CN108957403B - Gaussian fitting envelope time delay estimation method and system based on generalized cross correlation - Google Patents
Gaussian fitting envelope time delay estimation method and system based on generalized cross correlation Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/18—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using ultrasonic, sonic, or infrasonic waves
- G01S5/22—Position of source determined by co-ordinating a plurality of position lines defined by path-difference measurements
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention pertains to radio orientation; radio navigation; measuring distance or speed by using radio waves; localization or presence detection using reflection or re-radiation of radio waves; the technical field of similar devices adopting other waves discloses a Gaussian fitting envelope time delay estimation method and system based on generalized cross-correlation, and cross-correlation function values of signals are obtained through a generalized cross-correlation method; extracting an envelope point of a cross-correlation function by adopting a piecewise maximum value method; and fitting the envelope by using a Gaussian function, and taking the maximum value point of the Gaussian function as the time delay estimated value of the signal. The invention uses generalized cross-correlation to calculate the cross-correlation function value; extracting the envelope of the cross-correlation function by adopting a segmented maximum value envelope extraction method; fitting the envelope by adopting a Gaussian fitting mode; and taking the time delay corresponding to the maximum value of the envelope as the arrival time delay difference value of the two signals. The method is low in calculation complexity and easy to implement, and the effectiveness, universality and accuracy of time delay estimation of the algorithm are verified through simulation experiments.
Description
Technical Field
The invention pertains to radio orientation; radio navigation; measuring distance or speed by using radio waves; localization or presence detection using reflection or re-radiation of radio waves; the technical field of similar devices adopting other waves, in particular to a Gaussian fitting envelope time delay estimation method and system based on generalized cross correlation.
Background
Currently, the current state of the art commonly used in the industry is such that: the passive target detection has the advantages of good concealment, low power consumption, low cost, difficult interference and the like, and occupies an important position in the fields of military affairs, national defense safety, environmental monitoring, emergency rescue, military reconnaissance and the like. The acoustic array sensor network is one of the most common passive detection technologies, has excellent performances in the aspects of automatic detection, positioning, identification, high-speed real-time tracking and the like, can bypass obstacles such as trees, riprap and the like by sound waves, and can effectively search targets even in a radar detection blind area. Therefore, the acoustic array sensor network can make up the problems existing in active detection and has higher practical value. The accurate time delay estimation is a core link of the detection of the acoustic array sensor network and is also a key step for improving the positioning accuracy of the passive target. The least mean square adaptive filtering (LMS) method and the generalized cross-correlation function (GCC) method are the methods with the highest frequency, and a plurality of algorithms which are improved or derived based on the two methods improve the accuracy of the time delay estimation. However, the least mean square adaptive filtering method is easily influenced by environment, filter order and the like, and cannot approach to a true value well; the generalized cross-correlation function method is low in calculation complexity, easy to implement, good in real-time performance, capable of resisting reverberation and noise and wide in application. Based on the GCC sound source time delay estimation method, the robustness of the algorithm and the accuracy of time delay estimation are shown from the aspects of different test environments, different sound source objects and the like. In the prior art, a calibration algorithm for estimating a time delay of a frequency division band based on a frequency domain probability model is provided, on the basis of GCC, a received signal is trained in a frequency domain in a segmentation manner to establish probability models of a measurement node and a reference node, and a time delay estimation of the signal is obtained in a weighting manner of each frequency band; the method solves the problem of low time delay estimation precision caused by difference of hardware performance and environmental factors of the node receiving the signal, but the method needs priori knowledge for model training, the priori knowledge cannot be obtained in advance in practice, the model performance is limited by frequency division and deviation threshold selection, and a reasonable frequency division and deviation threshold strategy needs further research and determination. The second prior art provides a quadratic correlation delay estimation method based on empirical mode decomposition reconstruction, before correlation, a method combining a cepstrum method and a spectral subtraction method is adopted to distinguish a signal leading component and a noise component, and the empirical mode decomposition method is adopted to carry out signal decomposition and reconstruction, so that the noise immunity is better and the cross-correlation peak value is sharper when the method carries out delay estimation; although the time delay estimation precision is improved by the preprocessing and noise reduction technologies, the calculation complexity is extremely high, and the whole system is possibly paralyzed under the condition that the life cycle of the system is limited, so that the aim of detecting the target cannot be achieved. The third prior art provides a new method combining a generalized cross-correlation function method and a wavelet denoising algorithm, wherein the algorithm can inhibit noise, and can also show better time delay estimation performance for a mobile sound source, but the denoising effect of the wavelet is limited by the selection of a wavelet basis and the decomposition scale. In a noise and reverberation environment, the GCC time delay estimation precision is remarkably reduced, and in order to solve the problem, methods such as quadratic cross-correlation, Hilbert transform, spectrum analysis, matched filtering and the like are applied, however, the algorithms adopt an optimization algorithm to sharpen a peak value, the influence of noise on time delay estimation is weakened, extremely high calculation complexity is caused, a large amount of storage space is occupied, and huge storage cost of a system is caused.
