Learningbased Adaptive Estimation for AOA Target Tracking with NonGaussian White Noise In angleofarrival (AOA) target tracking, the target states are estimated using the noisy angular measurements. The nonGaussian measurement noise is common in practical applications and will decrease the estimation accuracy significantly. This paper is to reduce the negative impacts of the nonGaussian white noise (NGWN), which is modeled by multiple mixture Gaussian white noises, and guarantee the estimation accuracy. A learningbased adaptive extended Kalman filter (EKF) for AOA target tracking is developed in the NGWN environment. In the proposed method, the extreme learning machine (ELM) using a threelayer neural network is applied to identify the characters of measurement noise at each sampling time. Consequence, the EKF will consider the noise characters by the trained ELM, and make the corresponding realtime adjustments to improve the tracking performance. The effectiveness of the proposed method is verified by simulation examples.

Improved generalized labeled multiBernoulli filter for nonellipsoidal extended targets or group targets tracking based on random submatrices Abstract   For nonellipsoidal extended targets and group targets tracking, using an ellipsoid to approximate the target extension may not be accurate enough because of lacking shape and orientation information. In consideration of this, we model a nonellipsoidal extended target or target group as a combination of multiple ellipsoidal subobjects, each represented by a random matrix. Besides, in practical tracking scenes, the knowledge of detection probability is of critical importance, which, however, is prior unknown due to the fluctuation of target parameters. Thus based on the aforementioned random submatrices model, an improved generalized labeled multiBernoulli (GLMB) filter is presented for the tracking of nonellipsoidal extended targets or group targets in the case of unknown detection probability. In addition, a cheaper Gibbs sampling based joint prediction and updating implementation of the proposed GLMB filter is given, within which the extended targets or group targets are modeled using Beta gamma Gaussian inverse Wishart (BGGIW) distributions, achieving the estimation to detection probability, measurement rate, and extension state of each subobject and the kinematic state with the positions of all subobjects and unified kinematics for each extended target or target group. The simulation results demonstrate the effectiveness of the proposed filter.