CN110808929A - Real-complex conversion type signal-to-noise ratio estimation algorithm of subtraction strategy - Google Patents

Real-complex conversion type signal-to-noise ratio estimation algorithm of subtraction strategy Download PDF

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CN110808929A
CN110808929A CN201911057136.5A CN201911057136A CN110808929A CN 110808929 A CN110808929 A CN 110808929A CN 201911057136 A CN201911057136 A CN 201911057136A CN 110808929 A CN110808929 A CN 110808929A
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signal
noise
noise ratio
real
estimation algorithm
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涂亚庆
陈鹏
李明
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Pla Military Service College
Army Service Academy of PLA
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/0224—Channel estimation using sounding signals
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/336—Signal-to-interference ratio [SIR] or carrier-to-interference ratio [CIR]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/0224—Channel estimation using sounding signals
    • H04L25/0228—Channel estimation using sounding signals with direct estimation from sounding signals
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms

Abstract

The invention relates to the field of signal processing, in particular to a signal-to-noise ratio estimation algorithm of a noisy sinusoidal signal. The invention is applicable to signal-to-noise ratio estimation of a noisy sinusoidal signal, and comprises the following steps: firstly, preprocessing a sampling signal by using a real complex conversion type parameter estimation algorithm insensitive to noise to obtain signal frequency, amplitude and an initial phase, and constructing a reference signal not influenced by the noise; then, subtracting the sampling signal from the reference signal by using a subtraction strategy to obtain a pure noise signal; and finally, respectively calculating the average power of the noise signal and the reference signal, and solving the signal-to-noise ratio of the sampling signal through the definition of the signal-to-noise ratio. The signal-to-noise ratio estimation algorithm of the noisy sinusoidal signal related by the invention has the advantages of ingenious conception, simple realization and strong real-time property, and improves the signal-to-noise ratio estimation precision of the signal.