In summary, the problems of the prior art are as follows:
(1) in the prior art, prior knowledge is needed during model establishment, the requirement is often difficult to meet in an actual scene, the performance of the model is limited by frequency division and deviation threshold selection, and a reasonable and optimal frequency division and deviation threshold selection scheme is not provided by the technology.
(2) In the second prior art, various signal preprocessing and noise reduction technologies are adopted to improve the time delay estimation precision, but the operations of high-frequency signal selection, decomposition, reconstruction and the like performed by the method result in extremely high computational complexity and provide higher requirements for system storage; if the life cycle of the system is short, the technology cannot be applied.
(3) In the third prior art, under a noise and reverberation environment, a wavelet denoising technology is adopted for signal preprocessing, but the denoising performance of the technology is limited by wavelet basis and decomposition scale, and an optimal scheme for determining the parameter is not given in the technology.
(4) In order to weaken the influence of noise and reverberation on the delay estimation precision, the GCC peak value is sharpened by means of secondary cross-correlation, Hilbert transform, spectrum analysis, matched filtering and the like, and the precision is improved.
The difficulty and significance for solving the technical problems are as follows:
aiming at the technical problems, the problems of system power consumption and memory caused by the fact that the difficulty is mainly focused on how to obtain the prior knowledge of the target sound source, the determination of the optimal setting scheme of the model parameters and the optimization algorithm with high calculation complexity are solved. In practice, due to the characteristics of randomness, uncertainty of number, unpredictability of a target state and the like of a target sound source, prior knowledge is difficult to obtain, and the application of a method based on the prior knowledge is limited, so that the research of a time delay estimation method without the restriction of the prior knowledge has great significance for detecting moving and fixed targets; secondly, the optimization scheme of the model parameter setting is determined and researched, so that the precision and universality of the time delay estimation can be improved, the burden of the system on cost, power consumption, storage and the like can be reduced, and the realization of the time delay estimation-based technology in engineering is facilitated.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a Gaussian fitting envelope time delay estimation method and system based on generalized cross correlation.
The invention is realized in such a way, the Gaussian fitting envelope delay estimation method based on the generalized cross correlation obtains the cross correlation function value of the signal by the generalized cross correlation method; extracting an envelope point of a cross-correlation function by adopting a piecewise maximum value method; and fitting the envelope by using a Gaussian function, and taking the maximum value point of the Gaussian function as the time delay estimated value of the signal.
Further, the method for estimating the gaussian fitting envelope delay based on the generalized cross-correlation specifically comprises the following steps:
(1) extracting a segmented maximum value envelope;
(2) gaussian-fit envelope delay estimation for GCC, n discrete data points (x)i,yi) (i ═ 1, 2.., n), a gaussian function g (x) is constructed:
so that
The minimum value is obtained.
Further, the (1) specifically includes:
step one, data preprocessing
Setting the length of each segment of data: d _ length;
each piece of data: rs1s2(τ):d_R；
The number of segments is as follows: n _ d
if(d_R％d_length＝＝0)
N_d＝d_R/d_length
else
N_d＝d_R/d_length+1
end if
Step two, envelope extraction, namely calculating the maximum value of each section of data and storing the maximum value into a set to form an initial envelope point, namely up _ envelope;
step three, threshold calculation:
step four, searching a data segment where the maximum value in the up _ envelope is located, recording the data segment as R _ max, wherein the length of the data segment is d _ RmAnd performing data compensation according to a threshold value, wherein the rule is as follows:
if R_max(i)(i＝1,..,d_Rm)＞threshold1；
up_envelope＝up_envelope∪R_max(i)；
end if
and step five, obtaining a final envelope point set up _ envelope.
