CN113253236B - Rainy-day clutter suppression method based on millimeter-wave radar - Google Patents

Rainy-day clutter suppression method based on millimeter-wave radar Download PDF

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CN113253236B
CN113253236B CN202110767719.8A CN202110767719A CN113253236B CN 113253236 B CN113253236 B CN 113253236B CN 202110767719 A CN202110767719 A CN 202110767719A CN 113253236 B CN113253236 B CN 113253236B
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CN113253236A (en
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周树培
谢天
郭利庚
唐德琴
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Changsha Microbrain Intelligent Technology Co ltd
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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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/414—Discriminating targets with respect to background clutter
    • 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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/417—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section involving the use of neural networks

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Abstract

The invention discloses a method for suppressing clutter in rainy days based on a millimeter wave radar, which comprises the following steps: the millimeter wave radar carries out CFAR detection on the signals to obtain a point cloud data set; computing background checkMeasuring the variance value and the mean value of each frame point cloud set of the region; setting a sliding sign List, calculating the variance value of the echo amplitude of the point cloud within the number of sliding window frames, and if the variance value is greater than the threshold value<K 1Setting a sliding mark as a rainy day; if the variance value is 0, setting a sliding sign as a sunny day; the number of consecutive marks of the sliding sign as rainy days>K 2Then, the method enters a rainy background target detection mode, and the sliding marks are continuously marked as the number of sunny days>K 3And entering a fine day background target detection mode. According to the invention, through automatic identification in rainy days and sunny days, the target detection algorithm is flexibly adjusted, and the accuracy and stability of target detection are improved; the method has the advantages that the cloud point information of the background target point in the rainy day is extracted statistically, the accuracy of identification in the rainy day is effectively improved, the clutter in the rainy day is inhibited, and the false alarm probability in the rainy day is reduced.

Description

Rainy-day clutter suppression method based on millimeter-wave radar
Technical Field
The invention belongs to the technical field of radars, and particularly relates to a rainy-day clutter suppression method based on a millimeter-wave radar.
Background
Along with the rapid development of millimeter wave radar, on-vehicle, the wisdom traffic, unmanned aerial vehicle etc. each field is by wide application, the applied environment is also more complicated, especially apply very much in outdoor scene, the factor that faces influence target detection is also many, rainy day is exactly one of them comparatively big factor that influences, the millimeter wave radar produces the condition of false retrieval, missed retrieval easily in weather such as torrential rain, this brings serious interference for target detection, more serious probably causes radar information decay, there is great potential safety hazard. For example, in the use of wisdom traffic field, the radar service environment is comparatively complicated, and extremely strict to the stable detectability requirement of product, and weather such as torrential rain has greatly reduced the detection performance of millimeter wave radar to the target.
Disclosure of Invention
In view of this, the invention provides a method for suppressing clutter in rainy days based on a millimeter wave radar, which comprises the following steps:
the millimeter wave radar extracts echo information reflected after a transmitting signal meets a target to be detected, and the echo information is processed to obtain a range-Doppler heat map;
performing a CFAR detection algorithm on the distance-Doppler heat map to obtain an original point cloud, and performing angle estimation on the original point cloud to obtain a point cloud set Z;
setting a background detection area, and calculating the Z echo amplitude of the point cloud set of the current frame in the background detection area
Figure GDA0003238820030000011
Variance value of
Figure GDA0003238820030000012
Wherein i is a detection point number, and j is a frame number of the current frame;
setting a sliding mark List, storing the state mark of each frame in the sliding mark List, calculating the variance value of the point cloud echo amplitude within the sliding window frame number frameNum, if
Figure GDA0003238820030000021
Then in rainy days, where K1To preset the threshold, a sliding flag List [ k ] is set]Is a mark in rainy days; if it is not
Figure GDA0003238820030000022
Set slide flag List [ k ]]Setting sliding flag List [ k ] for clear day flag and other cases]And k is the frame number;
counting the number N of continuous marks in the sliding mark List in rainy days, and when N is larger than a set threshold K2When the target point is in the rainy background target detection mode, the target point echo amplitude P isjGreater than target detection threshold K4If the point target is a real target, otherwise, the point target is a clutter point;
counting the number N of consecutive marked sunny days in the sliding sign List1When N is present1Is greater than a set threshold K3And then entering a fine-day background target detection mode from the rainy-day background target detection mode.
