CN109412763B - Digital signal existence detection method based on signal energy-entropy ratio - Google Patents
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Abstract
The invention discloses a digital signal existence detection method based on signal energy-entropy ratio, belonging to the field of signal detection of wireless communication systems, and relating to a digital signal existence detection method which is low in calculation complexity and does not need any priori knowledge, wherein the method can be used as a frame synchronization method of non-cooperative communication, is used for solving the problem of coarse synchronization of a receiving end signal under the condition of unknown frame structure of a sender, replaces frame synchronization, and performs coarse extraction of information; the invention discloses a digital signal existence detection method based on signal energy-entropy ratio. Since the CME iterative algorithm proposed by the background art requires multiple iterations to converge, it will bring about extremely high computational complexity. Therefore, the invention provides the method for judging whether a signal exists in one frame by using the energy-entropy ratio as the measurement index of the existence of the signal and comparing the energy-entropy ratio with the threshold value through setting the threshold value, so that the iteration is not needed, the complexity of calculation is simplified, and the accuracy of the detection of the existence of the signal is higher under the condition of higher signal-to-noise ratio.
Description
Technical Field
The invention belongs to the field of signal detection of a wireless communication system, and relates to a digital signal existence detection method which is low in calculation complexity and does not need any priori knowledge.
Background
Due to the multipath effect and Doppler frequency shift influence in short wave channel transmission, signal fading is obvious, and the diversity combining technology has obvious advantages in resisting fading. Before combining the multi-channel signals, the multi-channel signals are synchronized, and only the useful signals are aligned to the maximum extent, so that a more remarkable combining effect can be achieved. It is therefore important to find the approximate location of the desired signal from the faded single-channel signal. The traditional digital signal detection method generally adopts energy detection, and provides a CME interference detection method in the literature PERTTI HENTTU, SAMI AROMAA. Consequential Mean evaluation Algorithm [ C ], and the method adopts an iterative idea to estimate the signal-to-noise ratio by multiple iterations so as to separate noise and interference signals. The algorithm has the defects of high complexity of multiple iterations, uncertain convergence, experience-based setting of threshold parameters and poor robustness. The endpoint detection is an extremely important method in speech signal processing, is used for determining the starting and ending positions of voice, and is developed more mature. The end point detection method of the voice signal utilizes the characteristics of voice, such as short-time energy of unvoiced consonants, short-time zero crossing rate and a specific design method of difference of signal spectrum entropy to distinguish silent sections without signals from voice sections, and in the sixth chapter of book MATLAB application in voice signal analysis and synthesis, several end point detection methods of the voice signal are taught, and the detection method based on energy-entropy ratio is particularly good in detection effect of signals under fading channels. The disadvantage of this scheme is that the sum of the squares of the amplitudes of all data in one frame is taken as the energy in the energy-entropy ratio, and the energy also contains the energy of noise, so that the error caused by low signal-to-noise ratio is large. The threshold setting uses double thresholds, which are helpful for voice signal detection, but also for digital signals, they are analyzed specifically. Due to the time variability of the voice signal, the short-time analysis can be performed only assuming that the short time (about 30ms) is unchanged, but for the digital signal, the requirement of the short time can be relaxed, that is, different frame lengths can be selected according to specific situations when the frames are divided.
Disclosure of Invention
The signal detection thought of the invention is derived from an end point detection method of a voice signal, the voice signal can also be regarded as a digital signal, the method of end point detection based on the energy-entropy ratio in the voice signal is also suitable for the existence detection of the digital signal, and only the spectral line energy near the modulation frequency point can be taken as the energy in the energy-entropy ratio under the condition that the modulation frequency point of the modulation signal is known, so that the accuracy is improved. Because the performance evaluation modes of the digital signal and the voice signal are different, little noise can be allowed at the head and the tail of a signal segment for the detection of the voice signal, but the length of a noise segment at the head and the tail of the digital signal is not allowed to exceed 1 symbol length, otherwise, error codes can be caused. The invention adopts twice signal existence detection when detecting digital signal end point, firstly, the frame length of a little bit larger can be selected for detection to determine the approximate position of the signal, thus ensuring no signal loss, secondly, the frame length is further reduced on the basis of the signal extracted for the first time, and the position of starting and stopping is further accurately combined with a merging algorithm. For the end point detection of digital signals, the threshold can be set by double thresholds or single thresholds (two thresholds of the double thresholds are set to be the same, namely the single threshold), the double thresholds are better for the first coarse signal detection, and the signal loss is avoided; the single threshold effect is superior to the double thresholds in the second fine detection, and the noise from the head to the tail can be removed as much as possible. Simulation shows that the method also has a good detection effect, is low in complexity and can accurately detect the position of the digital signal.