Further, the (2) specifically includes:
inputting: signals s received by sensor nodes1,s2；
Step one, calculating R by GCC methods1s2(τ)；
Step two, extracting an envelope point up _ envelope by adopting a sectional maximum value envelope extraction method;
step three, fitting the envelope points extracted in the step two by using a Gaussian curve;
step four, obtaining a time delay estimated value by searching a maximum value point of a Gaussian curve function:
another object of the present invention is to provide a gaussian fitting envelope delay estimation system based on generalized cross-correlation for implementing the method for estimating gaussian fitting envelope delay based on generalized cross-correlation, including:
the extraction cross-correlation function module is used for obtaining a cross-correlation function value of the signal by a generalized cross-correlation method; extracting an envelope point of a cross-correlation function by adopting a piecewise maximum value method;
and the fitting envelope module is used for fitting an envelope by utilizing a Gaussian function, and taking the maximum value point of the Gaussian function as the time delay estimated value of the signal.
Another object of the present invention is to provide an acoustic array sensor applying the gaussian fitting envelope delay estimation method based on generalized cross-correlation.
In summary, the advantages and positive effects of the invention are: the invention provides a Gaussian fitting envelope delay estimation method based on generalized cross correlation, which utilizes the generalized cross correlation to calculate a cross correlation function value; extracting the envelope of the cross-correlation function by adopting a segmented maximum value envelope extraction method; fitting the envelope by a Gaussian fitting mode; and taking the time delay corresponding to the maximum value of the envelope as the arrival time delay difference value of the two signals. The method does not need prior knowledge of any detection target, has low calculation complexity and easy realization, and verifies the effectiveness, universality and accuracy of time delay estimation by a simulation experiment.
The invention obtains the funding of national natural science fund projects (No.61572435, 61472305 and 61473222), basic research fund of complex electronic system simulation key laboratories (No. DXZT-JC-ZZ-2015-015) and natural science fund projects (No.2016A610035,2017A610119) of Ningbo city.
Drawings
Fig. 1 is a flowchart of a method for estimating delay of a gaussian fit envelope based on generalized cross-correlation according to an embodiment of the present invention.
Fig. 2 is a flowchart of generalized cross-correlation delay estimation according to an embodiment of the present invention.
Fig. 3 is a schematic flow chart of a GCC-based gaussian fitting envelope delay estimation method according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a waveform of a signal received by a node according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of delay estimation results of two methods according to the embodiment of the present invention.
Fig. 6 is a simplified diagram of outdoor experimental signal collection provided by the embodiment of the present invention.
Fig. 7 is a schematic diagram of a time-domain waveform of a received signal of an acoustic sensor according to an embodiment of the present invention.
Fig. 8 is a schematic diagram of delay estimation results of two algorithms provided in the embodiment of the present invention.
Fig. 9 is a schematic diagram of comparing delay estimation errors of three algorithms according to the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
The time delay estimation is one of the key technologies of passive target detection and positioning based on the acoustic array sensor network. The generalized cross-correlation algorithm with the highest frequency is easy to be interfered by noise, and the requirement of precision is difficult to meet. In order to solve the problem, the invention provides a time delay estimation method of Gaussian fitting envelope based on generalized cross-correlation, which obtains a cross-correlation function value of a signal by a generalized cross-correlation method; extracting an envelope point of a cross-correlation function by adopting a piecewise maximum value method; fitting envelope by using a Gaussian function, and taking a maximum value point of the Gaussian function as a time delay estimated value of a signal; through actual measurement and MATLAB simulation, the method has performance obviously superior to that of a generalized cross-correlation method and a quadratic correlation method, and has higher time delay estimation precision and noise resistance.
As shown in fig. 1, the method for estimating a gaussian fit envelope delay based on generalized cross-correlation according to the embodiment of the present invention includes the following steps:
s101: obtaining a cross-correlation function value of the signal by a generalized cross-correlation method; extracting an envelope point of a cross-correlation function by adopting a piecewise maximum value method;
s102: and fitting the envelope by using a Gaussian function, and taking the maximum value point of the Gaussian function as the time delay estimated value of the signal.
The application of the principles of the present invention will now be described in further detail with reference to the accompanying drawings.
