CN114270216A - FMCW array radar motion multi-target weak signal detection method and device under strong clutter, computer equipment and storage medium - Google Patents

FMCW array radar motion multi-target weak signal detection method and device under strong clutter, computer equipment and storage medium Download PDF

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CN114270216A
CN114270216A CN201980099412.6A CN201980099412A CN114270216A CN 114270216 A CN114270216 A CN 114270216A CN 201980099412 A CN201980099412 A CN 201980099412A CN 114270216 A CN114270216 A CN 114270216A
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张柏华
汤加跃
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Arkmicro Technologies Inc
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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
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/06—Systems determining position data of a target
    • G01S13/08—Systems for measuring distance only
    • G01S13/32—Systems for measuring distance only using transmission of continuous waves, whether amplitude-, frequency-, or phase-modulated, or unmodulated
    • G01S13/34—Systems for measuring distance only using transmission of continuous waves, whether amplitude-, frequency-, or phase-modulated, or unmodulated using transmission of continuous, frequency-modulated waves while heterodyning the received signal, or a signal derived therefrom, with a locally-generated signal related to the contemporaneously transmitted signal
    • 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
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/50—Systems of measurement based on relative movement of target
    • G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems

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Abstract

A FMCW array radar motion multi-target weak signal detection method and device under strong clutter comprises the following steps: respectively carrying out fast Fourier transform processing on difference frequency signals of different array elements with different frequency modulation periods to obtain distance spectrums of different targets (S101); performing cancellation processing on the distance spectrum to eliminate clutter signals generated by a stationary target (S102); carrying out first detection on the data after the cancellation processing to obtain a first target, wherein the first detection adopts ordered statistic constant false alarm rate detection (S103); performing second detection on the first target to enable the number of second targets obtained after the second detection to meet a preset condition, wherein the number of the second targets is smaller than that of the first targets (S104); carrying out third detection on the second target to obtain a third target, carrying out space spectrum estimation on the third detection by adopting array information, and rejecting a false target by utilizing the difference of the real target and the false target space spectrum estimation peak value (S105); the distance, speed and angle information of the third target is output (S106). The method can obviously improve the detection capability of the motion multi-target weak signals in the strong clutter environment.

Description

FMCW array radar motion multi-target weak signal detection method and device under strong clutter, computer equipment and storage medium Technical Field
The invention belongs to the technical field of radar detection, and particularly relates to a method and a device for detecting multiple target weak signals of FMCW array radar motion under a strong clutter, computer equipment and a storage medium.
Background
Compared with a pulse doppler radar, an FMCW (frequency modulated continuous wave) radar has a special advantage that a short-distance blind area is generally less than 1 meter (even several centimeters), which is significant for many application scenarios requiring short-distance ranging.
For FMCW radar, it is a difficult point to detect the weak signal of multiple moving targets under the background of strong clutter, and the strong clutter environment, especially the non-uniform clutter environment, brings new challenges to the detection of the constant false alarm of the moving weak signals. In many application fields of FMCW radars, the above problems are faced, and taking a vehicle-mounted radar which is hot at present as an example, the environment faced by the radar is often complex (many city streets, motor vehicles, non-motor vehicles and pedestrians), has various clutter (including non-uniform clutter), has a plurality of targets, and the echo signals of the targets are often weak (pedestrians and the like). Many scholars propose methods for improving the detection performance of the FMCW radar, but many methods are based on triangular waves, and the triangular waves need to pair the distance and the speed of a target, so that the detection capability of a weak target is limited, and false targets are easily introduced. Therefore, the problem of detecting the motion weak signal under the background of the strong clutter is not solved well. In the actual engineering, the low false alarm rate and the low false alarm rate of weak signal detection cannot be considered, so that the detection and identification efficiency is not high.
Technical problem
The invention aims to provide a method and a device for detecting multiple-target weak signals of FMCW array radar motion under strong clutter, the method effectively improves the detection performance of the weak signal targets, gives consideration to low false alarm rate and low false alarm rate of weak signal detection, and has high detection and identification efficiency.
Technical solution
In order to achieve the above object, in an embodiment of the first aspect of the present invention, a method for detecting multiple target weak signals in motion of an FMCW array radar under a strong clutter, where the FMCW array radar is modulated by a sawtooth signal, includes:
respectively carrying out fast Fourier transform processing on difference frequency signals of different array elements with different frequency modulation periods to obtain distance spectrums of different targets;
performing cancellation processing on the distance spectrum to eliminate clutter signals generated by a static target;
carrying out first detection on the data subjected to cancellation processing to obtain a first target; the first detection adopts ordered statistic constant false alarm rate detection;
carrying out second detection on the first target to enable the number of second targets obtained after the second detection to meet a preset condition; the number of the second targets is less than that of the first targets;
carrying out third detection on the second target to obtain a third target, wherein the third detection adopts array information to carry out spatial spectrum estimation, and eliminates a false target by utilizing the difference of the real target and the false target spatial spectrum estimation peak value;
and outputting the distance, speed and angle information of the third target.
In an embodiment of the present invention, the first detecting specifically includes:
determining a multiplication factor;
for each distance unit, setting a preset number of protection units and calculation units on two sides of the distance unit, and then sequencing the calculation units from large to small;
selecting a calculation unit with a preset sequence number to be multiplied by the product factor to serve as a threshold value for first detection;
and comparing the measured distance unit with the threshold value of the first detection by using a comparator, and determining the measured distance unit as a first target when the measured distance unit is larger than the threshold value.
In an embodiment of the present invention, the second detecting specifically includes:
multiplying the mean value of the first target obtained by the first detection by a preset coefficient to obtain an initial threshold value of the second detection;
comparing the measured distance unit with the initial threshold value of the second detection by using a comparator, and determining the measured distance unit as a second target when the measured distance unit is larger than the initial threshold value of the second detection;
when the number of the second targets is higher than a preset condition, increasing the initial threshold value by a preset search step value to be used as a comparison threshold value of next detection search, and when the number of the second targets is lower than the preset condition, decreasing the initial threshold value by a preset search step value to be used as a comparison threshold value of next detection search; and repeating the comparison and judgment until the number of the second targets meets the preset condition.
In an embodiment of the present invention, the third detecting specifically includes:
calculating the distance and the speed of the second target;
calculating covariance matrices of corresponding distances and velocities;
respectively carrying out characteristic decomposition on covariance matrixes of different targets;
the angle estimation is realized by minimum optimization search and the MUSIC spectrum is calculated;
and judging whether the MUSIC spectrum is true or false, judging as a third target when the MUSIC spectrum peak is larger than a preset judgment threshold, and otherwise, judging as a non-target.
