CN112114287B - Outlier real-time eliminating method for azimuth observation data - Google Patents

Outlier real-time eliminating method for azimuth observation data Download PDF

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CN112114287B
CN112114287B CN202010994478.6A CN202010994478A CN112114287B CN 112114287 B CN112114287 B CN 112114287B CN 202010994478 A CN202010994478 A CN 202010994478A CN 112114287 B CN112114287 B CN 112114287B
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钱进
毛卫宁
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Southeast University
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    • G—PHYSICS
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Abstract

The invention discloses a method for eliminating outliers of azimuth observation data in real time, which comprises the steps of firstly obtaining broadband array receiving signals of an underwater moving target, estimating the azimuth of the target by calculating the space spectrum of the receiving signals at different moments, obtaining the azimuth observation data, and distinguishing and correcting the outliers of the azimuth observation data by utilizing the uniform motion characteristic of the target. The invention is suitable for eliminating isolated outliers, spot outliers and mixed outliers, has simple method, improves the accuracy of dynamic outlier point detection and azimuth tracking performance of the moving target azimuth measurement data, can be popularized and applied to the discrimination and correction of isolated outliers, spot outliers and mixed outliers of measurement data of other moving target measurement systems, and has good theoretical and engineering application values.

Description

Outlier real-time eliminating method for azimuth observation data
Technical Field
The invention belongs to the field of underwater acoustic signal processing, and particularly relates to a method for removing outliers of underwater moving target azimuth observation data in real time.
Background
Sonar is equipment for underwater detection by using sound waves, and orientation estimation is a basic function of sonar. In the azimuth estimation process, due to the influence of various factors such as noise, interference and ocean multipath propagation channels, abnormal values, i.e. outliers, which are seriously deviated from the normal azimuth change rule, may exist in the azimuth measurement data. The existence of the outlier can seriously affect the performance of subsequent sonar signal processing. The outliers are generally classified into isolated, spot and mixed outliers. For isolated outliers, the widely adopted methods mainly comprise an alpha-beta-gamma filtering method, a least square estimation method, a matching element measurement method and the like, and the elimination effect is good. For speckle outliers, the elimination method mainly comprises a polynomial extrapolation fitting method, a Kalman filtering method, a robust filtering M estimation method, a least square B spline approximation method and the like, and is suitable for outlier elimination under different conditions, wherein only the robust filtering M estimation method has a good elimination effect, but the processing process is complex. For the mixed type outlier with both the isolated outlier and the speckle outlier, the common method is to distinguish the isolated outlier and the speckle outlier first, then to process them separately, the process is complicated, and further research is needed for the accurate determination of the starting point and the ending point of the speckle outlier. In addition, for a moving object, the azimuth changes with time, and it is difficult to effectively correct the speckle pattern outlier by methods such as polynomial extrapolation fitting, and further research and study are also required.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects of the prior art, the invention provides a field value real-time eliminating method of azimuth observation data by distinguishing and correcting the field value of azimuth measurement data through course data by utilizing the characteristic of uniform motion of a target.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the technical scheme that:
a method for eliminating outliers of azimuth observation data in real time comprises the following steps:
step 1, acquiring a broadband array receiving signal of an underwater moving target by using a base array.
And 2, calculating the spatial spectrum of the broadband array received signals at different moments, wherein the spatial spectrum is the energy distribution of all possible incoming wave directions in the space, estimating the target position through the spatial spectrum, and acquiring target position observation data.
And 3, for the uniform motion target or the segmented uniform motion target, taking three adjacent point azimuth measurement data with the time interval T in the uniform motion time period, and calculating the course:
Figure BDA0002692058920000011
where phi is the heading and alpha is1，α2，α3For adjacent three-point azimuth measurement data of time interval T, theta1＝α2-α1，θ2＝α3-α2。
Step 4, repeating the step 3 to obtain a group of course data
Figure BDA0002692058920000021
φiIndicating the heading at the ith time, and n indicating the n time.
Step 5, aligning the course data
Figure BDA0002692058920000022
Calculating the mean value thereof
Figure BDA0002692058920000023
And a variance σ.
Step 6, carrying out outlier discrimination on the course data, and calculating the residual error of the course data at each moment
Figure BDA0002692058920000024
Comparing the residual with a 3 sigma threshold, if viIf | ≧ 3 σ, heading data φ corresponding to the timeiAnd the azimuth data is a outlier, the outlier is removed, course data is utilized to correct the outlier, and sigma is the variance of the course data. Other methods may be used to determine the outlier of the heading data.
Further: for the plane wave incident signal in step 1, the frequency domain of the broadband array received signal is represented as:
X(ω)＝AS(ω)+N(ω)
where X (ω) is a frequency spectrum of an M × 1 dimensional array received signal, ω is an angular frequency, S (ω) is a frequency spectrum of an N × 1 dimensional spatial source signal, N (ω) is a frequency spectrum of an M × 1 dimensional noise signal, and a is an M × N dimensional array flow pattern matrix:
A＝[a0(ω),a1(ω),…,aN-1(ω)]
wherein, aiAnd (omega) is a guide vector of the ith signal, N is the number of information sources, and M is the number of array elements.
For a uniform linear array:
Figure BDA0002692058920000025
wherein i is 0,1iIs the incident angle of the ith signal, d is the array element spacing, j is the complex factor, c is the acoustic velocity [ ·]TIs a matrix transposition operation.
