相关测速声呐模型参数矩阵化求解方法

相关测速声呐的基本原理是“波形不变性”。该原理指出：发射具有一定时间间隔的信号,接收回波相关性最大的两个基元之间的间距与载体移动速度成正比。相关测速算法首先通过回波数据构建数据相关矩阵,然后利用速度估计算法将理论相关模型和回波数据构建的相关矩阵进行拟合得到最优参数信息,进而解算出载体速度信息。文章以简化最优参数求解流程为目标,提出了基于矩阵推导的参数拟合运算方法。该方法通过将底混响时空相关函数模型中的目标参量进行分离,进而将函数转变为矩阵形式,随后通过矩阵QR分解等手段,根据局部最小二乘法推导目标参数的求解公式,并直接求解出目标参数的解析解。仿真和湖上试验数据验证了该方法可以作为一种速度解算方法的可行性,并且具有运算复杂度低的优点。

Parametric matrix solution method of correlation velocimetry sonar model

The basic principle of correlation velocimetry sonar is "waveform invariance", which is described as follows: when a carrier transmits signals with a certain time interval, the distance between the two elements with the greatest correlation between the received echoes is proportional to the carrier''s moving speed. The correlation velocimetry algorithm firstly constructs the data correlation matrix through the echo data, then uses the velocity estimation algorithm to fit the theoretical correlation model with the correlation matrix constructed by the echo data to get the optimal parameter information, and then calculates the carrier velocity information. In order to simplify the process of solving optimal parameters, a parameter fitting algorithm based on matrix derivation is proposed in this paper. In this method, the target parameters in the space-time correlation function model of bottom reverberation are separated, and then the function is expressed in matrix form. Finally, by matrix QR decomposition and local least squares, the analytical solution formula of the target parameters is obtained. The feasibility of this scheme as a velocity solution method is verified by simulation and on-lake test data, and it has the advantages of low computational complexity.