In an embodiment of the present invention, the preset decision threshold is 5-10 times of the average of all spectral peaks of MUSIC.
In one embodiment of the present invention, the following expression is used for calculating the distance and the speed of the second target, wherein the expression is used for calculating the distance
Figure PCTCN2019130437-APPB-000001
Wherein c represents the speed of light, TcRepresenting a single frequency-modulated period, FsTo representSampling rate, MbinFFT unit for indicating the location of the target distance, B signal bandwidth, NFFTRepresenting the number of distance FFT points; calculating speed by using expressions
Figure PCTCN2019130437-APPB-000002
Wherein, VbinAn FFT unit for representing the target speed, f represents the frequency of the transmitted signal, K represents the number of frequency modulation cycles of the tacho period, and TfIndicating the tachometer period.
In one embodiment of the invention, the covariance matrix is corrected
Figure PCTCN2019130437-APPB-000003
Is subjected to characteristic decomposition of
Figure PCTCN2019130437-APPB-000004
Wherein, USIs a signal subspace spanned by eigenvectors corresponding to large eigenvalues, and UNIs a noise subspace spanned by the feature vectors corresponding to the small feature values; and estimating the number of the information sources by using the characteristic values after the characteristic decomposition.
In one embodiment of the invention, the angle estimation is implemented with a minimum optimization search using an expression
Figure PCTCN2019130437-APPB-000005
Calculating MUSIC spectrum by adopting expression
Figure PCTCN2019130437-APPB-000006
In one embodiment of the invention, the frequency modulation operating bandwidth of the FMCW array radar satisfies the expression
Figure PCTCN2019130437-APPB-000007
Wherein d isresRepresenting distance resolution, c representing light speed, and B representing frequency modulation working bandwidth; the single frequency modulation period of the FMCW array radar satisfies the expression
Figure PCTCN2019130437-APPB-000008
Wherein, VmaxIndicating the maximum measurable speed, TcRepresents a single frequency modulation period, and λ represents the transmitted signal wavelength; the speed measurement period of the FMCW array radar satisfies an expression
Figure PCTCN2019130437-APPB-000009
Wherein, VresRepresenting the velocity resolution, TfRepresenting a velocity measurement period, and lambda represents the wavelength of a transmitted signal; the sampling rate of the FMCW array radar satisfies an expression
Figure PCTCN2019130437-APPB-000010
Wherein, FsRepresenting the sampling rate, dmaxIndicating the maximum detectable distance, TcRepresenting a single frequency modulation period, c the speed of light, and B the frequency modulation operating bandwidth.
In order to achieve the above object, an apparatus for detecting multiple target weak signals in motion of an FMCW array radar modulated by a sawtooth signal in a strong clutter according to an embodiment of the second aspect of the present invention includes:
the Fourier transform module is used for respectively carrying out fast Fourier transform processing on difference frequency signals of different array elements with different frequency modulation periods to obtain distance spectrums of different targets;
the cancellation processing module is used for performing cancellation processing on the distance spectrum to eliminate clutter signals generated by a static target;
the first detection module is used for carrying out first detection on the data subjected to the cancellation processing to obtain a first target; the first detection adopts ordered statistic constant false alarm rate detection;
the second detection module is used for carrying out second detection on the first target so that the number of second targets obtained after the second detection meets a preset condition; the number of the second targets is less than that of the first targets;
the third detection module is used for carrying out third detection on the second target to obtain a third target, the third detection adopts array information to carry out spatial spectrum estimation, and a false target is removed by utilizing the difference of the real target and the false target spatial spectrum estimation peak value;
and the output module is used for outputting the distance, the speed and the angle information of the third target.
In an embodiment of the present invention, the first detecting module is specifically configured to:
determining a multiplication factor;
for each distance unit, setting a preset number of protection units and calculation units on two sides of the distance unit, and then sequencing the calculation units from large to small;
selecting a calculation unit with a preset sequence number to be multiplied by the product factor to serve as a threshold value for first detection;
and comparing the measured distance unit with the threshold value of the first detection by using a comparator, and determining the measured distance unit as a first target when the measured distance unit is larger than the threshold value.
In an embodiment of the present invention, the second detecting module is specifically configured to:
multiplying the mean value of the first target obtained by the first detection by a preset coefficient to obtain an initial threshold value of the second detection;
comparing the measured distance unit with the initial threshold value of the second detection by using a comparator, and determining the measured distance unit as a second target when the measured distance unit is larger than the initial threshold value of the second detection;
when the number of the second targets is higher than a preset condition, increasing the initial threshold value by a preset search step value to be used as a comparison threshold value of next detection search, and when the number of the second targets is lower than the preset condition, decreasing the initial threshold value by a preset search step value to be used as a comparison threshold value of next detection search; and repeating the comparison and judgment until the number of the second targets meets the preset condition.
In an embodiment of the present invention, the third detecting module is specifically configured to:
calculating the distance and the speed of the second target;
calculating covariance matrices of corresponding distances and velocities;
respectively carrying out characteristic decomposition on covariance matrixes of different targets;
the angle estimation is realized by minimum optimization search and the MUSIC spectrum is calculated;
and judging whether the MUSIC spectrum is true or false, judging as a third target when the MUSIC spectrum peak is larger than a preset judgment threshold, and otherwise, judging as a non-target.
In an embodiment of the present invention, the preset decision threshold is 5-10 times of the average of all spectral peaks of MUSIC.
In one embodiment of the present invention, the following expression is used for calculating the distance and the speed of the second target, wherein the expression is used for calculating the distance
Figure PCTCN2019130437-APPB-000011
Wherein c represents the speed of light, TcRepresenting a single frequency-modulated period, FsRepresenting the sampling rate, MbinFFT unit for indicating the location of the target distance, B signal bandwidth, NFFTRepresenting the number of distance FFT points; calculating speed by using expressions
Figure PCTCN2019130437-APPB-000012
Wherein, VbinAn FFT unit for representing the target speed, f represents the frequency of the transmitted signal, K represents the number of frequency modulation cycles of the tacho period, and TfIndicating the tachometer period.