Further: in step 2, the spatial spectrum can be calculated by methods such as adaptive beam forming (MVDR), high-resolution spatial spectrum estimation (MUSIC) and the like, and the target azimuth estimation can be obtained by searching spatial spectrum peaks.
Further: calculating the spatial spectrum of the array received signals at different moments by adopting a self-adaptive beam forming MVDR method:
Figure BDA0002692058920000026
wherein, PMVDR(theta) is the spatial spectrum, theta is the scan orientation, a (theta) is the steering vector, [ ·]HFor conjugate transposition, R-1Is the inverse of the received signal covariance matrix. Searching a spatial spectrum peak to obtain a target azimuth, and repeating the processing process on array received signals at different moments to obtain a group of azimuth observation data.
Further: in the step 1, the array is a linear array or a cylindrical array or any other array type.
Compared with the prior art, the invention has the following beneficial effects:
the invention utilizes the uniform motion characteristic of the target, judges and corrects the outlier of the azimuth measurement data through the course data, does not need to distinguish the isolated outlier and the spot outlier, can directly process the mixed outlier, has simple method, improves the detection accuracy and the azimuth tracking performance of the dynamic outlier point of the azimuth measurement data of the moving target, can be popularized and applied to the judgment and correction of the isolated outlier, the spot outlier and the mixed outlier of the measurement data of other moving target measurement systems, and has good theoretical and engineering application values.
Drawings
FIG. 1 shows the elimination of mixed outliers in azimuth measurement data by a polynomial extrapolation fitting method.
FIG. 2 shows the elimination result of the mixed outlier in the azimuth measurement data according to the method of the present invention.
Detailed Description
The present invention is further illustrated by the following description in conjunction with the accompanying drawings and the specific embodiments, it is to be understood that these examples are given solely for the purpose of illustration and are not intended as a definition of the limits of the invention, since various equivalent modifications will occur to those skilled in the art upon reading the present invention and fall within the limits of the appended claims.
A method for removing outliers of azimuth observation data in real time takes outliers of a group of azimuth measurement data to be removed in real time as an example, and the specific implementation process is as follows:
step (1) obtaining a broadband array receiving signal: for plane wave incident signals, the frequency domain of the broadband array received signal is represented as
X(ω)＝AS(ω)+N(ω)
Where X (ω) is a frequency spectrum of an M × 1 dimensional array received signal, S (ω) is a frequency spectrum of an N × 1 dimensional spatial source signal, N (ω) is a frequency spectrum of an M × 1 dimensional noise signal, a is an M × N dimensional array flow pattern matrix:
A＝[a0(ω),a1(ω),…,aN-1(ω)]
wherein a isiAnd (omega) is a guide vector of the ith signal, N is the number of information sources, and M is the number of array elements. For a uniform linear array of the wires,
Figure BDA0002692058920000031
wherein i is 0,1iIs the incident angle of the ith signal, d is the array element spacing [. degree]TIs a matrix transposition operation.
The given target signal is band-limited noise, the frequency band is 200 Hz-4000 Hz, the sampling frequency is 48kHz, the target moves at a constant speed, the speed is 20m/s, and the heading is 150 degrees. The receiving array is a uniform linear array, the number of the array elements is 10, and the spacing between the array elements is 0.5 m. The noise is white Gaussian noise with zero mean value, the signal-to-noise ratio is-10 dB, the sampling fast beat number is 2048, and the total observation time is 5.12 s.
Acquiring azimuth observation data: computing spatial spectrum of array received signals at different moments by adopting MVDR algorithm
Figure BDA0002692058920000041
Where a (θ) is the steering vector. And searching a spatial spectrum peak to obtain target azimuth estimation, and repeating the processing process on array received signals at different moments to obtain a group of azimuth observation data.
Step (3), calculating course: taking the azimuth measurement data of adjacent three points with time interval T, and calculating course by the following formula
Figure BDA0002692058920000042
Wherein alpha is1，α2，α3For adjacent three-point azimuth measurement data of time interval T, theta1＝α2-α1，θ2＝α3-α2. Other methods of calculating heading may also be employed.
Step (4) repeating step (3) to obtain a group of course data
Figure BDA0002692058920000043
Step (5) calculating course data
Figure BDA0002692058920000044
Mean value of
Figure BDA0002692058920000045
And a variance σ.
And (6) carrying out wild value discrimination and correction on the azimuth measurement data: calculating residual error of course data at each moment
Figure BDA0002692058920000046
Comparing the residual with a 3 sigma threshold, if viIf | ≧ 3 σ, heading data φ corresponding to the timeiAnd the orientation data is the outlier, the outlier is removed, and the outlier is corrected by utilizing the course data.
FIG. 1 is a diagram illustrating a mixed outlier in azimuth measurement data processed by a polynomial extrapolation fitting method, and FIG. 2 is a diagram illustrating a mixed outlier in azimuth measurement data processed by the method of the present invention. Comparing fig. 1 and fig. 2, it can be seen that, for the mixed outlier, the correction of the outlier point by the polynomial extrapolation fitting method, especially the correction error of the speckle outlier point is larger, and the isolated outlier and the speckle outlier in the outlier can be better distinguished and corrected by the present invention.
The method for eliminating the outlier of the orientation measurement data in real time provides an effective solution to the problems that the existing mixed outlier processing method is complex, the positions of the starting point and the ending point of the spot outlier are difficult to accurately determine, the dynamic spot outlier is difficult to correct and the like, improves the detection accuracy and the orientation tracking performance of the dynamic outlier of the orientation measurement data of the moving target, can be popularized and applied to the discrimination and correction of the isolated outlier, the spot outlier and the mixed outlier of the measurement data of other moving target measurement systems, and has good theoretical and engineering application values.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.

