CN113253262B - One-dimensional range profile recording-based background contrast target detection method - Google Patents
One-dimensional range profile recording-based background contrast target detection method Download PDFInfo
- Publication number
- CN113253262B CN113253262B CN202110581785.6A CN202110581785A CN113253262B CN 113253262 B CN113253262 B CN 113253262B CN 202110581785 A CN202110581785 A CN 202110581785A CN 113253262 B CN113253262 B CN 113253262B
- Authority
- CN
- China
- Prior art keywords
- range profile
- dimensional range
- sequence
- target
- profile sequence
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- 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
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
- G06T7/262—Analysis of motion using transform domain methods, e.g. Fourier domain methods
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Artificial Intelligence (AREA)
- Mathematical Physics (AREA)
- Evolutionary Computation (AREA)
- Computing Systems (AREA)
- Biophysics (AREA)
- General Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- Molecular Biology (AREA)
- Electromagnetism (AREA)
- Biomedical Technology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Multimedia (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention discloses a method for detecting a target by recording background contrast based on a one-dimensional range profile, which comprises the following steps: collecting a one-dimensional range profile sequence without a target by a radar, and calculating a variance; acquiring and processing signals to obtain a current frame one-dimensional range profile sequence; the radar executes a CFAR detection algorithm, carries out angle estimation and outputs point cloud data; calculating the variance of the current frame one-dimensional range profile sequence, and performing correlation calculation on the current frame one-dimensional range profile sequence and the stored one-dimensional range profile sequence; comparing the correlation result with a threshold, and judging that no target exists when the correlation is greater than the threshold, the variance of the current frame one-dimensional range profile sequence is less than the variance of the empty scene one-dimensional range profile sequence and the radar does not detect point cloud data; otherwise, the target is determined. The invention not only removes the environmental interference, but also retains the effective information of the target, can stably detect the vehicles which are driven into the vehicle at a large angle, and enhances the detection capability of other weak targets such as people; simple use and strong applicability.
Description
Technical Field
The invention belongs to the technical field of vehicle passing systems, and particularly relates to a method for detecting a target by recording background contrast based on a one-dimensional range profile.
Technical Field
With the development and progress of science and technology, parking systems are more and more intelligent. In a parking system, access control management is critical. At present, a barrier gate radar sensor is often used for identifying vehicles entering or exiting, and a gate machine is matched to control a barrier gate to obtain a lifting rod, so that the vehicles can be controlled to enter or exit in order.
However, when a vehicle enters at a large angle, as shown in fig. 1, due to the large plane metal surface on the side surface of the vehicle, most useful signals are refracted, and the signal returning to the radar is too weak, so that the radar cannot accurately detect a target, and the radar is judged as noise by a series of operations such as a constant false alarm detection algorithm, and the vehicle is easily missed by the radar. In practical use, the radar is used as a sensor to detect whether vehicles exist in a specific area, and the gate machine controls the rising and falling of the barrier gate according to the information of whether vehicles exist uploaded by the radar. And the radar missing detection may cause the bar falling of the gate bar controlled by the gate machine, and the phenomenon of crashing the gate bar occurs in severe cases.
In the prior art, vehicle missing detection is usually performed based on the accuracy of radar point cloud data so as to avoid radar missing detection, the calculation complexity is high, and the effect is not obvious.
Disclosure of Invention
In view of the above, the invention provides a method for performing target detection by using a one-dimensional distance image of a blank scene and a one-dimensional distance image of an actual scene and combining point cloud data, which can be applied to a parking management system, a vehicle access system and other scenes with a single background, and fully utilizes the characteristic that the background of the scene is not changed to perform weak target detection.