In one embodiment of the invention, the covariance matrix is corrected
Figure PCTCN2019130437-APPB-000013
Is subjected to characteristic decomposition of
Figure PCTCN2019130437-APPB-000014
Wherein, USIs a signal subspace spanned by eigenvectors corresponding to large eigenvalues, and UNIs a noise subspace spanned by the feature vectors corresponding to the small feature values; and estimating the number of the information sources by using the characteristic values after the characteristic decomposition.
In one embodiment of the invention, the angle estimation is implemented with a minimum optimization search using an expression
Figure PCTCN2019130437-APPB-000015
Calculating MUSIC spectrum by adopting expression
Figure PCTCN2019130437-APPB-000016
In one embodiment of the invention, the frequency modulation operating bandwidth of the FMCW array radar satisfies the expression
Figure PCTCN2019130437-APPB-000017
Wherein d isresRepresenting distance resolution, c representing light speed, and B representing frequency modulation working bandwidth; the single frequency modulation period of the FMCW array radar satisfies the expression
Figure PCTCN2019130437-APPB-000018
Wherein, VmaxIndicating the maximum measurable speed, TcRepresents a single frequency modulation period, and λ represents the transmitted signal wavelength; the speed measurement period of the FMCW array radar satisfies an expression
Figure PCTCN2019130437-APPB-000019
Wherein, VresRepresenting the velocity resolution, TfRepresenting a velocity measurement period, and lambda represents the wavelength of a transmitted signal; the sampling rate of the FMCW array radar satisfies an expression
Figure PCTCN2019130437-APPB-000020
Wherein, FsRepresenting the sampling rate, dmaxIndicating the maximum detectable distance, TcRepresenting a single frequency modulation period, c the speed of light, and B the frequency modulation operating bandwidth.
To achieve the above object, a computer apparatus according to an embodiment of the third aspect of the present invention includes: one or more processors, a memory, and one or more computer programs, wherein the processors and the memory are connected by a bus, the one or more computer programs are stored in the memory and configured to be executed by the one or more processors, and the processors execute the computer programs to realize the above FMCW array radar motion multi-target weak signal detection method under strong clutter.
In order to achieve the above object, a storage medium of a fourth aspect of the present invention is a storage medium for storing a computer program, which when run on a computer device, causes the computer device to execute the FMCW array radar motion multi-target weak signal detection method under strong clutter as described above.
Advantageous effects
The method eliminates clutter generated by a static target through cancellation processing, and then detects data with the clutter removed. The detection at the moment adopts 3 times of detection, and the first detection ensures that the weak signal can pass through a threshold to ensure low false alarm rate; the range of the second detection is further reduced, and the false target is removed to reduce the operation amount of the third detection; the third detection utilizes array information to carry out spatial spectrum estimation, and utilizes the difference of the real target and the false target spatial spectrum estimation peak value to carry out distinction, so that the residual false targets can be removed. Through three times of detection, the detection performance of the weak signal target can be obvious, the low false alarm rate and the low false alarm rate of weak signal detection are considered, and the detection and identification efficiency is high.
Drawings
Fig. 1 is a schematic flow chart of a method for detecting multiple target weak signals in motion of an FMCW array radar under a strong clutter according to an embodiment of the present invention;
FIG. 2 is a block diagram of a first test;
FIG. 3 is a schematic flow chart of a third test;
fig. 4 is a schematic block diagram of a FMCW array radar motion multi-target weak signal detection apparatus under a strong clutter according to an embodiment of the present invention;
fig. 5 is a block diagram of a computer device according to an embodiment of the present invention.
Modes for carrying out the invention
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative only and should not be construed as limiting the invention.
In the description of the present invention, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In the description of the present invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "connected" and "connecting" are to be interpreted broadly, and may be, for example, mechanical or electrical; may be directly connected or indirectly connected through an intermediate. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present invention with reference to the following description and drawings. In the description and drawings, particular embodiments of the invention have been disclosed in detail as being indicative of some of the ways in which the principles of the embodiments of the invention may be practiced, but it is understood that the scope of the embodiments of the invention is not limited correspondingly. On the contrary, the embodiments of the invention include all changes, modifications and equivalents coming within the spirit and terms of the claims appended hereto.
The embodiment of the invention provides a method for detecting multiple target weak signals in FMCW array radar motion under a strong clutter, wherein the FMCW array radar is modulated by sawtooth wave signals, as shown in figure 1, the method comprises the following steps:
s101, performing fast Fourier transform processing on difference frequency signals of different array elements with different frequency modulation periods to obtain distance spectrums of different targets.
The input array difference frequency signal data comprises N array elements and K frequency modulation periods, and each frequency modulation period is provided with M sampling points. And respectively carrying out Fast Fourier Transform (FFT) processing on the K frequency modulation periods of the N array elements to obtain distance spectrums of different targets. The distance spectrum is 3-dimensional stereo data.
And S102, performing cancellation processing on the distance spectrum to eliminate clutter signals generated by the static target.
The clutter cancellation processing can eliminate clutter signals generated by the static targets, so that the number of echoes participating in subsequent operation is reduced, and the subsequent multi-moving target separation work is simplified.
S103, carrying out first detection on the data after cancellation processing to obtain a first target; and the first detection adopts the constant false alarm rate detection of the ordered statistics.
Constant false alarm detection of order statistics is actually an adaptive threshold detection that uses a reference window sample to estimate parameters of the ambient noise to achieve the constant false alarm rate characteristic. The environmental noise includes clutter and interference. When the reference environment unit is a multi-target environment which is non-uniformly distributed, the ordered statistic constant false alarm detection has better anti-interference capability compared with other types of constant false alarm detection.
Fig. 2 is a block diagram of the first detection. The first detection specifically comprises:
determining a multiplication factor T; for each distance unit D, a preset number of protection units (Pn, Pn +1) and calculation units (X) are set on both sides of the distance unit D1-X N) Then, sequencing the calculation units from big to small; the preset serial number can be selected to represent the frequency modulation period number K of the speed measurement period. And multiplying the calculation unit Z with the preset serial number K by the product factor T to serve as a threshold value S for first detection. In the diagram D is the target unit under test, since the power of the target may leak into the adjacent units, several units adjacent to the target are not used as the estimation of the background clutter and are used as the protection unit P.
And comparing the measured distance unit D with the threshold value S for the first detection by using a comparator, and determining the measured distance unit D as the first target when the measured distance unit D is larger than the threshold value S.
