阅读说明：本技术 相减策略的实复转换式信噪比估计算法 (Real-complex conversion type signal-to-noise ratio estimation algorithm of subtraction strategy ) 是由 涂亚庆 陈鹏 李明 于 2019-10-23 设计创作，主要内容包括：本发明涉及信号处理领域,特别是含噪正弦信号的信噪比估计算法。本发明的适用对象为含噪正弦信号的信噪比估计,包括以下步骤：首先,利用对噪声不敏感的实复转换式参数估计算法对采样信号进行预处理,得到信号频率、幅值和初相位,并构造不受噪声影响的参考信号；然后,利用相减策略将采样信号与参考信号相减,得到纯噪声信号；最后,分别计算噪声信号和参考信号的平均功率,通过信噪比的定义求解采样信号的信噪比。本发明涉及的含噪正弦信号的信噪比估计算法构思巧妙、实现简单、实时性强,提高了信号的信噪比估计精度。(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.)

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

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

in the formula: p_sRepresenting the average power, P, of the reference signal_nRepresents the average power of the noise signal;

the fourth step: by usingAnd calculating the signal-to-noise ratio estimation value.

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

Amplitude estimation

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,

and calculating the signal-to-noise ratio of the sampling signal by using the formula (6) according to the signal-to-noise ratio definition.

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

Amplitude estimation

And an initial phase estimate

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

Calculating the average power of the reference signal by

Calculating the average power of the noise signal and defining it according to the signal-to-noise ratio by using

And calculating the signal-to-noise ratio of the sampling signal.