Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA.
Neural Netw. 2012 Aug;32:349-57. doi: 10.1016/j.neunet.2011.12.006. Epub 2011 Dec 29.
In this paper, a novel extended kernel recursive least squares algorithm is proposed combining the kernel recursive least squares algorithm and the Kalman filter or its extensions to estimate or predict signals. Unlike the extended kernel recursive least squares (Ex-KRLS) algorithm proposed by Liu, the state model of our algorithm is still constructed in the original state space and the hidden state is estimated using the Kalman filter. The measurement model used in hidden state estimation is learned by the kernel recursive least squares algorithm (KRLS) in reproducing kernel Hilbert space (RKHS). The novel algorithm has more flexible state and noise models. We apply this algorithm to vehicle tracking and the nonlinear Rayleigh fading channel tracking, and compare the tracking performances with other existing algorithms.
本文提出了一种新的扩展核递归最小二乘算法，将核递归最小二乘算法与卡尔曼滤波器或其扩展相结合，用于估计或预测信号。与刘提出的扩展核递归最小二乘（Ex-KRLS）算法不同，我们的算法的状态模型仍然构建在原始状态空间中，并且使用卡尔曼滤波器估计隐藏状态。隐藏状态估计中使用的测量模型是通过核递归最小二乘算法（KRLS）在再生核希尔伯特空间（RKHS）中学习的。新算法具有更灵活的状态和噪声模型。我们将该算法应用于车辆跟踪和非线性瑞利衰落信道跟踪，并与其他现有算法的跟踪性能进行了比较。