For example: after 1024-point FFT is carried out on echo difference frequency signals of a group of frequency modulation signals, 1024 output data are obtained, data 1-512 are taken for detection (because the output of the FFT is symmetrical), each data is detected one by one, and one data represents one frequency point (namely represents a certain distance). If the number of protection units is 4 and the number of calculation units is 8, then for the 20 th unit, the protection units are 18, 19, 21 and 22; the calculation units are 14, 15, 16, 17, 23, 24, 25 and 26.
In order to detect a target from a weak signal, the threshold needs to be lowered for the first detection to avoid missing a real target. The first detection is to detect a suspected target from the non-uniform clutter.
S104, carrying out second detection on the first target to enable the number of second targets obtained after the second detection to meet a preset condition; the number of the second targets is smaller than the number of the first targets.
The second detection is used for removing part of false targets. Since the requirements for constant false alarm detection are low for the first order statistic, it is possible that some false targets are mistaken for targets. And the range is further reduced by the second detection, and the false target is removed.
The second detection specifically comprises:
and multiplying the mean value of the first target obtained by the first detection by a preset coefficient to obtain an initial threshold value of the second detection. And calculating the mean value of the first detection threshold-crossing target, multiplying the mean value by a preset coefficient to obtain an initial threshold value of the second detection search, and setting a small point, namely setting a preset system to be small, of the initial fixed threshold value of the second detection search in order to reduce the false alarm probability and consider the detection of strong signals and weak signals.
And comparing the measured distance unit with the initial threshold value of the second detection by using a comparator, and determining the measured distance unit as a second target when the measured distance unit is larger than the initial threshold value of the second detection.
When the number of the second targets is higher than a preset condition, increasing the initial threshold value by a preset search step value to be used as a comparison threshold value of next detection search, and when the number of the second targets is lower than the preset condition, decreasing the initial threshold value by a preset search step value to be used as a comparison threshold value of next detection search; and repeating the comparison and judgment until the number of the second targets meets the preset condition.
Setting a preset search step value (for example, 10%), if the second detection is lower than the preset condition, decreasing the second initial threshold by the search step value to continue searching until the target is searched; and if the number of the targets searched for the second time is higher than the preset condition, which indicates that the false targets are too many, increasing the initial threshold for the second time by a search stepping value for searching again.
The preset condition of the number of targets in the second detection can be generally set according to needs (depending on hardware and requirements). For example, the vehicle-mounted angle radar and the forward radar are obviously different, the action distance of the angle radar is short, the number of targets can be set to be a few points (for example, 10-20), the action distance of the forward radar is long, the hardware performance has enough margin, and the number of targets can be set to be a large point (for example, 30-40).
When the number of the targets obtained by the m-1 detection search is higher than the preset condition, the judgment threshold needs to be increased, and when the m detection search is performed after the judgment threshold is increased by a preset search step value, the number of the targets obtained by the detection search is found to be lower than the preset condition, and the judgment threshold needs to be reduced. The amplitude for reducing the judgment threshold can not be changed according to the original adjustment amplitude, otherwise, the data can not be converged, and the target quantity obtained by the second detection can never meet the preset condition. At this time, the adjustment range can be halved. That is, the preset search step value is increased or decreased by half, which is helpful for the convergence of the number of the second targets obtained by the second detection to quickly satisfy the preset condition.
For example: the preset search step value is 10%. The target initial threshold is 100. When the detection threshold is 100, the number of the second targets obtained by the second detection is higher than the preset condition, so that the detection threshold needs to be increased, when the detection threshold is increased by a preset search step value to 100+10 ═ 110, the number of the second targets obtained by the second detection is lower than the preset condition, at this time, the detection threshold needs to be decreased, and at this time, if the number of the second targets obtained by the second detection is decreased by a preset search step value 110-10 ═ 100, the number of the second targets obtained by the second detection cannot meet the preset condition all the time. Therefore, the preset search step value needs to be halved. And (5) reducing the amplitude by half, namely setting the detection threshold to be 110-5 to 105, and the like.
And S105, carrying out third detection on the second target to obtain a third target, wherein the third detection adopts array information to carry out spatial spectrum estimation, and eliminates the false target by utilizing the difference of the real target and the false target spatial spectrum estimation peak value.
As shown in fig. 3, the third detection specifically includes the following steps:
and S1051, calculating the distance and the speed of the second target.
Calculating the distance and the speed of the second target uses the following expression, wherein the expression is used for calculating the distance
Figure PCTCN2019130437-APPB-000021
Wherein c represents the speed of light, TcRepresenting a single frequency-modulated period, FsRepresenting the sampling rate, MbinFFT unit for indicating the location of the target distance, B signal bandwidth, NFFTRepresenting the number of distance FFT points.
Calculating speed by using expressions
Figure PCTCN2019130437-APPB-000022
Wherein, VbinThe FFT unit where the target speed is located is represented, f represents the frequency of the transmitted signal, K represents the number of frequency modulation cycles (the number of points of the speed FFT) of the speed measurement cycle, and TfIndicating the speed measuring period (equal to KT)c). The above equation relates to the definition of velocity where the target is positive near the radar velocity.
And S1052, calculating a covariance matrix of the corresponding distance and speed.
Here, two cases need to be distinguished: one condition is that no coherent signal exists (i.e. two or more objects have the same distance and the same speed), the array covariance matrix at this time can be obtained by using the data S of the original data S (three-dimensional stereo data: one frame of data has N array elements, each array element has K frequency modulation periods, each frequency modulation period has a plurality of sampling point data) through two FFT transformations (firstly, FFT is carried out on the sampling point data of a single frequency modulation period to obtain the distance information of the object; secondly, FFT is carried out on the K frequency modulation period data of the corresponding object distance on the basis of the first FFT to obtain the speed information of the object)FFT(N×K×N FFTDimension) is obtained, i.e. the data for finding the corresponding target range bin and velocity bin for each array element is arranged into a vector X ═ X according to the array element1 X 2 … X N] TWherein
X n＝S FFT(n,V bin,M bin),n＝1,2,···N；V bin＝1,2,···K,M bin＝1,2,···N FFT. At this time, the covariance matrix of the corresponding target is:
Figure PCTCN2019130437-APPB-000023
the superscript 'H' denotes the conjugate transpose.
In another case, there is a coherent signal, and the covariance matrix in this case needs to sum up the cell data of the range cell with the object without the velocity object, and X in this casenBecomes a vector (X)n＝S FFT(n,1:K,M bin) N is 1,2, … N), X is an N × K matrix, and the covariance matrix of the corresponding target is:
Figure PCTCN2019130437-APPB-000024
and S1053, respectively carrying out characteristic decomposition on the covariance matrixes of different targets.