A method for detecting a target by recording background contrast based on a one-dimensional range profile is applied to a millimeter wave radar, and comprises the following steps:
the millimeter wave radar carries out background learning and acquires a one-dimensional range profile sequence when no target exists in an actual application sceneXCalculating a one-dimensional range profile sequenceXVariance of (2)Var[X ]And stored in the memory of the radar;
ADC data acquisition and one-dimensional fast Fourier transform processing are carried out on millimeter wave radar echo signals to obtain a current frame one-dimensional range profile sequenceY；
The millimeter wave radar continues to perform two-dimensional fast Fourier transform processing on the one-dimensional fast Fourier transform processing result, executes a CFAR detection algorithm, performs angle estimation and outputs point cloud data;
calculating one-dimensional range profile sequence of current frameYVariance of (2)Var[Y ]For the current frame one-dimensional range profile sequenceYWith stored one-dimensional range profile sequenceXPerforming correlation calculation;
correlating the result with a predetermined thresholdKComparing when the correlation is greater than the thresholdKAnd satisfyVar[Y ]Is less thanm*Var[X ]WhereinmIf the point cloud data is a preset value, and the point cloud data is not detected by the millimeter wave radar, judging that no target exists; otherwise, the target is determined.
Further, the variance isVar[X ]AndVar[Y ]the calculation method of (2) is as follows:
Var[X ] =
Var[Y ] = 
whereinnThe length of the sequences X and Y is,Xiare the elements in the sequence X and are,Yiare the elements of the sequence Y in question,xis the average value of the sequence X,yis the average of the sequence Y.
Further, the correlation is calculated as follows:
calculating the covariance of the X and Y one-dimensional range profile sequences:
Cov(X,Y) = 
；
calculating the correlation of the one-dimensional range profile sequence X and Y:
R = 
whereinnThe length of the sequences X and Y is,Xiare the elements in the sequence X and are,Yiare the elements of the sequence Y in question,xis the average value of the sequence X,yis the average of the sequence Y.
Further, the practical application scenario is vehicle access management, and the targets include vehicles and pedestrians.
Further, the threshold valueKObtained by training a neural network.
Compared with the prior art, the invention has the following beneficial effects:
1) the one-dimensional range profile is adopted to record a radar memory, and then the range profile sequences currently detected by the radar are compared, so that the environmental interference is removed, the effective information of the target is kept, the vehicles driven into the vehicle at a large angle can be stably detected, and the detection stability can be ensured to be consistent for different scenes;
2) the detection capability of other weak targets such as people is enhanced;
3) the judgment is made by combining the radar point cloud data, the azimuth information of the point cloud, the correlation of the distance image contrast and the comparison of the variance of the two sequences are compared, and the method is simple to use and high in applicability on the premise of ensuring the timeliness and stability of the trigger position.
Drawings
FIG. 1 is a schematic illustration of a reflected signal of a ramp-in vehicle;
FIG. 2 is a flow chart of a method for detecting a target based on one-dimensional range profile record background contrast of the present invention;
FIG. 3 is a comparison graph of a distance image of an empty scene when the vehicle is moving forward;
FIG. 4 is a comparison graph of a range profile of a strong target behind a vehicle occluded by the vehicle and an empty scene range profile.
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 detecting a target based on one-dimensional range profile record background contrast, which comprises the following steps:
s10: background learning is carried out, and the radar acquires a one-dimensional range profile sequence without targets (namely, empty background) in an actual application sceneXCalculating a one-dimensional range profile sequenceXVariance of (2)Var[X ]And stored in the memory of the radar;
and performing ADC data acquisition on the millimeter wave radar echo signal to obtain sampling data.
Variance (variance)Var[X ]The calculation method of (2) is as follows:
recording a sequence of background range images ofXTotal length of sequence ofnThen there isX(n)。
Var[X ] = 
(1)
WhereinnIn order to be the length of the sequence,Xias elements in a sequence，xIs the sequence element average.