1 description of the Algorithm
1.1 Signal model
Two sound sensor sensors with distance diAnd sensorjReceived signal si(t) (i, j ═ 1.., M) can be expressed as:
s1(t)＝α1s(t-τ1)+n1(t)
s2(t)＝α2s(t-τ2)+n2(t)；
where s (t) is the original signal of the sound source, αiFor attenuation of sound propagating from the source to the array and ai∈(0,1]，τiFor the travel time of the sound source to the sound sensor, niAnd (t) is interference noise.
1.2 generalized Cross-correlation Algorithm
The generalized cross-correlation algorithm is a classic algorithm in the field of time delay estimation, and the weight of an effective frequency spectrum in a signal is improved in a frequency domain weighting processing mode, so that high-precision time delay estimation is obtained. The algorithm flow chart is shown in fig. 2.
The time delay estimation method based on the GCC comprises the following calculation steps:
1.2 Gaussian fitting envelope time delay estimation method based on generalized cross correlation
Based on the fact that the time delay estimation precision of the generalized cross-correlation algorithm stays at the level of a sampling point, and the time delay estimation performance is remarkably reduced under the condition of low signal-to-noise ratio, the high-precision time delay estimation method, namely the Gaussian fitting envelope time delay estimation algorithm based on the generalized cross-correlation, is disclosed, and a flow chart of the algorithm principle is shown in a figure 3.
A-segment maximum value envelope extraction algorithm
In the generalized cross correlation, due to the influence of noise and reverberation environment, the peak value is not sharp any more, but the value with a large correlation coefficient has a large influence on the delay estimation, and the reasonable utilization of the large value is very important for accurately estimating the delay. The method for extracting the segmented maximum value envelope effectively utilizes the maximum value information and processes the minimum value redundant information, so that the time delay estimation is more accurate. The algorithm comprises the following calculation steps:
gaussian fitting envelope time delay estimation method based on GCC
Suppose n discrete data points (x) are knowni,yi) (i ═ 1, 2.., n), a gaussian function g (x) is to be constructed:
so that the sum of the deviations of the Gaussian function in the formula at the respective points is minimized, i.e.
And obtaining the minimum value.
The method can realize the external prediction and the fine internal calculation through Gaussian curve fitting, improve the precision of time delay estimation, and comprises the following calculation steps:
the application effect of the present invention will be described in detail below with reference to the delay simulation and performance analysis.
In order to verify the performance of the proposed algorithm, indoor and outdoor actually-measured sound source data collection is carried out, and 2 groups of experiments are designed according to the sound source characteristics and the experiment scene difference. The sampling frequency fs is 61400Hz, and the sampling interval Ts is 4 s. And the direct correlation algorithm (CC) and the secondary correlation algorithm are compared and analyzed with the algorithm provided by the invention through the evaluation index of Root Mean Square Error (RMSE).
Indoor fixed point sound source experiment
In the experiment, the acoustic source target was 0.76m from the left probe, 0.90m from the right probe, and the distance difference was-0.14 m. In the experiment, a data segment with a higher amplitude value is intercepted from the collected signals, the length of the data segment is 2048 sampling points, time delay estimation is carried out, and the time domain waveforms of the two paths of signals are shown in figure 4. The time delay estimation is carried out by adopting the GCC and the method provided by the invention, and the result is shown in a figure 5, as can be seen from the figure, a Gaussian curve can better show the variation trend of the cross-correlation function, and RMSE of multi-frame time delay estimation values of the two methods is calculated, and as shown in a table 1, compared with the GCC method, the method provided by the invention has lower root mean square error and higher time delay estimation precision.
TABLE 1 comparison of the Performance of the two methods
| Method | True value | GCC method | Proposed method |
| Time delay value | -0.00041176 | -0.00073294 | -0.00038415 |
| |
0 | 0.000321176 | 0.00002765 |
B outdoor fixed point sound source test
And sound source signal collection is carried out outdoors by adopting a linear array, and a scene sketch map of outdoor measurement is shown in figure 6. In measurement, the distance between the node 1 and the node 2 is 12.5m, the distance between the node 2 and the node 3 is 14.5m, the target distance is 19.8m from the sound sensor 1, the distance is 10.4m from the sound sensor 2, and the distance is 12.24m from the sound sensor 3; the sampling frequency Fs is 50000Hz, and the sampling time Ts is 20 s.
