooperative Constrained Particle Flow

Abstract

applications in scenarios such as pedestrian navigation, emergency rescue, and vehicle networks. In these conditions, the measurement models are often non-linear, and traditional Kalman and particle filters cannot provide long-time highprecision location-based services. To this end, we propose a Gaussian Condensation Filter algorithm that can achieve highaccuracy localization in a harsh environment. However, aiming at the degradation of sampling points in target tracking based on the Gaussian condensation filter, this paper proposes a Gaussian Condensation Filter algorithm based on particle flow, which transfers the sample points satisfying the prior distribution of the target state to the posterior distribution, thereby improving the practical accuracy of the target tracking algorithm. Further, to enhance the information fusion in the cooperative network, we propose a multi-target cooperative tracking algorithm to accomplish spatially constrained timing filtering of state information for improving the error correction of the target nodes on timing estimation. Numerical simulations are conducted to determine the effectiveness of our proposed algorithms. Compared with the Gaussian condensation filter, its positioning accuracy is improved to 44.6%. Compared with the Gaussian condensation algorithm based on particle flow, the practical accuracy of the Gaussian condensation filter algorithm based on cooperative constrained particle flow in multi-target tracking is improved to 58.1