MMF-GSTIW-PMBM Adaptive Filter for Multiple Group Target Tracking With Heavy-Tailed Noise
Xirui Xue, Shucai Huang, Daozhi Wei
Abstract
High-resolution sensors can monitor and track multiple group targets and provide information support for battlefield situation assessment. However, the tracking accuracy of sensors is significantly impacted by the heavy-tailed noise with unknown characteristics. To solve the multigroup target tracking issue when the statistical characteristics of the noise are unknown, we propose a novel Poisson multi-Bernoulli mixture (PMBM) filter with nested multivariate myriad filter (MMF), which is based on the gamma Student’s <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${t}$ </tex-math></inline-formula> inverse Wishart (GSTIW) mixture distribution of the state variables and is called the MMF-GSTIW-PMBM adaptive filter. First, we model the noise and group target extended states using the Student’s <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${t}$ </tex-math></inline-formula> -distribution and the inverse Wishart (IW) distribution, respectively, and build a group target tracking model based on finite set statistics (FISST) and PMBM filter. Second, the MMF is embedded into the PMBM filter to estimate the characteristics of the innovation distribution and adaptively adjust the noise’s freedom and scale matrix parameters. Finally, to further enhance the adaptive estimation capability of the MMF, the multiwindow weighted fusion technique is employed to optimize the selection of sampling windows. Simulation experiments show that the proposed filter is capable of adaptively estimating the noise characteristic parameters and accurately tracking multiple group targets. It has higher estimation accuracy and is more robust to noise than the gamma Gaussian inverse Wishart (GGIW)-PMBM filter and GSTIW-PMBM filter.