GNSS Signal Processing Using Real-Part Amplitude Thresholding
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Solution Overview
Problem
Existing Global Navigation Satellite System (GNSS) receivers face challenges in accurately estimating Gaussian noise power due to strong interference, which hinders effective separation of GNSS signals from noise and interference.
Innovation Solution
A method that processes radionavigation signals by converting them into the frequency domain, determining the distribution law of the amplitude of the real component, setting a threshold based on this distribution, and filtering components with amplitudes greater than the threshold to separate useful GNSS signals from interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-correlative interference cancellation processing is implemented to separate GNSS signals from interference, then signal separation capability is improved, but Gaussian noise power estimation becomes difficult due to strong interference (100 to 1,000 times stronger than noise)
Solution Approach 1:
The patent segments the signal processing by separating the real part (I) and imaginary part (Q) of the complex signal, and further divides the frequency spectrum into multiple bins. By processing only the real part and using histogram analysis on segmented frequency bins, the method can estimate noise power in regions unaffected by interference, thus resolving the contradiction between signal separation and noise estimation accuracy in the presence of strong interference
Solution Approach 2:
Instead of processing the entire complex signal (both real and imaginary parts), the patent applies partial action by processing only the real part (I component). This reduces computational complexity and resource requirements while still enabling effective noise power estimation through histogram analysis of the real component's amplitude distribution, addressing the resource constraint issue
2Volume of moving object
If miniaturization of GNSS receivers is implemented to reduce size, then portability is improved, but digital resources become limited making noise power estimation more difficult
Solution Approach 1:
The patent implements partial action by processing only the real part (I component) of the complex signal instead of both real and imaginary parts. This reduces the computational load and digital resource requirements by approximately half, enabling noise power estimation in miniaturized receivers with limited processing capabilities while maintaining estimation accuracy
Solution Approach 2:
The patent uses a computationally efficient histogram-based approach that requires minimal digital resources. By using simple amplitude thresholding and counting operations rather than complex statistical estimators, the method enables noise power estimation in resource-constrained miniaturized receivers without requiring expensive or complex processing units
3Object-generated harmful factors
If frequency filtering techniques (frequency excision or amplitude blocking) are used to cancel interference, then interference cancellation is improved, but accurate Gaussian noise power estimation becomes essential and difficult to achieve
Solution Approach 1:
The patent applies preliminary action by first performing a Fourier transform to convert the time-domain signal to the frequency domain, then constructing a histogram of the real component amplitudes before any filtering operations. This preliminary noise power estimation is performed on the frequency spectrum where interference appears as distinct peaks, allowing the method to identify and exclude interference-affected frequency bins before estimating the noise power from the remaining bins
Solution Approach 2:
The method uses partial action by focusing the histogram analysis only on the real part (I component) of the complex signal and only on frequency bins that are not affected by interference. This selective processing reduces computational complexity while maintaining the ability to accurately estimate noise power for subsequent filtering operations
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method effectively reduces the resource requirements for GNSS signal processing by focusing on the real part of the signal, allowing for efficient separation of GNSS signals from strong interference with minimal digital resources.
Implementation Method 1
converting the radionavigation signal into the frequency domain by means of a complex Fourier transform
Data Source
AI summary
The invention relates to a method for processing a radionavigation signal generated by a satellite (SAT), said method comprising the following steps implemented in a processing unit of a radionavigation receiver: converting (102) the radionavigation signal into the frequency domain by means of a complex Fourier transform so as to obtain a frequency-domain radionavigation signal comprising a real part I and an imaginary part Q, the real part I having an amplitude I2 associated with one frequency; determining (103) a distribution law of the amplitude I2 of the real component I of the frequency-domain radionavigation signal; determining (104) an amplitude of the real component for which the distribution function is zero, said amplitude defining a threshold; processing (105) the frequency-domain radionavigation signal so as to filter components the amplitude of which is higher than the determined threshold.