Claims (5)

1. A method for eliminating outliers of azimuth observation data in real time is characterized by comprising the following steps:
step 1, acquiring a broadband array receiving signal of an underwater moving target by using a base array;
step 2, calculating the spatial spectrum of the broadband array received signal at different moments, wherein the spatial spectrum is the energy distribution of all possible incoming wave directions in the space, estimating the target position through the spatial spectrum, and acquiring target position observation data;
and 3, for the uniform motion target or the segmented uniform motion target, taking three adjacent point azimuth measurement data with the time interval T in the uniform motion time period, and calculating the course:
Figure FDA0002944145490000011
where phi is the heading and alpha is1，α2，α3For adjacent three-point azimuth measurement data of time interval T, theta1＝α2-α1，θ2＝α3-α2；
Step 4, repeating the step 3 to obtain a group of course data
Figure FDA0002944145490000016
φiIndicating the course of the time i, and n indicates the time;
step 5, aligning the course data
Figure FDA0002944145490000012
Calculating the mean value thereof
Figure FDA0002944145490000013
And a variance σ;
step 6, carrying out outlier discrimination on the course data, and calculating the residual error of the course data at each moment
Figure FDA0002944145490000014
Comparing the residual with a 3 sigma threshold, if viIf | ≧ 3 σ, heading data φ corresponding to the timeiAnd the azimuth observation data are outliers, the outliers are removed, course data are used for carrying out outlier correction, and sigma is the variance of the course data.
2. The method for eliminating outliers of the azimuth observation data in real time according to claim 1, which is characterized in that: for the plane wave incident signal in step 1, the frequency domain of the broadband array received signal is represented as:
X(ω)＝AS(ω)+N(ω)
where X (ω) is a frequency spectrum of an M × 1 dimensional array received signal, ω is an angular frequency, S (ω) is a frequency spectrum of an N × 1 dimensional spatial source signal, N (ω) is a frequency spectrum of an M × 1 dimensional noise signal, and a is an M × N dimensional array flow pattern matrix:
A＝[a0(ω),a1(ω),…,ak(ω),…,aN-1(ω)]
wherein, ak(omega) is a steering vector of a kth signal, N is an information source number, and M is an array element number;
for a uniform linear array:
Figure FDA0002944145490000015
wherein k is0,1,...,N-1，
Figure FDA0002944145490000021
Is the incident angle of the kth signal, d is the array element spacing, j is a complex factor, c is the underwater sound velocity [ ·]TIs a matrix transposition operation.
3. The method for eliminating outliers of the azimuth observation data in real time according to claim 1, which is characterized in that: and 2, calculating the spatial spectrum of the array received signals at different moments by adopting a self-adaptive beam forming MVDR method or a high-resolution spatial spectrum estimation MUSIC method, wherein the position corresponding to the spatial spectrum peak is the target position.
4. The method for eliminating outliers of the azimuth observation data in real time according to claim 1, which is characterized in that: calculating the spatial spectrum of the array received signals at different moments by adopting a self-adaptive beam forming MVDR method:
Figure FDA0002944145490000022
wherein, PMVDR(theta) is the spatial spectrum, theta is the scan orientation, a (theta) is the steering vector, [ ·]HFor conjugate transposition, R-1Is the inverse of the received signal covariance matrix; searching a spatial spectrum peak to obtain a target azimuth, and repeating the processing process on array received signals at different moments to obtain a group of azimuth observation data.
5. The method for eliminating outliers of the azimuth observation data in real time according to claim 1, which is characterized in that: in the step 1, the array is a linear array or a cylindrical array or any other array type.
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