CN114609626A - Multi-target detection method for vehicle-mounted millimeter wave radar - Google Patents
Multi-target detection method for vehicle-mounted millimeter wave radar Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- 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
- G01S13/88—Radar or analogous systems specially adapted for specific applications
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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
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
- G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
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- G06F18/23—Clustering techniques
Abstract
The invention discloses a multi-target detection method suitable for the field of vehicle-mounted millimeter wave radar target detection, which aims to solve the problems of multi-target azimuth angle measurement limitation and multi-target angle parameter matching under the condition of insufficient radar receiving array element number, and the main technical scheme comprises the following steps: acquiring millimeter wave radar A/D sampling data; rearranging the multi-array element A/D sampling data into three-dimensional matrix data; performing two-dimensional FFT processing on the A/D sampling data in a distance dimension and a speed dimension, and storing a distance dimension FFT result; obtaining target two-dimensional matrix data by using GO-CFAR; DBSCAN density clustering is carried out on the detected two-dimensional motion parameters of the target points, the maximum peak value point in each category set after clustering is taken as a detection target corresponding to the set, and the speed distance information of a plurality of targets is obtained; extracting a multi-array element beat signal corresponding to each target from the distance dimension FFT result; and carrying out MUSIC algorithm on the beat signal corresponding to each target to estimate the azimuth angle corresponding to the target, and outputting the azimuth angle matched with the target speed distance information.
Description
Technical Field
The invention relates to a multi-target detection technology of a millimeter wave radar, belonging to radar signal processing and target detection technologies.
Background
The multi-target detection has extremely high application value in the fields of traffic control, security monitoring, intelligent driving and the like. In practical application, multi-target detection must have the ability of flexible target detection and rapid and accurate target information extraction. Therefore, the multi-target detection algorithm cannot be too complex, the complexity of calculation time is reduced as much as possible, and the actually existing targets and motion parameters can be detected in a self-adaptive manner on the premise that the number of the targets is unknown.
The millimeter wave radar has the advantages of small size, light weight, high detection precision, stable performance in severe weather, easiness in maintenance and the like, can provide stable and effective target detection and assists a vehicle in avoiding an obstacle target. However, the traditional multi-target detection method based on the millimeter wave radar has the problems of target missing detection, incapability of further extracting and matching azimuth information of the target and the like, and is difficult to flexibly and accurately sense the target in a complex sensing environment.
Due to the limitations of the radar system and the complexity of the environment, the peak value corresponding to each target is not a single peak value but a set of peak values, and when the azimuth angles of the targets are too close, the targets are overlapped, and target missing detection is generated. The DBSCAN clustering algorithm provides a density-based clustering algorithm, and the type of the sample is determined according to the compactness of the sample distribution, so that the peak value sets of two targets are adaptively distinguished, and the problem of target omission caused by target overlapping is reduced.
The existing scheme 1 (with the publication number of CN109975807) provides a dimension reduction subspace angle measurement method based on a millimeter wave vehicle-mounted radar. The existing scheme 1 adopts a beam domain MUSIC algorithm and a new MUSIC estimator to perform azimuth angle measurement on a target.
The main technical solutions of prior art scheme 1 are briefly described as follows:
(1) and establishing a space spectrum estimation mathematical model of the millimeter wave vehicle-mounted radar system to obtain expressions of the transmitting signal and the receiving signal.
(2) And (2) establishing a three-dimensional data structure for expanding the intermediate frequency signal after mixing the received signal under the condition of multi-receiving on the basis of (1).
(3) FFT is respectively carried out on the three-dimensional received signal data in a fast time dimension and a slow time dimension to obtain target speed distance information and a received signal data vector Y of an antenna array on a corresponding distance Doppler unit.
(4) And calculating an optimized wave beam by using the information of the azimuth angle range of the vehicle to be detected by the vehicle-mounted radar, and converting the received signal data vector Y from the array element domain to the wave beam domain.
(5) And calculating a sample covariance matrix by using the beam domain received signal matrix.
(6) And (3) decomposing the eigenvalue of the sample covariance matrix to obtain a noise subspace, and establishing an MUSIC spatial spectrum function based on the noise subspace to search a spectrum peak to obtain an azimuth angle estimated value.
