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Implementation of a Novel Algorithm on Acoustic Doppler Current Profiler Signal Processing System

机译:新型算法在声学多普勒电流轮廓仪信号处理系统中的实现

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摘要

Acoustic Doppler current profiler (ADCP) uses acoustic energy directed along narrow beams for current measurement. In conventional method, the quantity of sampling affects the precision of fast Fourier transform (FFT) algorithm, and the algorithm needs a large amount of data to process. A novel frequency estimator, enhanced least mean square ( ELMS) algorithm for a single complex sinusoid in complex white Gaussian noise, is proposed in ADCP system. As sampling frequency equals 120 krad/s and the sampling number equals 240. the minimum resolving is 0. 5 krad/ s. All variances keep 11,11 percent. ELMS algorithm needs less data than FFT. And the robust algorithm can estimate the spectrum true value to 99. 9 percent when the signal to noise ratio (SNR) is equal to 0 dB. Experiments prove that the estimation values will diverge much from the ideal when SNR is less than -6 dB.
机译:声学多普勒电流剖面仪(ADCP)使用沿窄光束定向的声能进行电流测量。在传统方法中,采样数量会影响快速傅立叶变换(FFT)算法的精度,并且该算法需要处理大量数据。在ADCP系统中,提出了一种新颖的频率估计器,即针对复杂白高斯噪声中的单个复杂正弦波的增强最小均方(ELMS)算法。由于采样频率等于120 krad / s,采样数等于240,因此最小分辨率为0. 5 krad / s。所有差异均保持11,11%。 ELMS算法比FFT需要更少的数据。当信噪比(SNR)等于0 dB时,鲁棒算法可以将频谱真实值估计为99. 9%。实验证明,当SNR小于-6 dB时,估计值与理想值会有很大差异。

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