S20: ADC (analog-digital converter) data acquisition and one-dimensional fast Fourier transform processing are carried out on millimeter wave radar echo signals to obtain a current frame one-dimensional range profile sequenceY；
S30: the radar continues to perform two-dimensional fast Fourier transform processing on the one-dimensional fast Fourier transform processing result, executes a CFAR (Constant False Alarm Rate) detection algorithm, performs angle estimation, and outputs point cloud data;
for signals at the output of radar receiversx(t) It means that there are two cases here:
noise and signal are present simultaneously:x (t ) = s (t) + n (t)；
only noise is present:x (t) = n (t) 。
by usingH 0AndH 1respectively representing the hypotheses of no signal input and signal input of the receiver; by usingD 0AndD 1indicating that the detector makes no-signal and signal decisions, respectively, so that there will be four cases for the radar input and detector decisions:
H 0true, it is judged asD 0Namely, no signal is input to the radar, and the detector judges that no signal exists, which is called correct finding;
H 0true, it is judged asD 1The radar has no signal input, and the detector judges that a signal exists, which is called false alarm;
H 1true, it is judged asD 0The radar has signal input, and the detector judges that no signal exists, which is called false alarm;
H 1true, it is judged asD 1The radar has signal input, and the detector judges that the signal exists, which is called correct detection;
the first and fourth cases belong to correct decisions and the remaining two belong to erroneous decisions. By usingp (z∣H 0) Andp (z∣H 1) Respectively representing the probability density function of the signal level of the radar output end when no signal is input and a signal is input into the radar; by usingZ 0AndZ 1respectively representing decision regions where the detector makes no-signal and signal decisions, when the input level is atZ 0The region is judged to be without signal atZ 1The region is judged to have a signal.
S40: calculating one-dimensional range profile sequence of current frameYVariance of (2)Var[Y ]For the current frame one-dimensional range profile sequenceYWith stored one-dimensional range profile sequenceXPerforming correlation calculation;
variance (variance)Var[Y ]The calculation method of (2) is as follows:
the background range image sequence was recorded as:Ytotal length of sequence ofnThen there isY(n)
Var[Y ] = 
(2)
The correlation is calculated as follows:
calculating the covariance of the X and Y one-dimensional range profile sequences:
Cov(X,Y) = 
； (3)
calculating the correlation of the one-dimensional range profile sequence X and Y:
R = 
(4)
wherein the content of the first and second substances,nin order to be the length of the sequence,Xi，Yiis as follows.
S50: correlating the result with a predetermined thresholdKBy contrast, when the correlation is greater than a thresholdK(indicating high similarity to radar-stored empty scenes) and satisfyVar[Y ]Is less thanVar[X ]Meanwhile, when the radar does not detect the point cloud data, judging that no target exists; otherwise, the target is determined.
1) When no point cloud data exists in the detection area, the target can be preliminarily considered to be absent, because the radar does not detect the point cloud data at the moment, no target possibly appears, and in order to prevent the radar from missing detection, the one-dimensional range profile sequence of the current frame is further judgedYWith stored one-dimensional range profile sequenceXWhether the correlation is larger than a threshold value K or not is carried out, if so, no target appears in the current frame for the second time, because if the target appears, the one-dimensional range profile sequence of the current frameYHas a dispersion greater than that of the stored one-dimensional range profile sequenceX，The correlation between the two sequences will be less than the threshold; judging the one-dimensional range profile sequence of the current frameYIs less than the stored one-dimensional range profile sequenceXIf the variance of (2) is less than the variance of (3), finally confirming that no target exists in the current frame, because when the target exists, the image dispersion degree is increased, the variance is increased, and the variance of the current frame is smaller than that of a space scene, which indicates that no target exists in the current frame.
2) In the case of not belonging to 1), the target is determined.
The targeted cases include the following:
A. presence of point cloud data;
B. if no point cloud data exists, the current frame one-dimensional range profile sequenceYWith stored one-dimensional range profile sequenceXIs less than a threshold value K, and the current frame is a one-dimensional range profile sequenceYIs greater than the stored one-dimensional range profile sequenceXThe variance of (a);
C. if no point cloud data exists, the current frame one-dimensional range profile sequenceYWith stored one-dimensional range profile sequenceXIs less than a threshold value K, and the current frame is a one-dimensional range profile sequenceYIs less than the stored one-dimensional range profile sequenceXThe variance of (a);
D. if no point cloud data exists, the current frame one-dimensional range profile sequenceYWith stored one-dimensional range profile sequenceXIs greater than a threshold value K, and the current frame is a one-dimensional range profile sequenceYIs greater than the stored one-dimensional range profile sequenceXThe variance of (c).
