LSL自适应权向量法检测弱脉冲信号方法研究

自适应权向量ＬＭＳ算法用于检测弱信号具有收敛响应较慢的缺点，本文应用自适应权向量格型ＬＳＬ算法检测噪声中的射频弱脉冲信号取得满意结果。仿真检验表明，本文方法用于检测快变弱信号是一种行之有效的方法。

On Making Adaptive Weighting Vector Method Effective for Detection of Weak Radio-Frequency Pulse Signals

Adaptive Weighting Vector Method(AWVM), proposed and improved by staff members at Northwestern Polytechnical University since 1994, still failed to give good results when applied to detecting weak radio-frequency (RF) pulse signals buried in strong background noise. We now propose making AWVM effective in this case by introducing Least Squares Lattice(LSL) algorithm into AWVM as shown schematically in Fig.3. Then we derived eqs.(15),(16), and (17) needed for detecting weak RF pulse signals. We denote our improved AWVM as LSL and denote the earlier AWVM as LMS. Simulation results are given in Fig.5. Fig5(a) shows the weak RF pulse signal; Fig.5(b) shows the weak RF signal buried in background noise; Fig.5(d) shows the weak RF detected with LSL when signaltonoise ratio (SNR) is -10 dB; Fig.5(f) shows the weak RF signal detected with LMS when SNR is -10 dB. Comparison shows that LSL is much more effective than LMS.