Further, the step of processing the echo information to obtain a range-doppler thermal map comprises the steps of:
carrying out frequency mixing processing on the transmitting signal and the echo signal to generate a frequency mixing signal;
performing AD sampling on the mixing signal to generate a time domain sampling signal;
and performing one-dimensional fast Fourier transform on the time domain sampling information to obtain a one-dimensional range profile of the radar, and performing two-dimensional fast Fourier transform processing on the one-dimensional range profile to obtain a range-Doppler thermal map.
Further, the set background detection area has no point cloud data under the condition of sunny days.
Further, the echo amplitude P is calculated as follows:
Figure GDA0003238820030000023
where σ denotes the RCS value of the target, PtIs the maximum transmit power, G, of the RF front endRXIs the gain of the receiving antenna, GTXIs the transmit antenna gain, λ denotes the wavelength, TmeasRepresents the total measurement time of all chirp, d represents the radial distance, k represents the boltzmann constant, T represents the antenna temperature, and F represents the noise figure.
Further, the calculation process of the mean and variance of the echo amplitudes of each frame of point cloud data set Z is as follows:
Figure GDA0003238820030000031
calculating the variance of echo amplitude in the current point cloud data set and the background detection area
Figure GDA0003238820030000032
Wherein i is a detection point number, NPThe number of the point clouds in the current point cloud data set,
Figure GDA0003238820030000033
is the point cloud set Z echo amplitude.
Further, a target detection threshold K4Calculated as follows:
Figure GDA0003238820030000034
wherein N represents the number of signs meeting the rainy day, k represents a gain coefficient, and mu represents the average value of the amplitude of the background echo in the rainy day
Figure GDA0003238820030000035
μjThe mean value of the echo amplitudes of each frame of point cloud data set.
Further, detecting a target detection threshold value K according to the background data in rainy days4And adjusting, and setting different gain coefficients k for different rainfall magnitudes.
Further, a neural network is used to adjust the gain factor k.
Further, the target is identified by adopting a fixed amplitude limit in the fine-day background target detection mode.
Compared with the prior art, the invention has the following beneficial effects:
1) by automatic identification in rainy days and sunny days, the target detection algorithm is flexibly adjusted, and the accuracy and stability of target detection are improved.
2) The variance of the echo amplitude P of the target point cloud is adopted, and the target point cloud information of the rainy day background is statistically extracted through multi-frame data, so that the accuracy of rainy day identification is effectively improved, rainy day clutter is effectively inhibited, and the rainy day false alarm probability is reduced.
Drawings
FIG. 1 is a flowchart of the present invention for clutter suppression in rainy weather;
FIG. 2 is a diagram of the variance of the echo amplitude of the background point cloud in sunny and rainy days;
FIG. 3 is a diagram of echo amplitude variance of the target point cloud in rainy days;
fig. 4 is a diagram of the effect of the invention in rainy weather.
Detailed Description
The invention is further described with reference to the accompanying drawings, but the invention is not limited in any way, and any alterations or substitutions based on the teaching of the invention are within the scope of the invention.
The invention discloses a method for suppressing clutter in rainy days based on a millimeter wave radar, which comprises the following steps:
if the radar transmission signal e (t, n) is expressed as
e(t,n)＝A0·exp[j2πf0(t+nTc)+jKπt2]
Wherein f is0For radar transmission carrier frequency, T is fast time with each Chirp transmission time as a starting point, TcFor the modulation period of Chirp, K ═ B/TcThe Chirp rate in Chirp, B is the bandwidth of the transmitted signal, N is the Chirp index of the transmitted signal, N belongs to {0, 1, 2.., N-1}, and a frame of signals totally transmits N Chirps,
Figure GDA0003238820030000041
is a virtual unit.
S1: and extracting echo information reflected after the transmitting signal meets the target to be detected by the millimeter wave radar, and processing the echo information to obtain the range-Doppler heat map. S1 includes the steps of:
s10: the millimeter wave radar acquires echo information s (t, n, m) reflected by a target to be detected based on a transmitted signal;
Figure GDA0003238820030000051
where M is the number of targets (scattering points), AiRepresents the scattering intensity of the i-th scattering point to the millimeter wave, tauiFor the scattering two-way time delay of the ith scattering point, m ∈ {0, 2.. 7} is the radar receiving virtual array element number, and the virtual channel is 8 in consideration of the MIMO array of 2Tx4Rx adopted by the radar.
