Gaussian Sum FIR Filtering for 2D Target Tracking

Abstract

The constant velocity (CV) motion model has been typically used in 2-dimensional (2D) target tracking problems, but it has an uncertain process noise covariance problem. Unlike the Kalman filter (KF), the least square finite impulse response filter (LSFF) does not require noise covariance information and can overcome the uncertain process noise covariance problem. However, the LSFF has a cumbersome problem that is to select a suitable value of design parameter called the horizon size. This paper proposes a Gaussian sum FIR filter (GSFF), where the Gaussian sum method is used to deal with the horizon size in LSFFs. The GSFF overcomes the uncertain process noise covariance problem and can be alternative to existing filters in 2D target tracking. Superior performance of GSFF is demonstrated by comparison with the Gaussian sum KF (GSKF) that is an existing filter to solve the uncertain process noise covariance problem.