The existing scheme 1 mainly aims to provide a new MUSIC estimator, so that the signal subspace expression is more refined, and the excellent angle measurement performance is maintained under the conditions of low calculation complexity and memory occupation. But only the angle estimation optimization of multiple targets is completed, and the target angle and the speed distance parameter can not be matched for the multiple targets.
In the prior scheme 2, under the condition of single snapshot number, the prior information is utilized to optimize the beam forming matrix, the mathematical expression of the covariance matrix of the received signal estimation sample is modified, the estimation precision of the covariance matrix of the sample is improved,
the prior art 2 (publication number CN111856420A) proposes a doppler radar multi-target detection method. In the conventional scheme 2, multiple targets are detected by combining two-dimensional FFT, amplitude accumulation and center peak traversal.
The main technical solutions of prior art scheme 2 are briefly described as follows:
(1) and acquiring echo signal data A of the moving target.
(2) Windowing and distance dimension FFT (fast Fourier transform) are carried out on the data A to obtain a frequency domain data matrix B;
(3) performing speed dimension FFT on the data B and performing modulus taking to obtain a Doppler spectrum C;
(4) accumulating the Doppler spectrum C data in the same distance direction to obtain a sequence D;
(5) and setting a threshold value, and performing iterative traversal D according to a preset peak value center range to find a plurality of targets.
In the existing scheme 2, a plurality of targets are searched in an iterative traversal manner based on the same distance accumulation, so that the calculation amount of target detection is reduced.
The existing scheme 2 mainly aims at solving the problems of high computation complexity and high false alarm probability of Doppler radar multi-target detection. The threshold setting for detecting multiple targets has no flexibility, and flexible multiple target detection cannot be provided, so that the problem of target omission under certain conditions is caused. And further azimuth measurements of multiple targets cannot be made.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method for realizing self-adaptive multi-target identification and multi-target azimuth angle measurement and matching under the condition of insufficient element number of a millimeter wave radar receiving array.
The invention adopts the technical scheme that the multi-target detection method of the vehicle-mounted millimeter wave radar comprises the following steps:
and 7, carrying out multi-signal classification MUSIC algorithm processing on the multi-array element beat signals corresponding to each target, estimating the azimuth angle corresponding to the target, and outputting the azimuth angle in a matching manner with the speed, distance parameters and azimuth angle of the target, thereby completing multi-target detection.
The invention has the beneficial effects that:
1) according to the characteristics of the actually measured data, the two-dimensional FFT result is subjected to averaging processing, and clutter is effectively removed.
2) By adopting the DBSCAN clustering algorithm, the number of targets does not need to be preset, the current data detection target number is obtained in a self-adaptive manner, and the point with the maximum data value in each target cluster is used as the target point of the target cluster, so that the target positioning precision is improved.
3) And carrying out azimuth angle measurement by using MUSIC estimation operators which are in one-to-one correspondence with the target points obtained by clustering, so that the angle output by the MUSIC can be matched with the target points. The method is favorable for providing accurate and perfect target parameters for a subsequent fusion processing algorithm.
Under the condition of insufficient element number of a millimeter wave radar receiving array, the method can realize self-adaptive multi-target identification and multi-target azimuth angle measurement and matching, solves the problem of target angle overlapping of the conventional multi-target detection, has good generalization capability and is convenient for engineering realization.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of an averaging and GO-CFAR detector configuration;
FIG. 3 is a GO-CFAR detection diagram;
FIG. 4 is a SO-CFAR detection diagram;
FIG. 5 is a CA-CFAR detection map;
FIG. 6 is a graph of MUSIC function spectrum corresponding to GO-CFAR clustering target.
Detailed Description
Abbreviations and Key Definitions
FFT (Fast Fourier Transform)
GO-CFAR (great of Constant False Alarm Rate, maximum choice Constant False Alarm Rate)
PRI (Pulse Repetition Period)
DBSCAN (sensitivity-Based Spatial Clustering of Applications with Noise response Based on Spatial Clustering of densities)
MUSIC (Multiple Signal Classification )
The example uses a chirped continuous wave LFWCW signal as the radar transmit signal, with a pulse repetition period PRI number of M. The target of the embodiment is actually measured pedestrians, and the pedestrians are used as an integral point target to be subjected to target detection.