The technical scheme of the invention is as follows:
framing the received signal, and setting an overlapped section with a half frame length for smooth transition between frames; performing Fast Fourier Transform (FFT) on the framed data in each frame to obtain a magnitude spectrum of each frame, and obtaining the energy and the spectrum entropy value of each frame of data according to the magnitude spectrum, wherein the ratio of the energy to the spectrum entropy value is the energy entropy ratio; the energy entropy ratio is used as a basis for judging whether one frame of data is noise or digital signal; the invention adopts double-threshold judgment, so that the judgment of the existence of the signal is more accurate, and the double-threshold is more favorable for finding out the start-stop position of the signal, thereby reducing the error of the start-stop position judgment; generally speaking, the method for finding the starting and ending positions of a digital signal segment from a segment of data containing noise and having discontinuous signal segments is to traverse all frames, find out the starting frame and the ending frame of a segment of digital signal by comparing the energy-entropy ratio and the threshold value of each frame data, and then continue the search of the next signal segment; finally, recording the starting and ending positions of each digital signal segment in the segment of data;
the invention relates to a digital signal existence detection method based on signal energy-entropy ratio, which comprises the following steps:
Yi(k),i＝1,2,...,fn；k＝1,2,...,wlen；
and 3, calculating the energy of each frame of data, wherein the time domain energy calculating method comprises the following steps: el (electro luminescence)i＝log10(1+AMPiA), i ═ 1,2,. fn, where
1,2, fn, a is a set constant; due to the presence of a, when AMP is usediAmplitude hasWill be in EL with larger variationiThe fading is eased, so that signals and noise at the serious fading position can be more easily distinguished; or the energy can also be obtained from the frequency domain, and for the received data of the known modulation frequency point, the energy is calculated as follows:
1,2.., fn, where Φ is the set of several locations near where the modulation bins are located.
for noise, the distribution of the normalized spectrum probability density function is uniform, so the spectrum entropy value is larger; for digital signals, due to the fact that energy is concentrated in modulation, the normalized spectral probability density function of the digital signals is not uniformly distributed, and therefore the spectral entropy of the digital signals is generally lower than that of noise;
step 5, calculating the energy-entropy ratio of each frame of data:
i＝1,2,...,fn；
step 6, setting double thresholds: the setting of the threshold is related to the energy-entropy ratio of each frame, and the energy-entropy ratio is obtained through calculation; the method comprises the following specific steps: solving the maximum value Me and the initial average value eth of the energy-entropy ratios of all the frames, wherein eth is the average value of the energy-entropy ratios of the previous NIS frames, and NIS is a constant and represents the length of the noise frame before the signal segment starts; setting the threshold Th1 as a noise threshold: th1 ═ alpha1X (Me-eth) + eth, setting threshold Th2 as the signal threshold: th2 ═ alpha2×(Me-eth)+eth，α1,α2Is constant and alpha1＜α2；
Step 7, setting the maximum allowable noise frame length MaxSilence and the minimum signal frame length MinLen, and starting the end point detection of the signal section;
and 8: detecting an end point of a signal segment;
step 8.1, initialization: the signal frame Count is 0, the status flag Case is 0, and the noise frame Count is 0;
step 8.2, reading the energy-entropy ratio of one frame of data, and if Case is 0 or 1, turning to step 8.3; if Case is 2, go to step 8.6;
step 8.3, comparing the energy-entropy ratio with the signal threshold Th 2; if the current frame number is greater than Th2, the signal is detected, the Count is increased by 1, the current frame number is recorded as the initial frame position of the signal, Case is set to be 2, and the step is switched to step 8.2; otherwise, turning to step 8.4;
8.4, comparing the energy entropy ratio with the noise threshold Th1, if the energy entropy ratio is larger than Th1, indicating that a signal possibly exists, increasing the Count by 1, setting the Case to be 1, and turning to the step 8.2; otherwise, turning to step 8.5;
step 8.5, switching to step 8.1 when no signal is detected;
8.6, comparing the magnitude of the energy entropy ratio and the noise threshold Th1, wherein the magnitude is larger than Th1, and the data of the frame is still in the signal section, the Count is increased by 1, the Case is unchanged, and the step 8.2 is switched; otherwise, turning to step 8.7;
step 8.7, noise exists, and the Silence is increased by 1; judging whether the Silence reaches the maximum allowable noise frame length, if not, continuing to serve the current frame as a part of the signal section, increasing the Count by 1, keeping the Case unchanged, and turning to the step 8.2; otherwise, judging whether the signal frame Count is greater than the minimum signal frame length, if the signal frame Count is less than the minimum signal frame length, determining that the number of the detected signal section frames is too short, and turning to the step 8.1 if no signal exists; otherwise, turning to step 8.8;
and 8.8, judging the signal section, recording the position of the ending frame, adding 1 to the signal section mark, preparing to detect the next signal section, and turning to the step 8.1.