Description

Real-complex conversion type signal-to-noise ratio estimation algorithm of subtraction strategy
Technical Field
The invention relates to the field of signal processing, in particular to a signal-to-noise ratio estimation algorithm of a noisy sinusoidal signal.
Background
The signal-to-noise ratio is an important parameter in sinusoidal signals, the signal-to-noise ratio estimation is to estimate the signal-to-noise ratio from sampling signals containing noise, can be used for analyzing signal characteristics, is widely applied to the fields of radars, communication, voice, biomedicine, instrument devices and the like, and has important theoretical significance and application value.
At present, the research on parameters such as signal frequency and the like is more, the research on signal to noise ratio is less, and a time-frequency method based on subspace method, statistical method based on received signals and FFT method improvement is mainly adopted.
(1) The algorithm has signal-to-noise ratio tracking capability, but is complex for noisy sinusoidal signals, and is disadvantageous for increased computation due to the need of performing maximum likelihood Estimation on a covariance matrix of the received signals, which is unfavorable for practical use.
(2) The time-frequency method based on FFT improvement (reference [3 ]: strong permission, welfare, country of reign, etc.. Matlab-based estimation of sinusoidal signal-to-noise ratio time-frequency method research [ J ]. test technical report, 2012, 26 (1): 46-50.) is easy to implement, but needs to be used under specific conditions, and the sampling point number, sampling frequency and sampling time of the signal all affect the estimation precision of the signal-to-noise ratio, and 3 parameters need to be adjusted for signals of different frequencies, so that the use is inconvenient, and the estimation precision is not high.
Disclosure of Invention
The invention aims to provide a signal-to-noise ratio estimation algorithm with high estimation precision and strong real-time performance, which is suitable for signal-to-noise ratio estimation of a noise-containing sinusoidal signal, solves the problems of complex realization and low estimation precision of the existing signal-to-noise ratio estimation algorithm, and expands the application range of the existing signal-to-noise ratio estimation algorithm.
The real-complex conversion type signal-to-noise ratio estimation algorithm of the subtraction strategy is explained as follows:
the basic idea of the algorithm is as follows: preprocessing the sampling signal by adopting a real repeated conversion type parameter estimation algorithm insensitive to noise, constructing a reference signal without noise, obtaining a noise signal by a subtraction strategy, and obtaining a signal-to-noise ratio estimation value according to the definition of the signal-to-noise ratio.
Firstly, preprocessing a sampling signal by using a noise-insensitive real-complex conversion type parameter estimation algorithm (reference [4 ]: CHEN Pen, TU Yaq, LI Ming, et al. A real-to-complex conversion phase differentiation method for a Coriolis mass flowmeter signal [ C ].2019 International conference on Communications, Information System and Computer Engineering (CISCE), Nanjing, 2019: 280-284.), obtaining a signal frequency, an amplitude and an initial phase, and constructing a reference signal which is not influenced by noise; then, subtracting the sampling signal from the reference signal by using a subtraction strategy to obtain a pure noise signal; and finally, respectively calculating the average power of the noise signal and the reference signal, and solving the signal-to-noise ratio of the sampling signal through the definition of the signal-to-noise ratio. The signal-to-noise ratio estimation algorithm of the noisy sinusoidal signal related by the invention has the advantages of ingenious conception, simple realization and strong real-time property, and improves the signal-to-noise ratio estimation precision of the signal.
The sampling signal is a noise-containing sinusoidal signal, and the model is shown as the formula (1).
x(n)＝acos(ωn+θ)+z(n) (1)
In the formula: ω, a and θ respectively represent the frequency, amplitude and initial phase of the signal, N is 0, 1, L, N-1, N is a sampling time point, and N is a signal length; z (n) is mean 0 and variance σ2White additive gaussian noise.
In order to estimate the signal-to-noise ratio of the sampling signal and better analyze the signal characteristics, a real-complex conversion type signal-to-noise ratio estimation algorithm of a subtraction strategy is provided.
The first step is as follows: generating a reference signal
For the sampling signal x (n), the real complex conversion type parameter estimation algorithm insensitive to noise is utilized to process the sampling signal x (n) to obtain a sampling signal frequency estimation value
Figure BSA0000193676160000021
Amplitude estimation
Figure BSA0000193676160000022
And an initial phase estimateAnd a reference signal containing no noise component is constructed using equation (2).
The second step is that: obtaining a noise signal
And subtracting the sampling signal and the reference signal by using a subtraction strategy to obtain a pure noise signal.
z(n)＝x(n)-r(n) (3)
The third step: calculating the signal-to-noise ratio
Calculating the average power of the reference signal and the noise signal by using the equations (4) and (5) respectively,
Figure BSA0000193676160000025
Figure BSA0000193676160000026
and calculating the signal-to-noise ratio of the sampling signal by using the formula (6) according to the signal-to-noise ratio definition.
Figure BSA0000193676160000027
Drawings
The invention is further elucidated on the basis of the figures and the detailed description.
Fig. 1 shows the basic idea of the real-complex transform snr estimation algorithm of the subtraction strategy.
In the figure: 1 represents a sampling signal; 2, representing the frequency, amplitude and initial phase estimation value of the signal; 3 denotes a reference signal not affected by noise; 4 represents a noise signal; 5 denotes the average power of the reference signal and the noise signal; 6 represents the signal-to-noise ratio estimated value; 7, a real-complex conversion type parameter estimation algorithm; 8, generating a reference signal; 9 denotes a subtraction strategy; 10 and 11 represent average power calculations; and 12 represents the signal-to-noise ratio estimate.
Fig. 2 is a schematic time domain diagram of a sampled signal.
Detailed Description
The specific embodiment of the invention is as follows:
the first step is as follows: processing by using a real complex conversion type parameter estimation algorithm insensitive to noise to obtain a sampling signal frequency estimation value
Figure BSA0000193676160000031
Amplitude estimation
Figure BSA0000193676160000032
And an initial phase estimate
Figure BSA0000193676160000033
And useA reference signal is constructed that does not contain noise components.
The second step is that: subtracting the reference signal from the sampled signal using z (n) ═ x (n) — (n) to obtain a pure noise signal.
The third step: by using
Figure BSA0000193676160000035
Calculating the average power of the reference signal by
Figure BSA0000193676160000036
Calculating the average power of the noise signal and defining it according to the signal-to-noise ratio by using
Figure BSA0000193676160000037
And calculating the signal-to-noise ratio of the sampling signal.

Claims (1)

1. The real-complex conversion type signal-to-noise ratio estimation algorithm of the subtraction strategy is characterized in that: the applicable object is the signal-to-noise ratio estimation of the noise-containing sinusoidal signal;
the algorithm comprises the following steps:
the first step is as follows: preprocessing the sampling signal x (n) by using a real-complex conversion type parameter estimation algorithm to obtain a signal frequency estimationValue of
Figure FSA0000193676150000011
Amplitude estimation
Figure FSA0000193676150000012
And an initial phase estimate
Figure FSA0000193676150000013
And constructing a noise-free reference signal
Figure FSA0000193676150000014
In the formula: n represents a signal time point, N is 0, 1, L, N-1, and N represents a signal length;
the second step is that: obtaining a pure noise signal by using a formula z (n) ═ x (n) -r (n);
the third step: respectively using type
Figure FSA0000193676150000015
And
Figure FSA0000193676150000016
obtaining the average power of the reference signal and the noise signal;
in the formula: psRepresenting the average power, P, of the reference signalnRepresents the average power of the noise signal;
the fourth step: by usingAnd calculating the signal-to-noise ratio estimation value.
CN201911057136.5A 2019-10-23 2019-10-23 Real-complex conversion type signal-to-noise ratio estimation algorithm of subtraction strategy Pending CN110808929A (en)

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