FIG. 1 is a flow diagram of the method of the invention, which comprises radar echo beat data acquisition and reconstruction, a radar signal algorithm processing module, and target detection parameter matching output, wherein the radar signal algorithm processing module mainly comprises two-dimensional FFT, GO-CFAR detection, DBSCAN clustering, and MUSIC estimation.
According to fig. 1, 7 main parts are summarized:
1. and acquiring millimeter wave radar A/D sampling data, namely sampling the radar beat echo signal S (t). And in M pulse repetition periods, the transmitting signal of the sawtooth wave frequency modulation continuous wave radar is used as a local oscillation signal, the local oscillation signal is mixed with the original echo signal received by the receiving antenna, and a beat signal is obtained after mixing.
Suppose a transmit signal within a Pulse Repetition Interval (PRI) of T transmit Repetition Interval (m +1) with mT ≦ T is recorded as:
wherein A is0Is the signal amplitude, f0Is the carrier frequency of the signal and,
is the initial phase of the signal, u-B/T is the chirp rate, m is the mth period, T is the period lengthAnd B is the bandwidth. If there is a little target distance radar R at the initial time (t ═ 0)0And approaches the radar at a radial velocity v. Let the speed of light be c and the initial time delay be tau0＝2R0And c, the echo time delay corresponding to the time t is as follows:
FMCW echo signal S at time tR,m(t) is:
(3) wherein eta is the target reflection coefficient, and tau is the time delay of t moment; mixing the formula (1) and the formula (3) to obtain a beat signal S at the time tB,m(t) the following:
SB,m(t)＝0.5ηA0 2 exp{j[2π(f0τ(t)-0.5uτ2(t)+uτ(t)·(t-mT))]} (4)
exp is an exponential function with a natural constant e as the base.
Beat signal SB,mAnd (t) obtaining A/D sampling data after sampling.
2. And rearranging the multi-array element A/D sampling data into three-dimensional matrix data.
A commonly used Linear Frequency Modulation Continuous Wave (LFMCW) signal processing method in engineering implementation is to sample beat signals according to a repetition period, set up N points for each period sampling, and continuously acquire M repetition periods, perform N-point FFT (distance dimension FFT) on N point sampling data in each repetition period once, that is, separate radar detection targets located in different distance units in the distance dimension; and performing M-point FFT (velocity dimension FFT) on the M-point sampling data belonging to the same distance unit in the distance dimension FFT result once, so that different Doppler (or velocity) units in the same distance unit can be distinguished.
Let x (t) be a continuous time signal, which is sampled to obtain xm(n), the nth sampling point of the mth period is shown. For x (t) continuous sampling M repeated cyclesAnd each repetition period samples N points, namely M is 0,1, …, M-1, N is 0,1, … and N-1. Obtaining a discrete Fourier transform X of echo signals of each repetition period m from a continuous signal X (t)m(i)。
The result after FFT processing for each row of range dimension is the frequency amplitude of the beat signal in response to different range bins, and can also be regarded as the result after filtering by the doppler filter. In different sampling periods, the phase of the frequency response coefficient corresponding to the same distance unit only has time delay, and the delay coefficients are the same. The range dimension FFT may be viewed as separating radar samples located at different range bins. I.e. the frequency response coefficients corresponding to the same range unit, are the sampling results of a single signal source.
To Xm(i) The result X (l, i) after the two-dimensional FFT operation is performed by performing a discrete fourier transform on M-point sequences that are in the same range bin but do not belong to the same repetition period, i denotes a range bin number, l denotes a doppler bin number, i is 0,1, …, N-1, and l is 0,1, …, N-1.
3. Performing two-dimensional FFT processing on the A/D sampling data in a distance dimension and a speed dimension, and storing a distance dimension FFT result; and the two-dimensional FFT respectively is a distance dimensional FFT and a speed dimensional FFT corresponding to beat signal data received by each echo receiving array element to obtain two-dimensional matrix data.
4. Carrying out averaging processing on the two-dimensional matrix data, and acquiring moving target motion parameters by using GO-CFAR; and the GO-CFAR detection is to perform target detection on the two-dimensional matrix data after the two-dimensional FFT processing, detect whether a target exists and mark the target. When the invention analyzes the measured data, a selected large cell average (GO-CFAR) detector is adopted, and the structure is shown in figure 2.