For covariance matrix
Figure PCTCN2019130437-APPB-000025
Is subjected to characteristic decomposition of
Figure PCTCN2019130437-APPB-000026
Wherein the content of the first and second substances,U Sis a subspace spanned by the eigenvectors corresponding to the large eigenvalues, i.e. the signal subspace, and UNIs a subspace spanned by the feature vectors corresponding to the small feature values, i.e. the noise subspace. The estimation of the number of the information sources can be carried out by utilizing the characteristic values after the characteristic decomposition, and the correct estimation of the number of the information sources is the premise of the estimation of the space spectrum. Ideally the signal subspace and the noise subspace in the data space are orthogonal to each other, i.e. the steering vectors in the signal subspace are also orthogonal to the noise subspace: a isH(θ)U N＝0。
And S1054, angle estimation is realized by minimum optimization search, and a MUSIC spectrum is calculated.
The angle estimation here is carried out with a minimum optimization search, i.e.
Figure PCTCN2019130437-APPB-000027
The spatial spectrum estimation formula of the classical MUSIC algorithm is obtained as follows:
Figure PCTCN2019130437-APPB-000028
and S1055, judging whether the MUSIC spectrum is true or false, judging as a third target when the peak of the MUSIC spectrum is larger than a preset judgment threshold, and otherwise, judging as a non-target.
And (3) judging a true and false target of the MUSIC spectrum obtained by calculation: and if the MUSIC spectral peak is larger than a preset judgment threshold, judging as a third target, otherwise, judging as a non-target. The preset decision threshold can be set to be 5-10 times of the average value of all spectral peaks of the MUSIC through a large amount of data simulation and combination of measured data.
And S106, outputting the distance, speed and angle information of the third target.
After three times of detection, false targets can be removed, and the distance, speed and angle of real targets can be obtained.
In this embodiment, the specific steps of the first detection, the second detection and the third detection are all the optimal solutions. In other embodiments, the specific steps of the first detection, the second detection, and the third detection may also be implemented in other forms to achieve the corresponding effect of each detection.
Of course, in the actual engineering implementation, many problems need to be solved, such as correction of array errors, mutual coupling of array elements and channel amplitude inconsistency, characteristic decomposition of matrices, correct estimation of the number of signal sources, processing of signal decorrelation, and the like, which are not described herein again.
Compared with the modulation of a triangular wave FMCW signal, the modulation of a sawtooth wave FMCW signal has obvious advantages, the distance and speed matching is not needed, the detection performance of a weak signal target can be effectively improved, and a sawtooth wave FMCW signal system is selected. The signal waveform and hardware parameters of the FMCW array radar modulated by the sawtooth wave signal need to meet the following conditions:
(1) selecting a proper frequency modulation working bandwidth according to the requirement of distance measurement precision, wherein the relation between the distance resolution and the working bandwidth is as follows:
Figure PCTCN2019130437-APPB-000029
wherein d isresDenotes the range resolution, c denotes the speed of light, and B denotes the frequency-modulated operating bandwidth of the FMCW array radar. If the distance resolution needs to be within 1 meter, the frequency modulation operating bandwidth should be greater than 150 MHz.
(2) And selecting a proper frequency modulation period and a proper speed measurement period according to speed measurement requirements (including speed precision requirements and maximum target speed requirements) and signal wavelengths. For sawtooth FMCW signal modulation, the maximum measurable velocity and velocity resolution need to satisfy the following relationships, respectively:
Figure PCTCN2019130437-APPB-000030
Figure PCTCN2019130437-APPB-000031
wherein, VmaxIndicating the maximum measurable speed, VresRepresenting the velocity resolution, TcRepresenting a single frequency-modulated period, TfWhich represents a tacho period, typically comprising K frequency modulation periods. If the transmitted signal wavelength is 0.004 m, the maximum measurable velocity (relative radial velocity to radar) is not less than 50 m/s, and the velocity resolution is not greater than 1 m/s, then TcShould not be greater than 20 microseconds, TfShould be no less than 2 milliseconds.
(3) And selecting a proper sampling rate according to the maximum measuring distance and the frequency modulation working bandwidth. The sampling rate of the signal should satisfy the following relationship:
Figure PCTCN2019130437-APPB-000032
wherein, FsRepresenting the sampling rate, dmaxIndicating the maximum detectable distance, TcRepresenting a single frequency modulation period, c the speed of light, and B the frequency modulation operating bandwidth. If B is 200MHz, dmaxIs 200m, TcIs 20 microseconds, then FsShould not be less than 40/3 MHz.
And selecting proper array element number according to the angle measurement requirement. For conventional goniometry methods, the angular resolution satisfies the following relationship (rayleigh limit):
Figure PCTCN2019130437-APPB-000033
wherein, thetaresIndicating the angular resolution, N the number of elements, d the element spacing, and θ the angle of the target to the array normal (if the target is located at the array normal, θ is 0). If array element spacingIs half wavelength, the target is located at the array normal, the number of array elements is 8, and the angular resolution is approximately 14.324 deg.. For single-target angle measurement, the single-pulse angle measurement precision can reach 2% of the angle resolution under an ideal condition, certainly, many factors restrict the angle resolution in actual engineering, generally reach 10%, and the angle resolution can meet the requirements for some application scenes, but is not enough for vehicle-mounted application scenes. The high-resolution spatial spectrum estimation provides a solution for small-array high-resolution angle measurement, which can break through Rayleigh limit and obtain angle measurement precision far superior to that of the traditional angle measurement method.
The method is used for detecting the FMCW array radar motion multi-target weak signals under the strong clutter background, simulation verification shows that the number of array elements is 8, the target signal-to-noise ratio is 0dB, the noise-to-noise ratio is 15dB, the detection success probability of 100 Monte Carlo simulations reaches 100% under the comprehensive errors (the mutual coupling (between adjacent array elements) is 0.1+0.1i, the array element errors (within plus or minus 10%) and the amplitude phase inconsistency (within plus or minus 1dB and within plus or minus 10 degrees of phase)), and the mean deviation and the variance are respectively not more than 0.01 degrees and 0.02 degrees. The method can effectively detect the target (extremely low false alarm probability and false alarm probability when the signal-to-noise ratio is 0 dB) and has high angle measurement precision.
