Multipath time delay estimation method and device based on compressed sensing in dense environment

Abstract

The invention relates to a multipath time delay estimation method and a device based on compressed sensing in a dense environment, and the method comprises the steps: S1, setting a reference signal, and preprocessing a received signal and the reference signal to a set length; S2, performing cyclic correlation processing on the received signal and the reference signal to obtain a cyclic correlationfunction; S3, expressing the obtained cyclic correlation function as an inverse Fourier transform form as a parameter signal; S4, generating a parameter matrix according to the parameter signals; S5,adopting a measurement matrix, constructing a sparse base dictionary, and performing compressed sampling on the time delay parameters by using a compressed sensing algorithm; and S6, carrying out matching tracking on the observation signal by adopting an orthogonal matching algorithm to obtain a multipath time delay parameter. Compared with the prior art, the method has the advantages of improving the resolution capability of time delay signal estimation in the dense environment and the like.

Description

technical field [0001] The present invention relates to the technical field of signal processing, in particular to a multipath delay estimation method and device based on compressed sensing in a dense environment. Background technique [0002] The wireless positioning system uses the principle of wireless signal propagation between the transmitter and receiver. The positioning process uses various characteristics of wireless propagation, the most common ones are positioning through signal strength, signal arrival angle and signal arrival delay. The estimation of multipath delay parameters is a popular research topic in the field of signal processing nowadays, and it is widely used in communication, radar, sonar, geological exploration and other fields. The classic time delay estimation technology is based on correlation algorithms and has high robustness and low computation, but their resolution is limited by the signal bandwidth. Classical algorithms are only effective if ...

AI Technical Summary

Problems solved by technology

However, these cost function-based estimation algorithms involve a lot of parameter optimization problems, which require a large amount of calculation, are difficult to implement, and cannot meet the requirement of real-time performance.

With the gradual deepening of research on multipath time delay estimation, the idea of ​​array signals is applied to time delay estimation, and a multiple signal classification algorithm based on eigenvalue decomposition is established. Based on this, the cross-correlation and eigensubspace decomposition methods In combination, it is introduced into the field of time delay estimation in the frequency domain, thereby studying the time delay estimation of the MUSIC algorithm based on the frequency domain, but its performance is often only good for signals with broadband and approximately flat spectrum, and the estimation performance for narrowband signals Poor, and in the case of short observation time (few snapshots), because the obtained signal energy is less, the time delay information of dense signals cannot be accurately estimated, so its application range is limited

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