D is the data of the detected unit and outputs the data to one input end of the comparator, and two sides of the comparator are protection units; x is the number of1,x2,…,xNAnd y1,y2,…,yNDistance dimension FFT data and speed dimension FFT data which are used as sample data and correspond to the reference units on the two sides respectively; x is a radical of a fluorine atom1,x2,…,xNAnd y1,y2,…,yNRespectively carrying out equalization processing and outputting to a large selection unit, wherein the large selection unit inputsSelecting a group of data x with larger averaging from the 2 groups of dataiTo make a noise power beta2The calculation of (2) is carried out, and then the calculation is multiplied by an input detector threshold scale factor alpha to obtain a detection threshold estimation value
Then the data D is output to the other input end of the comparator, and the comparator compares the data D of the detected unit with the detection threshold estimated value
Is large or small, if
Then the situation H indicating the existence of the target1Otherwise, it is the case of no target H0And finally outputting the two-dimensional matrix data with the target.
The judgment criterion of GO-CFAR detection is as follows:
wherein H0Indicates no target, H1Indicating the presence of a target, alpha is a detector threshold scale factor,
is an interference power estimate. Assuming that the interference noise is independently and equally distributed and the noise power is beta2Data x in reference cells when using square rate detectorsiObeying an exponential distribution, xiHas a probability density function of
Since the interference of each reference unit is independently and equally distributed, the joint probability density function of the observation vector x composed of the adjacent N sample data is
Taking the logarithm can obtain the log-likelihood function equivalent to the logarithm
In the formula, beta2Is equal to 0, then
The noise power can be obtained by the formula (8)
Is estimated as
The estimated noise power alpha is multiplied by the scale factor alpha to obtain the estimated value of the check threshold
The final check threshold expression obtained by substituting equation (9) into equation (10)
Let zi＝αxiZ is obtained from the formulae (6) and (11)iProbability density function of
The check threshold estimate may be further derived
Probability density function of
Is composed of
Due to false alarm probability
Then its mathematical expectation
Is composed of
The final result of the mathematical expectation of the false alarm probability can be solved by equation (14)
Then the scale factor of the detector threshold can be obtained from equation (15)
It can be seen from equation (15) that the average false alarm probability is independent of the actual noise power, and the unit average detection method has a constant false alarm characteristic. When the GO-CFAR detector is used for experiments, the estimated value of the noise power can be measured through the formula (9) according to the sampling data of the reference unit; for a detector given a false alarm probability, a threshold scale factor can be obtained from equation (16); on the basis of this, the threshold value of the detector can be calculated. In the experiment based on the measured data, two units, i.e., the left and right, of the detection unit are selected as protection units, the number N of reference units is 8, and after the false alarm probability is given to be 0.04, the scale factor α is calculated to be 4.
5. For detected targeted two-dimensional matrix data x1,x2,…,xMM represents data in which a target is present; and performing DBSCAN density clustering, and after clustering, taking the maximum peak point in each category set as a detection target corresponding to the set, acquiring the speed distance information of a plurality of targets, and simultaneously obtaining the distance gate of the moving target.
The DBSCAN clustering is based on the two-dimensional matrix data with the targets after GO-CFAR detection, multiple pairs of speed distance unit coordinates (i, l) corresponding to each target are used as independent classes by utilizing the DBSCAN clustering, sample division is carried out, multiple clustering classes are obtained, and each class corresponds to one target.
The specific treatment method comprises the following steps:
inputting: sample set D ═ x1,x2,…,xM) Each element in the sample set D is two-dimensional distance dimension FFT data and velocity dimension FFT data; neighborhood parameters (epsilon, MinPts) and epsilon are domain distance thresholds, and MinPts is a sample number threshold;
and (3) outputting: and C, cluster division.
1) Initializing a set of core objects
Initializing cluster number k equal to 0, initializing unvisited sample set Γ equal to D, and clustering
2) For j 1, 2.. times.m, all core objects are found by the following steps:
a) passing distanceFrom metric approach, find sample xjE-neighborhood subsample set N∈(xj)
b) If the number of the sub-sample set samples meets the requirement of N∈(xj) | ≧ MinPts, sample xjAdding a core object sample set:
Ω＝Ω∪{xj} (17)
3) if core object set
The algorithm ends, otherwise step 4) is carried out.