For the situation A, because the radar has point cloud data, detection omission does not exist, variance and correlation do not need to be compared, and the existing target can be accurately judged;
for cases B and C, the current frame one-dimensional range profile sequenceYWith stored one-dimensional range profile sequenceXIs less than the threshold value K, which indicates that there is a target, resulting in a one-dimensional range profile sequence of the current frameYIs more discrete than the stored one-dimensional range profile sequenceX，The correlation of the two sequences is smaller than a threshold value, so that the existence of the target can be accurately judged;
for case D, the current frame one-dimensional range profile sequenceYIs greater than the stored one-dimensional range profile sequenceXThe variance of (2) indicates that there is an object, resulting in a one-dimensional range profile sequence of the current frameYThe variance of (2) becomes large, and the presence of a target can be accurately determined.
Because the prior art does not use the one-dimensional range profile of the empty scene and the target scene to carry out variance comparison and the judgment of the correlation to improve the technical scheme of radar point cloud data omission, the invention overcomes the technical bias and develops a new way, utilizes the characteristic that the background of a single background application scene is not changed, and utilizes the characteristic that the fluctuation is large when the one-dimensional range profile of the vehicle inclines compared with the empty scene range profile, adopts the correlation to measure, and reduces the calculation complexity and the target detection accuracy compared with the prior art which only starts from the angle of improving the accuracy of the point cloud data.
The threshold value K is obtained after experiments are carried out according to actual application scenes. Preferably, in this embodiment, the threshold K is set after training through the neural network:
K = 
(5)
wherein, w and b are training parameters, X is a stored one-dimensional range profile sequence of the empty scene, and Z is a one-dimensional range profile sequence which is subjected to target detection only by using point cloud data in an actual application scene and has missing detection. And (3) adjusting the values of w and b by using a gradient descent algorithm through continuous improvement, so that the loss function of the model reaches the minimum value, namely, the model parameters are optimized.
This example uses a 0-1 loss function, the specific formula is as follows:
whereinYIn order to achieve the target value,f(X) Is a predicted value.
The neural network types used in this embodiment include convolutional neural networks and cyclic neural networks, such as LeNet, AlexNet, VGG, ResNet, DenseNet, LSTM, GRU, and other models, which is not limited in this embodiment.
Fig. 3 is a comparison diagram of a target range profile and an empty scene range profile when a vehicle normally enters, the ordinate is the range profile amplitude, and the abscissa is the distance unit point. It can be seen from the figure that when the target normally enters the radar detection range, the abscissa 61-66 points are peak values generated when the vehicle is moving forward, the correlation degree of the target range profile and the recorded background range profile is low, which indicates that the target exists in the detection area at the moment, and at the moment, the millimeter wave radar can detect point cloud data, so that the target exists in the area can be accurately judged.
As shown in fig. 4, the dotted line is a background distance image of an empty scene, a road tooth or other strong targets are in the empty scene, the amplitude of the dotted line distance image with a higher distance unit point at the 89-93 point on the abscissa is generated by the road tooth or other strong targets, and the solid line is a one-dimensional distance image after the vehicle enters obliquely and shields the strong targets such as the road tooth. It can be seen from the figure that when a strong target in the environment is blocked by the current detection target, the amplitude of the strong target distance unit point in the environment at the abscissa 89-93 point is obviously reduced, and at the position where the vehicle is inclined, the distance image amplitude at the abscissa 27-32 point is obviously increased, because the target distance image and the recorded empty scene distance image have two large changes, the correlation is obviously lowered, and the target in the detection area can be accurately detected. At the moment, the variance of the one-dimensional range profile when the vehicle inclines is far larger than that of the empty scene, and the variance of the one-dimensional range profile when the vehicle inclines is far larger than that of the empty sceneVar[Y ]Variance of empty sceneVar[X ]Is/are as followsmPreferred in this embodimentmIs 1.5. The correlation between the target distance image and the recorded empty scene distance image when the vehicle inclines is lower than that when the vehicle normally enters, and the variance between the target distance image and the recorded empty scene distance image when the vehicle inclines is higher than that when the vehicle normally enters, so that the detection accuracy of the vehicle inclining is greatly improved.