The invention has the beneficial effects that: the invention discloses a digital signal existence detection method based on signal energy-entropy ratio. Since the CME iterative algorithm proposed by the background art requires multiple iterations to converge, it will bring about extremely high computational complexity. Therefore, the invention provides the method for judging whether a signal exists in one frame by using the energy-entropy ratio as the measurement index of the existence of the signal and comparing the energy-entropy ratio with the threshold value through setting the threshold value, so that the iteration is not needed, the complexity of calculation is simplified, and the accuracy of the detection of the existence of the signal is higher under the condition of higher signal-to-noise ratio.
Drawings
FIG. 1 is a general flow of data processing;
FIG. 2 is a flow chart of signal segment dual threshold endpoint detection in accordance with the present invention;
FIG. 3 is a flow chart of signal segment single threshold endpoint detection according to the present invention;
FIG. 4 is a diagram of the effect of simulation detection on a signal segment containing noisy data;
Detailed Description
The technical scheme of the invention is detailed below by combining the accompanying drawings and the embodiment. It should be understood that the scope of the present invention is not limited to the following examples, and any techniques implemented based on the present disclosure are within the scope of the present invention.
According to the program, the following parameters are initially set:
the sampling frequency Fs is 9600Hz, and the frequency point of the known signal is f1800Hz and f21400Hz, a frame data length is 30ms, i.e. the frame length wlen is 288 sampling points, the frame-to-frame overlap rate is set to 50%, the frame shift inc is 144 sampling points, and the threshold setting parameter α is set1,α20.05 and 0.15 respectively, the maximum noise section length Maxsilence is set to 5 frames, and the minimum signal section length MinLen is set to 5 frames.
As shown in fig. 1, for a received digital sequence r (N), N is 1,2, N, the signal presence detecting step is as follows:
Step 3, calculating the energy of each frame of data: here, since the modulation frequency point is known, the energy is obtained from the frequency domain:
i＝1,2,...,fn，
step 5, calculating the energy-entropy ratio of each frame of data:
i＝1,2,...,fn；
step 6, setting double thresholds: and calculating the maximum value Me of the energy-entropy ratios of all the frames and an initial mean value eth, wherein eth is the mean value of the energy-entropy ratios of the previous 15 frames, and considering that the signal segment has noise of at least 15 frames before the beginning. Setting the threshold Th1 as a noise threshold: th1 ═ 0.05 × (Me-eth) + eth, with a threshold Th2 set as the signal threshold: th2 ═ 0.15 × (Me-eth) + eth;
step 7, setting the maximum allowable noise frame length MaxSilence and the minimum signal frame length MinLen, and starting the end point detection of the signal section;
as shown in fig. 2, the end point detection algorithm for the signal segment has the following steps:
and 3, comparing the energy-entropy ratio with the signal threshold Th 2. If the current frame number is larger than Th2, the signal is detected, Count is increased by 1, the current frame number is recorded as the initial frame position of the signal, Case is set to be 2, and step 2 is switched. Otherwise, turning to the step 4;
and 4, comparing the energy-entropy ratio Eer with the noise threshold Th1, wherein the energy-entropy ratio Eer is larger than Th1, indicating that a signal possibly exists, increasing the Count by 1, setting the Case to be 1, and turning to the step 2. Otherwise, turning to the step 5;
step 5, turning to step 1 when no signal is detected;
and 6, comparing the energy-entropy ratio Eer with the noise threshold Th1, wherein the energy-entropy ratio is larger than Th1, indicating that the frame data is still in the signal section, the Count is increased by 1, the Case is unchanged, and turning to the step 2. Otherwise, turning to step 7;
step 7, noise exists, and the Silence is increased by 1 (possibly due to signal fading, the energy entropy of a signal segment is lower); and judging whether the Silence reaches the maximum allowable noise frame length, if not, continuing to serve as a part of the signal section by the current frame, increasing the Count by 1, keeping the Case unchanged, and turning to the step 2. Otherwise, judging whether the signal frame Count is greater than the minimum signal frame length, if so, judging that the number of the detected signal section frames is too short, considering that no signal exists, and turning to the step 1. Otherwise, turning to the step 8;
and 8, judging the signal segment, recording the position of the ending frame, adding 1 to the signal segment mark, preparing to detect the next signal segment, and turning to the step 1.
