Improved short-wave unit positioning method

Abstract

The invention provides an improved short-wave unit positioning method, and aims to provide a positioning method with the advantages of high-resolution positioning performance, small calculation amount, and high estimation precision. The improved short-wave unit positioning method is implemented by the steps of: acquiring a beam domain covariance matrix through solving an autocorrelation matrix based on beam domain output data; calculating eigenvalues and corresponding eigenvectors of the beam domain covariance matrix through decomposition, judging whether a signal source number exits, and determining signal source subspaces and noise subspaces corresponding to the signal source number, so as to obtain estimated values for automatic pairing of signal sources; then constructing a polynomialf(z) and solving roots of the polynomial f(z); solving a root high resolution spectrum, solving roots (on an approaching unit circle) of which the number is the same as that of dimension numbers of the signal subspaces, combining with array element data, beam number and corresponding physical parameters, performing external radiation signal source direction-of-arrival estimation, acquiring eigenvectors of the signal source number, performing deep learning, and adjusting positioning precision of a short-wave single station automatically.

Description

technical field [0001] The present invention relates to a method for improving short-wave single-station positioning performance, in particular to the improvement of direction-of-arrival estimation that is widely used in fields such as radar, communication, sonar, navigation, earthquake, biomedical engineering, and radio astronomy to adapt to complex electromagnetic environments The method realizes the detection, direction finding and positioning of the target. Background technique [0002] In modern short-wave communication monitoring, the "triangulation method" of two-station positioning is usually used when locating long-distance communication signals. Position the station under test by "triangulation". According to the triangulation method, at least two direction-finding machines located at different locations are required to locate a station under test, and the two direction-finding machines and the station under test must not be in a straight line. In the short wave ...

AI Technical Summary

Problems solved by technology

These classical algorithms are based on the linear prediction model, but the linear prediction theory cannot effectively use the statistical characteristics of additive noise, resulting in relatively poor resolution performance.

In addition, nonlinear estimation methods must meet certain preconditions to be established. For example, the signal source in the set space must be continuously distributed, and the signal must satisfy stationary random characteristics. However, in actual detection, this premise is not established. Therefore, these classic Algorithms have certain limitations in practical applications

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