Claims (4)
1. A method for detecting a target based on one-dimensional range profile record background contrast is applied to a millimeter wave radar, and is characterized by comprising the following steps:
the millimeter wave radar carries out background learning and acquires a one-dimensional range profile sequence when no target exists in an actual application sceneXCalculating a one-dimensional range profile sequenceXVariance of (2)Var[X ]And stored in the memory of the radar;
ADC data acquisition and one-dimensional fast Fourier transform of millimeter wave radar echo signalsThe one-dimensional range profile sequence of the current frame is obtained by conversion processingY；
The millimeter wave radar continues to perform two-dimensional fast Fourier transform processing on the one-dimensional fast Fourier transform processing result, executes a CFAR detection algorithm, performs angle estimation and outputs point cloud data;
calculating one-dimensional range profile sequence of current frameYVariance of (2)Var[Y ]For the current frame one-dimensional range profile sequenceYWith stored one-dimensional range profile sequenceXPerforming correlation calculation;
correlating the result with a predetermined thresholdKComparing when the correlation is greater than the thresholdKAnd satisfyVar[Y ]Is less thanm*Var[X ]WhereinmIf the point cloud data is a preset value, and the point cloud data is not detected by the millimeter wave radar, judging that no target exists; otherwise, judging that a target exists;
wherein the threshold K is set after training through the neural network:
wherein, w and b are training parameters, X is a stored one-dimensional range profile sequence of the empty scene, and Z is a one-dimensional range profile sequence which is subjected to target detection only by using point cloud data in an actual application scene and has missing detection.
2. The method of claim 1, wherein the variance is a function of a distance between the object and the objectVar[X ]AndVar[Y ]the calculation method of (2) is as follows:
whereinnIs the sequenceThe length of the X and Y is the same as the length of the Y,Xiare the elements in the sequence X and are,Yiare the elements of the sequence Y in question,xis the average value of the sequence X,yis the average of the sequence Y.
3. The method of claim 1, wherein the correlation is calculated as follows:
calculating the covariance of the X and Y one-dimensional range profile sequences:
calculating the correlation of the one-dimensional range profile sequence X and Y:
whereinnThe length of the sequences X and Y is,Xiare the elements in the sequence X and are,Yiare the elements of the sequence Y in question,xis the average value of the sequence X,yis the average of the sequence Y.