The embodiment of the invention also provides a device for detecting the FMCW array radar multi-target weak signals in motion under the strong clutter, wherein the FMCW array radar is modulated by sawtooth wave signals, as shown in figure 4, the device comprises:
and the Fourier transform module is used for respectively carrying out fast Fourier transform processing on the difference frequency signals of different array elements with different frequency modulation periods to obtain distance spectrums of different targets.
The input array difference frequency signal data comprises N array elements and K frequency modulation periods, and each frequency modulation period is provided with M sampling points. And respectively carrying out Fast Fourier Transform (FFT) processing on the K frequency modulation periods of the N array elements to obtain distance spectrums of different targets. The distance spectrum is 3-dimensional stereo data.
And the cancellation processing module is used for performing cancellation processing on the distance spectrum to eliminate clutter signals generated by a static target.
The clutter cancellation processing can eliminate clutter signals generated by the static targets, so that the number of echoes participating in subsequent operation is reduced, and the subsequent multi-moving target separation work is simplified.
The first detection module is used for carrying out first detection on the data subjected to the cancellation processing to obtain a first target; and the first detection adopts the constant false alarm rate detection of the ordered statistics.
The first detection module is specifically configured to:
determining a multiplication factor;
for each distance unit, setting a preset number of protection units and calculation units on two sides of the distance unit, and then sequencing the calculation units from large to small;
selecting a calculation unit with a preset sequence number to be multiplied by the product factor to serve as a threshold value for first detection;
and comparing the measured distance unit with the threshold value of the first detection by using a comparator, and determining the measured distance unit as a first target when the measured distance unit is larger than the threshold value.
The second detection module is used for carrying out second detection on the first target so that the number of second targets obtained after the second detection meets a preset condition; the number of the second targets is smaller than the number of the first targets.
The second detection is used for removing part of false targets. Since the requirements for constant false alarm detection are low for the first order statistic, it is possible that some false targets are mistaken for targets. And the range is further reduced by the second detection, and the false target is removed.
The second detection module is specifically configured to:
multiplying the mean value of the first target obtained by the first detection by a preset coefficient to obtain an initial threshold value of the second detection;
comparing the measured distance unit with the initial threshold value of the second detection by using a comparator, and determining the measured distance unit as a second target when the measured distance unit is larger than the initial threshold value of the second detection;
when the number of the second targets is higher than a preset condition, increasing the initial threshold value by a preset search step value to be used as a comparison threshold value of next detection search, and when the number of the second targets is lower than the preset condition, decreasing the initial threshold value by a preset search step value to be used as a comparison threshold value of next detection search; and repeating the comparison and judgment until the number of the second targets meets the preset condition.
When the data is not converged, that is, the number of the targets obtained by the second detection cannot meet the preset condition all the time, the preset search step value needs to be increased or decreased by half, which is helpful for the number of the second targets obtained by the second detection to meet the preset condition quickly.
And the third detection module is used for carrying out third detection on the second target to obtain a third target, carrying out space spectrum estimation by adopting array information in the third detection, and eliminating the false target by utilizing the difference of the real target and the false target space spectrum estimation peak value.
The third detection module is specifically configured to:
calculating the distance and speed of the second target.
Calculating the distance and the speed of the second target uses the following expression, wherein the expression is used for calculating the distance
Figure PCTCN2019130437-APPB-000034
Wherein c represents the speed of light, TcRepresenting a single frequency-modulated period, FsRepresenting the sampling rate, MbinFFT unit for indicating the location of the target distance, B signal bandwidth, NFFTRepresenting the number of distance FFT points.
Calculating speed by using expressions
Figure PCTCN2019130437-APPB-000035
Wherein, VbinFFT unit for representing target speed, f representing frequency of transmitted signal, and K representing speed measuring periodNumber of FM cycles (also the number of points of the velocity FFT), TfIndicating the speed measuring period (equal to KT)c). The above equation relates to the definition of velocity where the target is positive near the radar velocity.
A covariance matrix of the corresponding distance and velocity is calculated.
Here, two cases need to be distinguished: one condition is that no coherent signal exists (i.e. two or more objects have the same distance and the same speed), the array covariance matrix at this time can be obtained by using the data S of the original data S (three-dimensional stereo data: one frame of data has N array elements, each array element has K frequency modulation periods, each frequency modulation period has a plurality of sampling point data) through two FFT transformations (firstly, FFT is carried out on the sampling point data of a single frequency modulation period to obtain the distance information of the object; secondly, FFT is carried out on the K frequency modulation period data of the corresponding object distance on the basis of the first FFT to obtain the speed information of the object)FFT(N×K×N FFTDimension) is obtained, i.e. the data for finding the corresponding target range bin and velocity bin for each array element is arranged into a vector X ═ X according to the array element1 X 2 … X N] TWherein
X n＝S FFT(n,V bin,M bin),n＝1,2,···N；V bin＝1,2,···K,M bin＝1,2,···N FFT. At this time, the covariance matrix of the corresponding target is:
Figure PCTCN2019130437-APPB-000036
the superscript 'H' denotes the conjugate transpose.
In another case, there is a coherent signal, and the covariance matrix in this case needs to sum up the cell data of the range cell with the object without the velocity object, and X in this casenBecomes a vector (X)n＝S FFT(n,1:K,M bin) N is 1,2, … N), X is an N × K matrix, and the covariance matrix of the corresponding target is:
Figure PCTCN2019130437-APPB-000037
and respectively carrying out characteristic decomposition on the covariance matrixes of different targets.
For covariance matrix
Figure PCTCN2019130437-APPB-000038
Is subjected to characteristic decomposition of
Figure PCTCN2019130437-APPB-000039
Wherein, USIs a subspace spanned by the eigenvectors corresponding to the large eigenvalues, i.e. the signal subspace, and UNIs a subspace spanned by the feature vectors corresponding to the small feature values, i.e. the noise subspace. The estimation of the number of the information sources can be carried out by utilizing the characteristic values after the characteristic decomposition, and the correct estimation of the number of the information sources is the premise of the estimation of the space spectrum. Ideally the signal subspace and the noise subspace in the data space are orthogonal to each other, i.e. the steering vectors in the signal subspace are also orthogonal to the noise subspace: a isH(θ)U N＝0。
And (4) carrying out angle estimation and calculating the MUSIC spectrum by minimum optimization search.
The angle estimation here is carried out with a minimum optimization search, i.e.
