梯度自适应格型滤波联合算法的性能分析

Performance Analysis of Gradient Adaptive Lattice Joint Processing Algorithm

针对在非平稳环境下,以最小均方(LMS)为代表的自适应梯度算法跟踪输入信号的能力不强和稳定性不够的问题,设计了一种以梯度自适应格型算法与线性组合器组成的联合处理器,并在白噪声、车载噪声、粉红噪声三种环境下的性能进行了仿真实验。结果显示该处理器在平稳和非平稳两种干扰环境下都只需要约20次迭代运算达到稳定,相对于横向自适应最小均方算法不仅减少了算法总的复杂度,而且对微弱信号的信噪比改善能力更强。研究表明该算法既满足收敛速度快、精度高、稳定性好等要求,又可以节省硬件资源。

In view of the limited tracking capability and stability of adaptive stochastic gradient filter algorithms represented by the least mean square （LMS） are under non-stationary circumstance, a joint processor which consist of the gradient lattice filter and transversal LMS filter is designed,whose performance is investigated by interfering the input signal with white noise,vehicle noise and pink noise respectively. The simulation shows that the joint processor could get stable only after 20 iterative operations,making the overall algorithmic simpler and providing stronger capability to improve the SNR of weak signal compared with transversal LMS filter. The study shows that the given algorithm meets the requirement of fast convergence, high precision and high stability with less hardware resource.