基于稳健最小二乘支持向量机的测深激光信号处理

针对浅海探测中激光回波噪声源多、信噪比低,传统非加权最小二乘支持向量机和加权最小二乘支持向量机对低信噪比信号滤波不足的问题,提出将稳健最小二乘法与加权最小二乘支持向量机相结合的滤波方法(HW-LS-SVM)。首先采用强淘汰权函数计算先验权值、残差和均方误差,然后采用权函数模型计算最小二乘支持向量机的权值,最后通过迭代计算实现回波信号滤波。通过仿真实验结果表明, HW-LS-SVM方法较最小二乘支持向量机、贝叶斯最小二乘支持向量机和传统加权最小二乘支持向量机滤波效果更加稳健,在噪声率为45%的情况下,滤波效果较为理想,水面和水底回波提取正确率为100%;对实测4组深水区和4组浅水区数据滤波后提取的海水深度均与背景资料的深度吻合。由此表明, HW-LS-SVM方法具有更好的抗噪性,更适合于对信噪比低的测深激光信号的滤波处理。

Laser bathymetry waveform processing based on robust least square support vector machine

The traditional nonweighted least squares Support Vector Machine (SVM) and weighted least square SVM have a few disadvantages of processing low Signal-to-Noise Ratio (SNR) laser echo in the field of lidar bathymetry, a filtering method named HW-LS-SVM was proposed by combining robust least square and weighted least square SVM. Firstly, strong prior weight function, residual error and mean square error were calculated by elimination weight function, then the weight of least square SVM was computed by weight function. Finally, the echo signal was filtered by iterative computation. The simulation results show that HW-LS-SVM algorithm is more robust than least square SVM, Bayes least square SVM and the traditional weighted least square SVM. The results were satisfactory when the noise rate reached to 45%, and the correct rate of the extracted water surface and bottom was 100%. The extracted water depths from 4 groups of laser echoes in deep area and 4 groups in shallow area all agree with the background data. The proposed method has better anti-noise performance and is more suitable for the filtering processing of the low SNR lidar bathymetry signal.