End point detection is carried out on a section of noisy data by Matlab simulation software, the detection effect is shown in figure 4 when the signal-to-noise ratio is 15dB, and a detected signal section is arranged between two sections of marked lines, so that the position of the signal section can be accurately found.
Claims (1)
1. A digital signal existence detection method based on signal energy-entropy ratio comprises the following steps:
step 1, sampling a received signal r (t) by a sampling frequency Fs to obtain r (N), setting the data length of r (N) as N, and framing r (N); the specific framing method and parameter setting are as follows: taking t seconds of data as a frame, namely the frame length wlen is t multiplied by Fs; the overlap ratio between the frame and the interframe is set as n%, that is, the frame shift is inc ═ wlen-wlen × n%; total number of minutes of N long data
So that the received sequence of length N, r (N), becomes [ r ] after framing1(n),r2(n),...,ri(n),...,rfn(n)]Wherein each element represents a frame of data;
step 2, performing fast Fourier transform on each frame data, and obtaining a modulus value to obtain an energy spectrum Yi(k),i＝1,2,...,fn；k＝1,2,...,wlen；
And 3, calculating the energy of each frame of data, wherein the time domain energy calculating method comprises the following steps: el (electro luminescence)i＝log10(1+AMPiA), i ═ 1,2,. fn, where
a is a set constant;
step 4, calculating the spectrum entropy value of each frame data: the normalized spectral probability density function for each frequency component within a frame of data is:
the spectral entropy is:
step 5, calculatingEnergy-entropy ratio of each frame data:
step 6, setting double thresholds: the setting of the threshold is related to the energy-entropy ratio of each frame, and the energy-entropy ratio is obtained through calculation; the method comprises the following specific steps: solving the maximum value Me and the initial average value eth of the energy-entropy ratios of all the frames, wherein eth is the average value of the energy-entropy ratios of the previous NIS frames, and NIS is a constant and represents the length of the noise frame before the signal segment starts; setting the threshold Th1 as a noise threshold: th1 ═ alpha1X (Me-eth) + eth, setting threshold Th2 as the signal threshold: th2 ═ alpha2×(Me-eth)+eth，α1,α2Is constant and alpha1＜α2；
Step 7, setting the maximum allowable noise frame length MaxSilence and the minimum signal frame length MinLen, and starting the end point detection of the signal section;
and 8: detecting an end point of a signal segment;
step 8.1, initialization: the signal frame Count is 0, the status flag Case is 0, and the noise frame Count is 0;
step 8.2, reading the energy-entropy ratio of one frame of data, and if Case is 0 or 1, turning to step 8.3; if Case is 2, go to step 8.6
Step 8.3, comparing the energy-entropy ratio with the signal threshold Th 2; if the current frame number is greater than Th2, the signal is detected, the Count is increased by 1, the current frame number is recorded as the initial frame position of the signal, Case is set to be 2, and the step is switched to step 8.2; otherwise, turning to step 8.4;
8.4, comparing the energy entropy ratio with the noise threshold Th1, if the energy entropy ratio is larger than Th1, indicating that a signal possibly exists, increasing the Count by 1, setting the Case to be 1, and turning to the step 8.2; otherwise, turning to step 8.5;
step 8.5, switching to step 8.1 when no signal is detected;
8.6, comparing the magnitude of the energy entropy ratio and the noise threshold Th1, wherein the magnitude is larger than Th1, and the data of the frame is still in the signal section, the Count is increased by 1, the Case is unchanged, and the step 8.2 is switched; otherwise, turning to step 8.7;
step 8.7, noise exists, and the Silence is increased by 1; judging whether the Silence reaches the maximum allowable noise frame length, if not, continuing to serve the current frame as a part of the signal section, increasing the Count by 1, keeping the Case unchanged, and turning to the step 8.2; otherwise, judging whether the signal frame Count is greater than the minimum signal frame length, if the signal frame Count is less than the minimum signal frame length, determining that the number of the detected signal section frames is too short, and turning to the step 8.1 if no signal exists; otherwise, turning to step 8.8;
and 8.8, judging the signal section, recording the position of the ending frame, adding 1 to the signal section mark, preparing to detect the next signal section, and turning to the step 8.1.
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| CN110706693B (en) * | 2019-10-18 | 2022-04-19 | 浙江大华技术股份有限公司 | Method and device for determining voice endpoint, storage medium and electronic device |
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