4. The method of claim 1, wherein the practical application scene is vehicle access management, and the objects comprise vehicles and pedestrians.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110581785.6A CN113253262B (en) | 2021-05-27 | 2021-05-27 | One-dimensional range profile recording-based background contrast target detection method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110581785.6A CN113253262B (en) | 2021-05-27 | 2021-05-27 | One-dimensional range profile recording-based background contrast target detection method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113253262A CN113253262A (en) | 2021-08-13 |
| CN113253262B true CN113253262B (en) | 2021-12-17 |
Family
ID=77184962
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110581785.6A Active CN113253262B (en) | 2021-05-27 | 2021-05-27 | One-dimensional range profile recording-based background contrast target detection method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113253262B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115128571B (en) * | 2022-09-02 | 2022-12-20 | 长沙莫之比智能科技有限公司 | Multi-person and non-motor vehicle identification method based on millimeter wave radar |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109633571A (en) * | 2018-12-18 | 2019-04-16 | 长沙莫之比智能科技有限公司 | A kind of 77GHz millimeter wave gate cracking protection radar signal processing device |
| CN110208767A (en) * | 2019-06-05 | 2019-09-06 | 哈尔滨工程大学 | A kind of radar target rapid detection method based on fitting correlation coefficient |
| CN111830508A (en) * | 2020-06-23 | 2020-10-27 | 北京航空航天大学 | Barrier gate anti-smashing system and method adopting millimeter wave radar |
-
2021
- 2021-05-27 CN CN202110581785.6A patent/CN113253262B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109633571A (en) * | 2018-12-18 | 2019-04-16 | 长沙莫之比智能科技有限公司 | A kind of 77GHz millimeter wave gate cracking protection radar signal processing device |
| CN110208767A (en) * | 2019-06-05 | 2019-09-06 | 哈尔滨工程大学 | A kind of radar target rapid detection method based on fitting correlation coefficient |
| CN111830508A (en) * | 2020-06-23 | 2020-10-27 | 北京航空航天大学 | Barrier gate anti-smashing system and method adopting millimeter wave radar |
Non-Patent Citations (3)
| Title |
|---|
| Matching score properties between range profiles of high-resolution radar targets;Hsueh-Jyh Li.et al;《IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION》;19960430;第44卷(第4期);第444-452页 * |
| 舰船目标一维距离像水声测量的相似性度量方法研究;卢建斌等;《声学技术》;20120831;第31卷(第4期);第133-136页 * |
| 舰船目标一维距离像特性分析;陈宏昆等;《电子科技》;20180430;第31卷(第4期);第29-32页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113253262A (en) | 2021-08-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108490410B (en) | Two-coordinate radar sea target joint detection and tracking method | |
| US8094060B2 (en) | Method of detecting a target | |
| CN101930072B (en) | Multi-feature fusion based infrared small dim moving target track starting method | |
| CN107942329B (en) | Method for detecting sea surface ship target by maneuvering platform single-channel SAR | |
| CN109633633B (en) | Life signal enhancement method based on segmented classification enhancement processing | |
| CN111157953B (en) | Two-stage threshold constant false alarm detection algorithm under strong ground clutter | |
| CN111582253B (en) | Event trigger-based license plate tracking and identifying method | |
| CN114114192B (en) | Cluster target detection method | |
| CN108226890B (en) | Airport foreign matter radar detection method based on time direction statistics | |
| CN113030973B (en) | Scene monitoring radar signal processing system and method | |
| CN111323765A (en) | Satellite-borne photon counting laser radar echo signal processing and target extraction method | |
| CN113096397A (en) | Traffic jam analysis method based on millimeter wave radar and video detection | |
| KR20140107799A (en) | Radar apparatus using image change detector, and method for operating the same | |
| CN109254271B (en) | Static target suppression method for ground monitoring radar system | |
| CN113253262B (en) | One-dimensional range profile recording-based background contrast target detection method | |
| CN107274410A (en) | Adaptive man-made target constant false alarm rate detection method | |
| Zheng et al. | Detection of small objects in sidescan sonar images based on POHMT and Tsallis entropy | |
| CN108983194B (en) | Target extraction and condensation method based on ground monitoring radar system | |
| US11262444B2 (en) | Method for tracking targets, in particular maritime targets, and radar implementing such a method | |
| Bi et al. | Improved multi-target radar TBD algorithm | |
| CN109239677B (en) | Environment self-adaptive constant false alarm rate detection threshold determination method | |
| CN115980689A (en) | Point cloud detection-based radiation source signal sorting method, device, equipment and medium | |
| CN113625266A (en) | Method, device, storage medium and equipment for detecting low-speed target by using radar | |
| CN116224280B (en) | Radar target detection method, radar target detection device, radar equipment and storage medium | |
| KR101430257B1 (en) | An automatical shot change detection device using adaptive threshold for digital surveillance camera system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |