GNSS signal capturing method based on artificial neural network

An artificial neural network and signal capture technology, applied in neural learning methods, biological neural network models, satellite radio beacon positioning systems, etc., can solve the burden of aggravating the dynamic adjustment of the tracking loop, affecting receiver performance, increasing capture time, etc. Problems, achieve fast and high-precision GNSS signal capture, improve capture accuracy and precision, and shorten search time

Active Publication Date: 2022-03-25
SHANDONG UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

When the step size of the carrier frequency search is set smaller, the frequency error is smaller, but the amount of related calculations will increase, resulting in longer capture time and affecting receiver performance; when the step size of the carrier frequency search is set larger, the frequency error will be smaller. Larger, the weaker the signal component output by the correlator, which will increase the false alarm rate of signal detection and reduce the sensitivity of signal acquisition, and when the estimation accuracy of carrier frequency is too high, it will increase the burden of dynamic adjustment of the tracking loop and increase the navigation accuracy. The data demodulation time will affect the performance of the receiver. In severe cases, the tracking loop will not be able to pull the signal to the locked state, resulting in errors in demodulation and navigation data.
[0004] In the implementation of the traditional acquisition algorithm, in order to meet the acquisition accuracy problem, after the frequency domain search is completed, the search step size is reduced in a small range and the search is repeated several times. The estimation accuracy of the carrier frequency is tens of hertz. Acquisition time is increased, and it is difficult to obtain high-precision carrier frequency

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  • GNSS signal capturing method based on artificial neural network
  • GNSS signal capturing method based on artificial neural network
  • GNSS signal capturing method based on artificial neural network

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Experimental program
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Effect test

Embodiment 1

[0060] A kind of GNSS signal acquisition method based on artificial neural network, such as figure 2 As shown, since the correlation peaks of different frequency search units on the same code band are the sampling points of the |sinc| function curve, based on the artificial neural network function, the maximum correlation peak and the maximum The relevant peak data is used as a data set to train the multi-layer perceptron neural network to obtain the optimal neural network structure and parameter values. Based on the obtained neural network structure and parameters, high-precision carrier frequency prediction is performed. The specific implementation steps include:

[0061] Step A: Get the dataset, including:

[0062] Step 1: Find the correlation peak value in the time domain;

[0063] The correlation peak in the time domain refers to: two sequences x(n) and y(n) are correlated in the time domain, which is equivalent to their Fourier transform X(k) and Y * (k)(Y * (k) is...

Embodiment 2

[0075] According to a kind of GNSS signal acquisition method based on artificial neural network described in embodiment 1, its difference is:

[0076] Such as figure 1 As shown, the specific implementation steps of step 1 include:

[0077] Step 1.1: Mix the received digital intermediate frequency signal with the replicated sine carrier and replicated cosine carrier signals of a certain frequency respectively to obtain a baseband complex signal;

[0078] Step 1.2: performing Fourier transform on the baseband complex signal obtained in step 1.1;

[0079] Step 1.3: Perform Fourier transform on the local copied pseudo-code, and multiply the conjugate value after Fourier transform with the result obtained in step 1.2;

[0080] Step 1.4: Perform inverse Fourier transform on the result obtained in step 1.3;

[0081] Step 1.5: Perform a modular square on the result obtained in step 1.4 to obtain the correlation peak value in the time domain.

[0082] In step 2, the average correla...

Embodiment 3

[0100] According to a kind of GNSS signal acquisition method based on artificial neural network described in embodiment 1 or 2, its difference is:

[0101] The digital intermediate frequency signal output by the radio frequency front end of the GNSS receiver is processed. In the signal acquisition phase, the GNSS receiver searches all PRNs in turn, saves the maximum value of the correlation peak value of the search frequency point, and generates the frequency index value of the X axis and the value of each PRN. A two-dimensional array data set A in which the correlation peak value corresponding to each frequency index value is the Y axis s .

[0102] Taking the application of Beidou signal in digital intermediate frequency signal processing as an example, m is [1, 41], f MP 10KHz, f IF 0.098MHz, f bin 500Hz, t is 1023, t bin is 1, f MC 100Hz, f cmin is 100Hz, o is [1, 3], and PRN_N is [1, 37]. Including the following steps:

[0103] Step 1: Set the satellite number PRN...

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Abstract

The invention relates to a GNSS signal capturing method based on an artificial neural network, which comprises the following steps of: training a multilayer perceptron neural network by taking a maximum correlation peak value generated during GNSS signal capturing and maximum correlation peak value data of frequency points nearby the maximum correlation peak value as a data set to obtain an optimal neural network structure and parameter values, and acquiring a GNSS signal according to the obtained neural network structure and parameter values. And high-precision carrier frequency prediction is carried out. The method utilizes the artificial neural network to predict the spatial distribution of the related peak values, shortens the search time of the carrier frequency, realizes the rapid capture of the signal, and improves the capture precision and the capture speed.

Description

technical field [0001] The invention relates to a GNSS signal acquisition method based on an artificial neural network, belonging to the technical field of satellite positioning and navigation. Background technique [0002] Satellite navigation and positioning systems involve politics, economy, military and other fields, and are widely used in vehicle navigation, aviation and navigation, geographic surveying and mapping, mass consumption, etc., and can provide users with location and time information. Transmitting radio navigation signals to achieve positioning and navigation functions for end users. [0003] The receiver is the core part of the navigation and positioning system, usually composed of an antenna, a radio frequency front end, and a baseband signal processing part. The antenna receives satellite signals broadcast by visible satellites in the space constellation; the radio frequency front-end converts the radio frequency signals received by the antenna into easy...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S19/29G01S19/30G06F17/14G06N3/08
CPCG01S19/29G01S19/30G06F17/14G06N3/08
Inventor 王永罗兵孙娟娟邱冬悦李祥杰
Owner SHANDONG UNIV
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