一种高效的弱目标线谱检测算法

为实现弱目标线谱检测,在自适应线谱增强(Adaptive Line Enhancement,ALE)算法的基础上,结合频域批处理技术,提出了一种能降低计算量的高效线谱检测算法——归一化频域批处理最小均方(Normalized Frequency-domain Block Least Mean Square,NFBLMS)算法;所提NFBLMS算法在权值迭代过程中,步长参数不受输入信号功率的影响。理论分析和数值仿真结果表明:相比于已有的线谱检测算法,NFBLMS算法能较好地解决ALE算法实时处理运算量问题,并可获得较高的系统增益,且其步长参数具有较强的鲁棒性,能同时兼顾算法的收敛速度和稳态误差。因此NFBLMS算法更适合实际工程应用。

A highly efficient weak target line-spectrum detection algorithm

To detect underwater weak targets, we propose an efficient line spectrum detection algorithm based on adaptive line spectrum enhancement (ALE) and frequency-domain block processing. The proposed algorithm is termed as normalized frequency-domain block least mean square (NFBLMS) algorithm, which is not affected by the input signal power in the weight iteration process. Through theoretical analysis and numerical simulation, it is shown that compared with existing algorithms, NFBLMS algorithm can implement ALE in real time and obtain higher system gain, furthermore, NFBLMS algorithm is more robust to step size, thereby producing a tradeoff between the convergence speed and the steady-state error. Therefore, NFBLMS algorithm is more suitable for the engineering application.