Figure PCTCN2019130437-APPB-000040
The spatial spectrum estimation formula of the classical MUSIC algorithm is obtained as follows:
Figure PCTCN2019130437-APPB-000041
and judging whether the MUSIC spectrum is true or false, judging as a third target when the MUSIC spectrum peak is larger than a preset judgment threshold, and otherwise, judging as a non-target.
And (3) judging a true and false target of the MUSIC spectrum obtained by calculation: and if the MUSIC spectral peak is larger than a preset judgment threshold, judging as a third target, otherwise, judging as a non-target. The preset decision threshold can be set to be 5-10 times of the average value of all spectral peaks of the MUSIC through a large amount of data simulation and combination of measured data.
And the output module is used for outputting the distance, the speed and the angle information of the third target.
After three times of detection, false targets can be removed, and the distance, speed and angle of real targets can be obtained.
The frequency modulation working bandwidth of the FMCW array radar satisfies an expression
Figure PCTCN2019130437-APPB-000042
Wherein d isresRepresenting distance resolution, c representing light speed, and B representing frequency modulation working bandwidth; the single frequency modulation period of the FMCW array radar satisfies the expression
Figure PCTCN2019130437-APPB-000043
Wherein, VmaxIndicating the maximum measurable speed, TcRepresents a single frequency modulation period, and λ represents the transmitted signal wavelength; the speed measurement period of the FMCW array radar satisfies an expression
Figure PCTCN2019130437-APPB-000044
Wherein, VresRepresenting the velocity resolution, TfRepresenting a velocity measurement period, and lambda represents the wavelength of a transmitted signal; the sampling rate of the FMCW array radar satisfies an expression
Figure PCTCN2019130437-APPB-000045
Wherein, FsRepresenting the sampling rate, dmaxIndicating the maximum detectable distance, TcRepresenting a single frequency modulation period, c the speed of light, and B the frequency modulation operating bandwidth.
Fig. 5 is a specific block diagram of a computer device according to an embodiment of the present application, where the computer device 100 includes: one or more processors 101, a memory 102, and one or more computer programs, wherein the processors 101 and the memory 102 are connected by a bus, the one or more computer programs are stored in the memory 102 and configured to be executed by the one or more processors 101, and the processor 101 implements the steps of the FMCW array radar motion multi-target weak signal detection method under strong clutter as provided by an embodiment of the present application when executing the computer programs. The computer equipment comprises a server, a terminal and the like. The computer device may be a personal computer, a mobile terminal or a vehicle-mounted device, and the mobile terminal includes at least one of a mobile phone, a tablet computer, a personal digital assistant or a wearable device.
In order to achieve the above embodiments, the present invention further provides a storage medium for storing a computer program, which when run on a computer device, causes the computer device to execute the steps of the FMCW array radar motion multi-target weak signal detection method under strong clutter according to an embodiment of the present application.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (19)

  1. A FMCW array radar motion multi-target weak signal detection method under a strong clutter is characterized in that the FMCW array radar adopts sawtooth wave signal modulation, and the method comprises the following steps:
    respectively carrying out fast Fourier transform processing on difference frequency signals of different array elements with different frequency modulation periods to obtain distance spectrums of different targets;
    performing cancellation processing on the distance spectrum to eliminate clutter signals generated by a static target;
    carrying out first detection on the data subjected to cancellation processing to obtain a first target; the first detection adopts ordered statistic constant false alarm rate detection;
    carrying out second detection on the first target to enable the number of second targets obtained after the second detection to meet a preset condition; the number of the second targets is less than that of the first targets;
    carrying out third detection on the second target to obtain a third target, wherein the third detection adopts array information to carry out spatial spectrum estimation, and eliminates a false target by utilizing the difference of the real target and the false target spatial spectrum estimation peak value;
    and outputting the distance, speed and angle information of the third target.
  2. The method according to claim 1, wherein the first detecting specifically comprises:
    determining a multiplication factor;
    for each distance unit, setting a preset number of protection units and calculation units on two sides of the distance unit, and then sequencing the calculation units from large to small;
    selecting a calculation unit with a preset sequence number to be multiplied by the product factor to serve as a threshold value for first detection;
    and comparing the measured distance unit with the threshold value of the first detection by using a comparator, and determining the measured distance unit as a first target when the measured distance unit is larger than the threshold value.
  3. The method according to claim 1, wherein the second detecting specifically comprises:
    multiplying the mean value of the first target obtained by the first detection by a preset coefficient to obtain an initial threshold value of the second detection;
    comparing the measured distance unit with the initial threshold value of the second detection by using a comparator, and determining the measured distance unit as a second target when the measured distance unit is larger than the initial threshold value of the second detection;
    when the number of the second targets is higher than a preset condition, increasing the initial threshold value by a preset search step value to be used as a comparison threshold value of next detection search, and when the number of the second targets is lower than the preset condition, decreasing the initial threshold value by a preset search step value to be used as a comparison threshold value of next detection search; and repeating the comparison and judgment until the number of the second targets meets the preset condition.
  4. The method according to claim 1, wherein the third detecting specifically comprises:
    calculating the distance and the speed of the second target;
    calculating covariance matrices of corresponding distances and velocities;
    respectively carrying out characteristic decomposition on covariance matrixes of different targets;
    the angle estimation is realized by minimum optimization search and the MUSIC spectrum is calculated;
    and judging whether the MUSIC spectrum is true or false, judging as a third target when the MUSIC spectrum peak is larger than a preset judgment threshold, and otherwise, judging as a non-target.
  5. The method of claim 4, wherein the predetermined decision threshold is 5-10 times the average of all spectral peaks of MUSIC.
  6. The method of claim 4, wherein calculating the distance and velocity of the second target uses the expression, wherein calculating the distance uses the expression
    Figure PCTCN2019130437-APPB-100001
    Wherein c represents the speed of light, TcRepresenting a single frequency-modulated period, FsRepresenting the sampling rate, MbinFFT unit for indicating the location of the target distance, B signal bandwidth, NFFTRepresenting the number of distance FFT points; calculating speed by using expressions
    Figure PCTCN2019130437-APPB-100002
    Wherein, VbinAn FFT unit for representing the target speed, f represents the frequency of the transmitted signal, K represents the number of frequency modulation cycles of the tacho period, and TfIndicating the tachometer period.
  7. The method of claim 4, wherein the covariance matrix is evaluated
    Figure PCTCN2019130437-APPB-100003
    Is subjected to characteristic decomposition of
    Figure PCTCN2019130437-APPB-100004
    Wherein, USIs a signal subspace spanned by eigenvectors corresponding to large eigenvalues, and UNIs a noise subspace spanned by the feature vectors corresponding to the small feature values; and estimating the number of the information sources by using the characteristic values after the characteristic decomposition.
