A Gaussian fitting envelope delay estimation method and system based on generalized cross-correlation

Abstract

The invention belongs to the technical field of radio orientation; radio navigation; ranging or speed measurement using radio waves; positioning or presence detection using reflection or re-radiation of radio waves; and similar devices using other waves. The method and system for fitting the envelope delay estimation, obtaining the cross-correlation function value of the signal by the generalized cross-correlation method; using the piecewise maximum method to extract the envelope points of the cross-correlation function; using the Gaussian function to fit the envelope, and using the Gaussian function to fit the envelope The function maximum point is used as the delay estimation value of the signal. The invention uses generalized cross-correlation to calculate the cross-correlation function value; adopts the method of segmental maximum value envelope extraction to extract the envelope of the cross-correlation function; uses Gaussian fitting to fit the envelope; The delay is the difference between the arrival delays of the two signals. The invention has low computational complexity and is easy to implement, and simulation experiments verify the effectiveness, universality and accuracy of time delay estimation of the algorithm.

Description

technical field [0001] The invention belongs to the technical field of radio orientation; radio navigation; ranging or speed measurement using radio waves; positioning or presence detection using reflection or re-radiation of radio waves; A fitting envelope delay estimation method and system. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: passive target detection occupies an important position in the fields of military, national defense and security, environmental monitoring, emergency rescue, military reconnaissance, etc. . Acoustic array sensor network is one of the most commonly used passive detection technologies, showing outstanding performance in automatic detection, positioning, recognition and high-speed real-time tracking, and sound waves can bypass obstacles such as trees and rocks, even in radar detection. The blind spot can also effectively search for the target. Therefore, the acoustic array s...

AI Technical Summary

Problems solved by technology

[0004] (1) Existing technology 1 requires prior knowledge when building a model. In actual scenarios, this requirement is often difficult to meet, and the performance of the model is limited by frequency band division and deviation threshold selection. This technology does not give a reasonable optimal Frequency band division and deviation threshold selection scheme

[0005] (2) Existing technology 2 uses various signal preprocessing and noise reduction techniques to improve the accuracy of time delay estimation, but the selection, decomposition, and reconstruction of high-frequency signals in this method lead to extremely high computational complexity , which puts forward higher requirements for system storage; if the system life cycle is short, this technology will not be applicable

[0006] (3) Existing technology 3 uses wavelet noise reduction technology for signal preprocessing in the noise and reverberation environment, but the noise reduction performance of this technology is restricted by the wavelet base and decomposition scale, and the parameter is not given in this technology best solution for

[0007] (4) In order to weaken the impact of noise and reverberation on the accuracy of time delay estimation, quadratic cross-correlation, Hilbert transform, spectral analysis, matched filtering and other means are used to sharpen the GCC peak and improve the accuracy, but these technologies themselves have The performance constraints are closely related to the delay estimation accuracy, such as filter order limit, reasonable spectrum analysis high-frequency signal selection strategy determination, etc. These methods will lead to extremely high computational complexity, which will cause storage and power consumption for the system. and other burdens, and even cause the entire system to crash due to unreasonable parameter settings

[0009] In view of the above technical problems, the difficulty of solving mainly focuses on how to obtain the prior knowledge of the target sound source, the determination of the optimal setting scheme of the model parameters, and the system power consumption and memory problems caused by the optimization algorithm with high computational complexity

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