Assuming that the initial distance of the slow-speed moving target from the radar is R under the observation coordinate system with the radar as the originiAt the beginningRadial velocity viAnd the angle is theta with the normal direction of the radar antennaiThen at MTcWithin a very short frame time, if the slow moving target can be considered to move at a constant speed relative to the radar, the time delay tau of the ith target is determinediCan be expressed as
Figure GDA0003238820030000052
Here, d ═ λ/2 denotes the distance between the virtual array elements, and c denotes the speed of light.
S11: mixing the echo signal s (t, n, m) with the transmitting signal e (t, n) to generate an intermediate frequency signal (ignoring τ)iHigher order term of (2)
Figure GDA0003238820030000053
It can be seen that the intermediate frequency signal is a continuous time single frequency signal on each receive channel and each Chirp.
S12: AD sampling is carried out on the intermediate frequency signal to obtain a time domain sampling signal
Figure GDA0003238820030000054
Where k is 0, 1., L-1 denotes a time-domain sampling factor, w (k) denotes a thermal noise sequence, and the virtual array element interval d λ/2 is substituted into the above equation.
S13: one-dimensional fast Fourier transform (range FFT) is carried out on the time domain sampling signals x [ k, n, m ], and a one-dimensional range profile sequence of the radar is obtained as
Figure GDA0003238820030000061
Wherein N isFFT1The number of points representing the distance dimension FFT, k' 0, 1FFT1-1 is a distance unit index, and the Sinc envelope width of the distance image after one-dimensional FFT is known asDistance cell width, i.e. distance cell width ΔR＝c/(2KTL)＝c/(2B)。
S14: the millimeter wave radar performs two-dimensional fast Fourier transform on the one-dimensional Fourier transform processing result to obtain a range-Doppler heat map RD [ k ', n' ], which is expressed as
Figure GDA0003238820030000062
Here RD [ k ', n']＝|RD[k′，n′，0]|+|RD[k′，n′，1]|+L+|RD[k′，n′，7]And l is a two-dimensional matrix formed by a distance index and a Doppler index, and 8 virtual array element signals are subjected to non-coherent accumulation processing by the matrix, mainly for the purpose of further increasing the signal-to-noise ratio by channel signal accumulation. N' 0, 1FFT2-1 denotes a doppler cell index, and the doppler resolution cell width is Δ v ═ λ/(2 NT)c)＝λ/(2TF)。
S2: two-dimensional cell average constant false alarm detection (CA-CFAR) is performed on the range-Doppler image to obtain an original point cloud (range index)
Figure GDA0003238820030000063
And Doppler element index
Figure GDA0003238820030000064
)。
Carrying out angle estimation processing (angle FFT) on the original point cloud by using 8 virtual channel signals to obtain point cloud angle observation
Figure GDA0003238820030000065
At angle spectrum Y [ m'](m′＝0，1，2，...，NFFT3-1) searching peak points, wherein the corresponding index value is the azimuth angle thetaiIf the peak point corresponds to the subscript
Figure GDA0003238820030000071
The current point cloud information about the ith detection point is
Figure GDA0003238820030000072
And the target position information can be obtained as
Figure GDA0003238820030000073
Figure GDA0003238820030000074
Figure GDA0003238820030000075
Figure GDA0003238820030000076
Generally, the polar coordinate is converted into the rectangular coordinate, and then
Figure GDA0003238820030000077
Figure GDA0003238820030000078
Thus point cloud collection
Figure GDA0003238820030000079
Namely the target point cloud set.
S3: setting a background detection area, wherein the detection area takes a radar as an origin, the right front of the radar is a longitudinal axis Y, the left direction and the right direction are a transverse axis X in a horizontal direction, and meanwhile, the background detection area needs to be ensured to be clean, namely no point cloud data exists in a non-rainy day, so that the point cloud data variance of the background detection area is 0 in a sunny day;
s4: calculating the echo amplitude of the cloud set Z of the current frame (with the frame number of j) points in the background detection area
Figure GDA00032388200300000710
Variance value
Figure GDA00032388200300000711
And a mean value;
the calculation process of the mean value and the variance of the echo amplitude of each frame of point cloud data set is as follows:
Figure GDA00032388200300000712
calculating the variance of echo amplitude in the current point cloud data set and the background detection area
Figure GDA00032388200300000713
S5: counting the echo amplitude of the target point cloud in rainy days in the frame number frameNum of the sliding window
Figure GDA0003238820030000081
The variance value of the difference between the two values,
Figure GDA0003238820030000082
then in rainy days, where K1Recording the rainy day mark into a sliding mark List for a preset threshold value; if it is not
Figure GDA0003238820030000083
If the background detection area has no point cloud data, the background detection area is a clear day, and the clear day mark is recorded into the sliding mark List;
s6: counting the number N of frames satisfying the rainy day mark in the sliding mark List, and when N is larger than a set threshold value K2Then, the method enters a rainy background target detection mode, and calculates the average value of the echo amplitude meeting the rainy background frame number as a target detection threshold value K4(ii) a By counting the number of frames N of the satisfied condition flags in the sliding flag List1When N is present1Is greater than a set threshold K3Entering a fine-day background target detection mode from a rainy-day background target detection mode;
in the rainy background detection mode, the echo amplitude P of the target pointj>K4The point target is a real target, otherwise, the point target is a clutter point.