  8. The method of claim 4, wherein the angle estimation is performed with a minimum optimization search using an expression
    Figure PCTCN2019130437-APPB-100005
    Calculating MUSIC spectrum by adopting expression
    Figure PCTCN2019130437-APPB-100006
  9. The method of claim 1, wherein the FMCW array radar has a frequency modulated operating bandwidth satisfying an expression
    Figure PCTCN2019130437-APPB-100007
    Wherein d isresRepresenting distance resolution, c representing light speed, and B representing frequency modulation working bandwidth; the single frequency modulation period of the FMCW array radar satisfies the expression
    Figure PCTCN2019130437-APPB-100008
    Wherein, VmaxIndicating the maximum measurable speed, TcRepresents a single frequency modulation period, and λ represents the transmitted signal wavelength; the speed measuring period of the FMCW array radar satisfies an expression
    Figure PCTCN2019130437-APPB-100009
    Wherein, VresRepresenting the velocity resolution, TfRepresenting a velocity measurement period, and lambda represents the wavelength of a transmitted signal; the sampling rate of the FMCW array radar satisfies an expression
    Figure PCTCN2019130437-APPB-100010
    Wherein, FsRepresenting the sampling rate, dmaxIndicating the maximum detectable distance, TcRepresenting a single frequency modulation period, c the speed of light, and B the frequency modulation operating bandwidth.
  10. The FMCW array radar motion multi-target weak signal detection device under the strong clutter is characterized in that the FMCW array radar adopts sawtooth wave signal modulation, and the device comprises:
    the Fourier transform module is used for respectively carrying out fast Fourier transform processing on difference frequency signals of different array elements with different frequency modulation periods to obtain distance spectrums of different targets;
    the cancellation processing module is used for performing cancellation processing on the distance spectrum to eliminate clutter signals generated by a static target;
    the first detection module is used for carrying out first detection on the data subjected to the cancellation processing to obtain a first target; the first detection adopts ordered statistic constant false alarm rate detection;
    the second detection module is used for carrying out second detection on the first target so that the number of second targets obtained after the second detection meets a preset condition; the number of the second targets is less than that of the first targets;
    the third detection module is used for carrying out third detection on the second target to obtain a third target, the third detection adopts array information to carry out spatial spectrum estimation, and a false target is removed by utilizing the difference of the real target and the false target spatial spectrum estimation peak value;
    and the output module is used for outputting the distance, the speed and the angle information of the third target.
  11. The apparatus of claim 10, wherein the first detection module is specifically configured to:
    determining a multiplication factor;
    for each distance unit, setting a preset number of protection units and calculation units on two sides of the distance unit, and then sequencing the calculation units from large to small;
    selecting a calculation unit with a preset sequence number to be multiplied by the product factor to serve as a threshold value for first detection;
    and comparing the measured distance unit with the threshold value of the first detection by using a comparator, and determining the measured distance unit as a first target when the measured distance unit is larger than the threshold value.
  12. The apparatus of claim 10, wherein the second detection module is specifically configured to:
    multiplying the mean value of the first target obtained by the first detection by a preset coefficient to obtain an initial threshold value of the second detection;
    comparing the measured distance unit with the initial threshold value of the second detection by using a comparator, and determining the measured distance unit as a second target when the measured distance unit is larger than the initial threshold value of the second detection;
    when the number of the second targets is higher than the preset condition, increasing the initial threshold value by a preset search step value to be used as a comparison threshold value of next detection search, and when the number of the second targets is lower than the preset condition, decreasing the initial threshold value by a preset search step value to be used as a comparison threshold value of next detection search; and repeating the comparison and judgment until the number of the second targets meets the preset condition.
  13. The apparatus of claim 10, wherein the third detection module is specifically configured to:
    calculating the distance and the speed of the second target;
    calculating covariance matrices of corresponding distances and velocities;
    respectively carrying out characteristic decomposition on covariance matrixes of different targets;
    the angle estimation is realized by minimum optimization search and the MUSIC spectrum is calculated;
    and judging whether the MUSIC spectrum is true or false, judging as a third target when the MUSIC spectrum peak is larger than a preset judgment threshold, and otherwise, judging as a non-target.
  14. The apparatus of claim 10, wherein the predetermined decision threshold is 5-10 times the average of all spectral peaks of MUSIC.
  15. The apparatus of claim 10, wherein calculating the distance and velocity of the second target uses the following expression, and wherein calculating the distance uses the expression
    Figure PCTCN2019130437-APPB-100011
    Wherein c represents the speed of light, TcRepresenting a single frequency-modulated period, FsRepresenting the sampling rate, MbinFFT unit for indicating the location of the target distance, B signal bandwidth, NFFTRepresenting the number of distance FFT points; calculating speed by using expressions
    Figure PCTCN2019130437-APPB-100012
    Wherein, VbinAn FFT unit for representing the target speed, f represents the frequency of the transmitted signal, K represents the number of frequency modulation cycles of the tacho period, and TfIndicating the tachometer period.
  16. The apparatus of claim 10, wherein the covariance matrix is evaluated
    Figure PCTCN2019130437-APPB-100013
    Is subjected to characteristic decomposition of
    Figure PCTCN2019130437-APPB-100014
    Wherein, USIs a signal subspace spanned by eigenvectors corresponding to large eigenvalues, and UNIs a noise subspace spanned by the feature vectors corresponding to the small feature values; and estimating the number of the information sources by using the characteristic values after the characteristic decomposition.
  17. The apparatus of claim 10, wherein the angle estimation implemented with a minimum optimization search employs an expression
    Figure PCTCN2019130437-APPB-100015
    Calculating MUSIC spectrum by adopting expression
    Figure PCTCN2019130437-APPB-100016
  18. A computer device, comprising: one or more processors, a memory, and one or more computer programs, wherein the processors and the memory are connected by a bus, the one or more computer programs being stored in the memory and configured to be executed by the one or more processors, characterized in that the processor when executing the computer program implements the FMCW array radar motion multi-target weak signal detection method under strong clutter according to any one of claims 1 to 9.
  19. A storage medium storing a computer program, which when run on a computer device causes the computer device to perform the FMCW array radar motion multi-target weak signal detection method under strong clutter according to any one of claims 1 to 9.
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