4) In a core object set omega, a core object o is randomly selected, and a current cluster core object queue omega is initializedcurK +1, C, the initialization class numberkUpdate the set of unaccessed samples Γ ═ Γ - { o }
5) If the current cluster core object queue
Then the current cluster C is clusteredkAfter generation, the cluster partition C is updated to { C ═ C1,C2,…,CkAnd updating a core object set omega-CkAnd (5) turning to the step 3. Otherwise, updating the core set object omega-Ck。
6) In the current cluster core object queue omegacurTaking out a core object o ', finding out all the belonged-neighborhood subsample sets N belonged (o') by using the domain distance threshold belonged to, and enabling delta to be N∈(o') # Γ, updating the current cluster sample set Ck＝CkAnd E, updating the unaccessed sample set gamma-delta, updating and turning to the step 5.
The output result is: class cluster C ═ { C1,C2,…,Ck}。
And according to the output clusters, carrying out coordinate element maximum peak value search on each category to serve as a target point corresponding to the category.
And extracting a distance dimension FFT result corresponding to different receiving array elements to form a two-dimensional matrix, and extracting a distance antenna dimension matrix vector corresponding to the three-dimensional structure data after one-dimensional FFT processing according to the clustered target point. It should be further noted that the data vector ensures that the obtained unique angle peak corresponds to the target after being processed by the MUSIC algorithm, so that the speed distance and the angle of the target are matched one by one, and the limit of the target azimuth angle detection number of the traditional MUSIC algorithm is broken through.
6. And 3, extracting a multi-array element beat signal vector corresponding to each target based on the distance dimension FFT result stored in the step 3. And (3) calculating the distance unit as i distance dimension Fourier transform coefficients of the target speed and the distance unit coordinates (i, l) as single signal source sampling data of the target at the distance unit i. For the signal source data, the phase difference between echo beat signals received by a plurality of antennas is utilized to estimate the azimuth angle, and the purpose of matching the same distance with the unique azimuth angle is achieved.
7. And carrying out MUSIC algorithm processing on the beat signal corresponding to each target, estimating the azimuth angle corresponding to the target, and outputting the azimuth angle matched with the target distance and the speed parameter.
The MUSIC algorithm is used for measuring azimuth angles by utilizing phase differences among echo beat signals received by a plurality of antennas. The beat signals with the array source number of L and the target number of 1 can be processed.
The specific treatment method comprises the following steps:
according to the signal source data (N receiving signal vectors X (i)) obtained in the step 6, calculating to obtain an estimated value of the following covariance matrix:
performing characteristic decomposition on the covariance matrix obtained above
Rx＝ARsAH+σ2I (19)
A is an array direction matrix, RsFor signal correlation matrix, σ2To be noise power, I is an L × L order identity matrix.
R is to bexAfter the characteristic decomposition, sorting according to the size of the characteristic values, and sorting the maximum characteristic value and the corresponding characteristic valueFeature vector v1Taking the rest L-1 eigenvalues and eigenvectors as a signal partial space, and obtaining a noise matrix En
AHvi＝0, i＝2,3,…,L (20)
En＝[v2,v3,…vL] (21)
Varying theta according to formula
a (θ) is an L-dimensional array vector, EnIs a noise matrix.
And calculating a spectrum function, and obtaining an estimated value theta of the azimuth angle unique to the target by seeking the maximum peak value.
To further illustrate the benefits of the present invention, the figures are presented in conjunction with experimental results.
Experiment 1: collecting multi-target radar echo data, wherein relevant radar parameters are shown in table 1, and obtaining different CFAR detection graphs, wherein GO-CFAR, CA-CFAR and SO-CFAR are respectively compared. As shown in the figures 3, 4 and 5, under the same frame data, after GO-CFAR and SO-CFAR are filtered and clustered, the target display is correct, but the clutter suppression effect of GO-CFAR is more obvious, the SO-CFAR effect is poorer, and the target multi-detection condition exists in CA-CFAR. Statistics of multi-frame comparison and three kinds of comparison show that the GO-CFAR has the best detection effect on the actually measured data.