The time domain waveforms of the signals received by the three sound sensor nodes are shown in figure 7. The time delay estimation results using GCC and the method proposed by the present invention are shown in fig. 8. As can be seen from fig. 8, the upper envelope and the lower envelope extracted by the present invention can highly reflect the variation trend of the correlation function, and the influence of noise on the delay estimation can be suppressed by means of gaussian fitting smoothing. The results of the GCC delay method compared to the true values are shown in table 2 below:
TABLE 2 comparison of Algorithm Performance
The data in table 2 show that in a noisy complex and reverberant environment, the method provided by the present invention can effectively estimate the time delay, and the mean square error is small, while the generalized cross-correlation method has a large deviation due to the influence of various noises. The method provided by the invention has noise resistance and robustness by combining indoor measurement and outdoor measurement. Using GCC and second correlation algorithm (second correlation) as reference, multi-frame signal delay estimation is adopted, and the result is shown in fig. 9. As can be seen from the mean square error result of FIG. 9, the method provided by the invention is significantly better than the reference algorithm, and has higher precision and stronger stability.
With the continuous and rapid development of electronic technology, network technology, communication technology and the like, networking-based applications permeate into various fields and play important roles, and the accurate acquisition of node position information is an indispensable research subject for the expansion of networking applications. In recent years, passive target detection with an acoustic array as a framework is widely applied, and the research and exploration of an accurate time delay estimation algorithm has great significance for passive sound source positioning. The envelope delay estimation method based on Gaussian fitting is provided on the basis of the traditional generalized cross correlation, and the fitting idea is ingeniously utilized, namely the fitting result shows the general trend, larger deviation cannot occur due to the abnormality of a certain point, the influence of noise is weakened, and the delay estimation is more accurate due to the smoothness.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (3)
1. A Gaussian fitting envelope time delay estimation method based on generalized cross-correlation GCC is characterized in that the Gaussian fitting envelope time delay estimation method based on generalized cross-correlation GCC obtains a cross-correlation function value of a signal through a generalized cross-correlation method; extracting an envelope point of a cross-correlation function by adopting a piecewise maximum value method; fitting envelope by using a Gaussian function, and taking a maximum value point of the Gaussian function as a time delay estimated value of a signal;
the Gaussian fitting envelope time delay estimation method based on the generalized cross-correlation GCC specifically comprises the following steps:
(1) extracting a segmented maximum value envelope;
(2) gaussian-fit envelope delay estimation for GCC, n discrete data points (x)i,yi) 1, 2.., n, a gaussian function g (x) is constructed:
so that
Obtaining a minimum value;
the (1) specifically comprises:
step one, data preprocessing
Setting the length of each segment of data: d _ length, each piece of data: rs1s2(τ):d_R；
The number of segments is as follows: n _ d
Step two, envelope extraction, namely calculating the maximum value of each section of data and storing the maximum value into a set to form an initial envelope point, namely up _ envelope;
step three, threshold calculation:
step four, searching a data segment where the maximum value in the up _ envelope is located, recording the data segment as R _ max, wherein the length of the data segment is d _ RmAnd performing data compensation according to a threshold value, wherein the rule is as follows:
if R_max(i′)＞threshold1，i′＝1，..，d_Rm；
up_envelope＝up_envelope∪R_max(i)；
end if
and step five, obtaining a final envelope point set up _ envelope.
2. A gaussian fitting envelope delay estimation system based on generalized cross-correlation GCC for implementing the method of claim 1, comprising:
the extraction cross-correlation function module is used for obtaining a cross-correlation function value of the signal by a generalized cross-correlation method; extracting an envelope point of a cross-correlation function by adopting a piecewise maximum value method;
and the fitting envelope module is used for fitting an envelope by utilizing a Gaussian function, and taking the maximum value point of the Gaussian function as the time delay estimated value of the signal.
3. An acoustic array sensor applying the method for estimating the envelope delay of Gaussian fit based on GCC with generalized cross-correlation as claimed in claim 1.
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| CN112512116B (en) * | 2020-11-25 | 2022-12-02 | 华东师范大学 | Self-adaptive quadratic cross-correlation TOA estimation method based on 5GSRS signal |
| CN112491489B (en) * | 2020-11-27 | 2022-07-29 | 清华大学 | Method, device and system for time synchronization based on in-band telemetry |
| CN114035157B (en) * | 2021-10-29 | 2022-06-14 | 中国科学院声学研究所 | Sub-band delay estimation method and system based on expectation maximization algorithm |
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