In a clear-sky background target detection mode, a fixed amplitude limiting method is used for identifying a target (when the amplitude of a target echo exceeds a set threshold value, the threshold value cannot be adjusted in a self-adaptive mode).
Under the rainy background detection mode, K can be detected in real time according to the rainy background data4Adjusting by adopting different K for different rainfall4A threshold value;
target detection threshold K4The calculation is as follows:
average value of amplitude of background echo in rainy day
Figure GDA0003238820030000084
In the formula: n represents the number of signs meeting the rainy day;
target detection threshold
Figure GDA0003238820030000085
The invention adopts the neural network to adjust the gain coefficient k so as to improve the accuracy of target identification under different rainfall conditions. For different storm levels: and the blue early warning, the yellow early warning, the orange early warning and the red early warning are respectively provided with different gain coefficients k. The embodiment trains the gain coefficient k by using a feedforward neural network, and comprises an input layer, two hidden layers and an output layer. The target echo amplitude average value in rainy days is input, 4 gain coefficient levels k1, k2, k3 and k4 are output, and the gain coefficients respectively correspond to the gain coefficients in rainy days of blue early warning, yellow early warning, orange early warning and red early warning.
The feedforward neural network forwards transmits the excitation, the weight and the offset of each layer to finally obtain an expected value, then obtains a residual value through a label value and the expected value, the magnitude of the residual value reflects the deviation degree of the expected value and the residual value, then uses a back propagation algorithm to perform gradient solution on the forward formula of the previous layer, then substitutes each variable x to obtain the weight w' corresponding to the current layer of each variable x, then sequentially performs back propagation to the previous layer, finally reaches the input layer to obtain the deviation value of the weight w corresponding to each layer, then sets a learning rate to set the magnitude of parameter updating to achieve the updating of the parameters, and then adjusts the w and b parameters through 4 iterations. In this embodiment, the learning rate is 0.001, and b is a fixed value. The activation function is a sigmoid function:
Figure GDA0003238820030000091
fig. 2 is a diagram of echo amplitude variance of a background point cloud in a sunny and rainy day, in which the echo amplitude variance of the background point cloud in the sunny day is 0, and the echo amplitude variance of the background point cloud in the rainy day is greater than 0 and fluctuates within a certain amplitude range. FIG. 3 is an echo amplitude variance plot in the presence of a target point in a rainy day when the echo amplitude variance of the target point cloud is greater than a threshold K1The time mark is a mark in rainy days, when K is continuously present2(K2A preset threshold) of the plurality of rainy day marks, entering a rainy day background target detection mode. FIG. 4 is a diagram of the detection effect of target detection in rainy days, after entering the background target detection mode in rainy days, the variance of the echo amplitude of the target point cloud is greater than the threshold K4Then, identifying the target, if the echo amplitude variance of the target point cloud is less than the threshold K4It is regarded as noise filtering. The invention can effectively inhibit the clutter in the rainy day and reduce the false alarm probability in the rainy day.
The invention has the following beneficial effects:
by automatic identification in rainy days and sunny days, the target detection algorithm is flexibly adjusted, and the accuracy and stability of target detection are improved.
The variance of the echo amplitude Z of the target point cloud is adopted, and the target point cloud information of the rainy day background is statistically extracted through multi-frame data, so that the accuracy of rainy day identification is effectively improved, rainy day clutter is effectively inhibited, and the rainy day false alarm probability is reduced.
The above embodiment is an embodiment of the present invention, but the embodiment of the present invention is not limited by the above embodiment, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be regarded as equivalent replacements within the protection scope of the present invention.

