Constrained Gaussian Condensation Filter for Cooperative Target Tracking

Abstract

Real-time high-precision navigation has many applications, such as pedestrian navigation, emergency rescue, and vehicle networks. In practice, the measurement models are often nonlinear, and sequential Bayesian filters, such as Kalman and particle filter, suffer from accumulative errors, which cannot provide long-time high-precision services for localization. To solve arbitrary noise distribution, this article proposes a Gaussian condensation filter (GCF) algorithm to achieve high-precision localization in a non-Gaussian noise environment. To this end, we proposed an error-ellipse resampling (EER)-based GCF (EER-GCF), which establishes error ellipses with different confidence probabilities and implements a resampling algorithm based on the sampling points’ geometrical positions. Furthermore, a cooperative EER-based GCF (CEER-GCF) is proposed to enhance information fusion in the multitarget network. This study accomplishes cooperative tracking based on spatial–temporal constraints to enhance error correction. The experimental results show that CEER-GCF can effectively eliminate the accumulative error and optimize state estimation, which outperforms state of the arts, such as unscented Kalman filter and particle filter.