A new normalized least mean square(NLMS) adaptive filter is first derived from a cost function, which incorporates the conventional one of the NLMS with a minimum-disturbance(MD)constraint. A variable regularization f...A new normalized least mean square(NLMS) adaptive filter is first derived from a cost function, which incorporates the conventional one of the NLMS with a minimum-disturbance(MD)constraint. A variable regularization factor(RF) is then employed to control the contribution made by the MD constraint in the cost function. Analysis results show that the RF can be taken as a combination of the step size and regularization parameter in the conventional NLMS. This implies that these parameters can be jointly controlled by simply tuning the RF as the proposed algorithm does. It also demonstrates that the RF can accelerate the convergence rate of the proposed algorithm and its optimal value can be obtained by minimizing the squared noise-free posteriori error. A method for automatically determining the value of the RF is also presented, which is free of any prior knowledge of the noise. While simulation results verify the analytical ones, it is also illustrated that the performance of the proposed algorithm is superior to the state-of-art ones in both the steady-state misalignment and the convergence rate. A novel algorithm is proposed to solve some problems. Simulation results show the effectiveness of the proposed algorithm.展开更多
针对传统Mean shift跟踪算法对空中运动目标跟踪效果不理想的问题,提出了基于Mean shift算法和归一化转动惯量(Normalized moment of inertia,NMI)特征的目标跟踪算法.算法中引入了目标NMI特征,建立了基于虚警概率最小原则和相似度二级...针对传统Mean shift跟踪算法对空中运动目标跟踪效果不理想的问题,提出了基于Mean shift算法和归一化转动惯量(Normalized moment of inertia,NMI)特征的目标跟踪算法.算法中引入了目标NMI特征,建立了基于虚警概率最小原则和相似度二级判决门限的跟踪策略,对目标模型进行更新.同时利用卡尔曼滤波,在目标被遮挡后进行估计预测.实验表明该算法在空中运动目标存在较大形变、被遮挡等情况下,能够进行实时、稳定跟踪.展开更多
基金supported by the National Natural Science Foundation of China(61571131 11604055)
文摘A new normalized least mean square(NLMS) adaptive filter is first derived from a cost function, which incorporates the conventional one of the NLMS with a minimum-disturbance(MD)constraint. A variable regularization factor(RF) is then employed to control the contribution made by the MD constraint in the cost function. Analysis results show that the RF can be taken as a combination of the step size and regularization parameter in the conventional NLMS. This implies that these parameters can be jointly controlled by simply tuning the RF as the proposed algorithm does. It also demonstrates that the RF can accelerate the convergence rate of the proposed algorithm and its optimal value can be obtained by minimizing the squared noise-free posteriori error. A method for automatically determining the value of the RF is also presented, which is free of any prior knowledge of the noise. While simulation results verify the analytical ones, it is also illustrated that the performance of the proposed algorithm is superior to the state-of-art ones in both the steady-state misalignment and the convergence rate. A novel algorithm is proposed to solve some problems. Simulation results show the effectiveness of the proposed algorithm.
文摘针对传统Mean shift跟踪算法对空中运动目标跟踪效果不理想的问题,提出了基于Mean shift算法和归一化转动惯量(Normalized moment of inertia,NMI)特征的目标跟踪算法.算法中引入了目标NMI特征,建立了基于虚警概率最小原则和相似度二级判决门限的跟踪策略,对目标模型进行更新.同时利用卡尔曼滤波,在目标被遮挡后进行估计预测.实验表明该算法在空中运动目标存在较大形变、被遮挡等情况下,能够进行实时、稳定跟踪.
