基于UPF的神经网络辅助机动目标跟踪

为提高机动目标跟踪性能，提出了一种神经网络辅助下的滤波方法。基于Unscented卡尔曼滤波方法，Unscented粒子滤波器（UPF）能够产生较准确的建议密度分布，因此相对于其它蒙特卡洛方法，UPF能够获得对非线性目标跟踪问题更好的近似。利用目标的机动特征建立和训练神经网络，将神经网络的输出作为加速度控制参数，用于修正目标的运动模型。仿真结果表明，与扩展卡尔曼滤波相比，神经网络辅助下的UPF具有更好的跟踪性能。

Maneuvering Target Tracking Based on Unscented Particle Filter Aided by Neutral Network

A filtering method aided by neural network to improve the maneuvering target tracking performance is proposed. Based on unscented Kalman filter, the unscented particle filter （UPF） has more accurate proposal distribution and better approximation to non-linear tracking problem than other Sequential Monte-Carlo methods. The neural network is constructed and trained by the maneuvering features, and the outputs of NN are used as acceleration control parameters to correct model parameters. Simulation results show the performance of UPF aided by NN is much improved than extensive Kalman filter.