Claims (9)

1. A method for suppressing clutter in rainy days based on a millimeter wave radar is characterized by comprising the following steps:
the millimeter wave radar extracts echo information reflected after a transmitting signal meets a target to be detected, and the echo information is processed to obtain a range-Doppler heat map;
performing a CFAR detection algorithm on the distance-Doppler heat map to obtain an original point cloud, and performing angle estimation on the original point cloud to obtain a point cloud set Z;
setting a background detection area, and calculating the Z echo amplitude of the point cloud set of the current frame in the background detection area
Figure FDA0003238820020000011
Variance value of
Figure FDA0003238820020000012
Wherein i is a detection point number, and j is a frame number of the current frame;
setting a sliding mark List, storing the state mark of each frame in the sliding mark List, calculating the variance value of the point cloud echo amplitude within the sliding window frame number frameNum, if
Figure FDA0003238820020000013
Then in rainy days, where K1To preset the threshold, a sliding flag List [ k ] is set]Is a mark in rainy days; if it is not
Figure FDA0003238820020000014
Set slide flag List [ k ]]Setting sliding flag List [ k ] for clear day flag and other cases]And k is the frame number;
counting the number N of continuous marks in the sliding mark List in rainy days, and when N is larger than a set threshold K2When the target point is in the rainy background target detection mode, the target point echo amplitude P isjGreater than target detection threshold K4If the point target is a real target, otherwise, the point target is a clutter point;
counting the number N of consecutive marked sunny days in the sliding sign List1When N is present1If the target detection time is larger than the set threshold K3, the method enters a sunny background target detection mode from a rainy background target detection mode.
2. The millimeter wave radar-based method for suppressing clutter in a rainy day according to claim 1, wherein the step of processing the echo information to obtain a range-doppler heat map comprises the steps of:
carrying out frequency mixing processing on the transmitting signal and the echo signal to generate a frequency mixing signal;
performing AD sampling on the mixing signal to generate a time domain sampling signal;
and performing one-dimensional fast Fourier transform on the time domain sampling information to obtain a one-dimensional range profile of the radar, and performing two-dimensional fast Fourier transform processing on the one-dimensional range profile to obtain a range-Doppler thermal map.
3. The method for suppressing the clutter in the rainy day based on the millimeter wave radar of claim 1, wherein the background detection area is set without point cloud data in a sunny day.
4. The method for suppressing the clutter in the rainy day based on the millimeter wave radar of claim 1, wherein the echo amplitude P is calculated as follows:
Figure FDA0003238820020000021
where σ denotes the RCS value of the target, PtIs the maximum transmit power, G, of the RF front endRXIs the gain of the receiving antenna, GTXIs the transmit antenna gain, λ denotes the wavelength, TmeasRepresents the total measurement time of all chirp, d represents the radial distance, k represents the boltzmann constant, T represents the antenna temperature, and F represents the noise figure.
5. The method for suppressing the clutter in the rainy day based on the millimeter wave radar of claim 1, wherein the calculation process of the mean and the variance of the echo amplitude of each frame of the point cloud data set Z is as follows:
Figure FDA0003238820020000022
calculating the variance of echo amplitude in the current point cloud data set and the background detection area
Figure FDA0003238820020000023
Wherein i is a detection point number, NPThe number of the point clouds in the current point cloud data set,
Figure FDA0003238820020000024
is the point cloud set Z echo amplitude.
6. The method for suppressing clutter in rainy days based on millimeter wave radar according to claim 1, wherein the target detection threshold K is4Calculated as follows:
Figure FDA0003238820020000031
wherein N represents the number of signs meeting the rainy day, k represents a gain coefficient, and mu represents the average value of the amplitude of the background echo in the rainy day
Figure FDA0003238820020000032
μjThe mean value of the echo amplitudes of each frame of point cloud data set.
7. The method for suppressing clutter in rainy days based on millimeter wave radar according to any of claims 1 or 4, wherein the threshold value K for target detection is detected based on the background data of rainy days4And adjusting, and setting different gain coefficients k for different rainfall magnitudes.
8. The method for suppressing the clutter in the rainy day based on the millimeter wave radar of claim 7, wherein the gain coefficient k is adjusted by using a neural network, and the trained gain coefficient k corresponds to the gain coefficients of the blue early warning, the yellow early warning, the orange early warning and the red early warning in the rainy day respectively.
9. The method for suppressing clutter in a rainy day based on millimeter wave radar of claim 1, wherein the target is identified by a fixed clipping method in the sunny background target detection mode.
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