TABLE 1 Radar parameters
Experiment 2: and outputting a speed and distance two-dimensional parameter result by using the GO-CFAR, performing DBSCAN clustering on the speed and distance two-dimensional parameter result, obtaining the target number in a self-adaptive manner, removing three clutter points fixed on a central line according to a clustering result shown in figure 2, detecting 4 targets, and labeling the clustered target number.
Experiment 3: based on the multiple targets detected by clustering in experiment 2, performing MUSIC algorithm processing on the one-dimensional FFT array element dimensional data corresponding to each target, outputting a spectrum function corresponding to each target, and comparing the spectrum function with a clustering detection graph, as shown in fig. 6, each target point is paired with a spectrum function, each spectrum function can uniquely identify a spectrum peak corresponding to the azimuth angle of the target.
Claims (3)
1. A multi-target detection method for a vehicle-mounted millimeter wave radar is characterized by comprising the following steps:
step 1, acquiring A/D sampling data of a multi-array element millimeter wave radar;
step 2, rearranging multi-array element millimeter wave radar A/D sampling data into three-dimensional matrix data which are respectively in a distance dimension, a speed dimension and an array element dimension;
step 3, performing two-dimensional Fast Fourier Transform (FFT) on the three-dimensional matrix data in a distance dimension and a speed dimension to obtain two-dimensional matrix data, and storing a distance dimension FFT result;
step 4, performing GO-CFAR detection on the two-dimensional matrix data to acquire two-dimensional matrix data containing a target;
step 5, performing density clustering of noise response DBSCAN based on density spatial clustering on the two-dimensional matrix data containing the targets, and taking the maximum peak point in each category set after clustering as a detection target corresponding to the set to obtain speed and distance parameters of a plurality of targets;
step 6, extracting a multi-array element beat signal corresponding to each target based on the stored distance dimension FFT result;
and 7, carrying out multi-signal classification MUSIC algorithm processing on the multi-array element beat signals corresponding to each target, estimating the azimuth angle corresponding to the target, and outputting the azimuth angle in a matching manner with the speed, distance parameters and azimuth angle of the target, thereby completing multi-target detection.
2. The method as claimed in claim 1, wherein the a/D sampling data of the multi-element millimeter wave radar is obtained by mixing a local oscillation signal of a saw-tooth wave fm continuous wave radar with an original echo signal received by a receiving antenna to obtain a beat signal within M pulse repetition periods.
3. The method of claim 1, wherein the GO-CFAR detection is specifically: outputting two-dimensional matrix data as data of the detected cell to one input terminal of the comparator;
the distance dimension FFT data and the speed dimension FFT data which are used as sample data are respectively subjected to averaging processing and then output to a large selection unit, the large selection unit selects a group of data with larger average value from 2 groups of input data to calculate noise power, and the noise power is multiplied by a threshold scale factor of a detector to obtain a detection threshold estimation value which is output to the other input end of the comparator;
the comparator compares the data of the detected unit with the detection threshold estimation value, if the data of the detected unit is larger than or equal to the detection threshold estimation value, the situation that the target exists is shown, otherwise, the situation that the target does not exist is shown, and finally, the two-dimensional matrix data with the target exists is output.
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| CN114879139A (en) * | 2022-07-13 | 2022-08-09 | 广东大湾区空天信息研究院 | Joint angle measurement method and device for vehicle-mounted 4D millimeter wave radar and related equipment |
| CN115236627A (en) * | 2022-09-21 | 2022-10-25 | 深圳安智杰科技有限公司 | Millimeter wave radar data clustering method based on multi-frame Doppler velocity dimension expansion |
| CN115546526A (en) * | 2022-11-25 | 2022-12-30 | 南京隼眼电子科技有限公司 | Three-dimensional point cloud clustering method and device and storage medium |
| CN115616577A (en) * | 2022-12-19 | 2023-01-17 | 广东大湾区空天信息研究院 | Environment self-adaptive vehicle-mounted millimeter wave radar detection method and device and related equipment |
| CN116008944A (en) * | 2023-01-18 | 2023-04-25 | 珠海微度芯创科技有限责任公司 | Method and device for judging space dimension information source number of millimeter wave FMCW radar |
| CN116643248A (en) * | 2023-07-26 | 2023-08-25 | 成都航空职业技术学院 | Constant false alarm detection method, storage medium and equipment |
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