CN109298414B - Radar multi-moving-target real-time tracking method - Google Patents
Radar multi-moving-target real-time tracking method Download PDFInfo
- Publication number
- CN109298414B CN109298414B CN201811153436.9A CN201811153436A CN109298414B CN 109298414 B CN109298414 B CN 109298414B CN 201811153436 A CN201811153436 A CN 201811153436A CN 109298414 B CN109298414 B CN 109298414B
- Authority
- CN
- China
- Prior art keywords
- target
- track
- point
- flight path
- segment
- 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
- G01S13/66—Radar-tracking systems; Analogous systems
- G01S13/72—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
- G01S13/723—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar by using numerical data
- G01S13/726—Multiple target tracking
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
A radar multi-moving target real-time tracking method comprises the following steps: s1, acquiring original echo signals of the radar, and preprocessing the original echo signals to obtain processed echo data; s2, carrying out segmentation processing on the echo data; s3, inquiring each segment of data after the segmentation processing, and searching the best target matching point; s4, adopting mean value type constant false alarm rate detection to judge whether the optimal target matching point of each segment of data is a moving target, and obtaining a target point of the moving target; and S5, performing track management on the target point to obtain a stable track, and outputting the position information of the stable track. The invention adopts the method of sectional processing and combining the detection and judgment of the mean constant false alarm rate, effectively reduces the complexity of operation under the condition of ensuring the accuracy, and ensures that the embedded low-power consumption singlechip can process in real time. The method is suitable for multi-moving-target real-time tracking of the ultra-wideband through-wall radar in a complex environment.
Description
Technical Field
The invention belongs to the field of radar detection, and particularly relates to a radar multi-moving-target real-time tracking method.
Background
The ultra-wideband through-wall radar has excellent penetrability, ultrahigh distance resolution and positioning accuracy, and is widely applied to scenes such as fire emergency rescue, military anti-terrorism, urban street battle and the like to detect specific positions and activity ranges of trapped people, criminals or hostage and the like after obstacles such as walls and the like are detected.
In the practical application of ultra-wideband through-wall radar multi-moving target detection and tracking, two problems need to be solved urgently. Firstly, the accuracy of detection, in the actual scene that through-the-wall radar surveyed, echo data receive the noise interference that system self and external environment brought very easily. Meanwhile, when a moving target is detected, the target movement intersects, the target positions are mutually shielded, the detection environment is complex, the multipath effect is serious and other factors can cause adverse effects on the accuracy of the through-wall radar detection, and a great deal of inconvenience is brought to operators. Secondly, the real-time performance of detection is realized, the traditional multi-target detection mostly adopts constant false alarm rate detection (CFAR), the detected sequence is converted into a binary sequence, often a target corresponds to a plurality of sampling points and is judged as a target point, and the specific position of the target needs to be estimated according to the plurality of target points. Therefore, the calculated amount is large, and the subsequent multi-target track management is added, so that the real-time detection and tracking of the embedded low-power-consumption chip are difficult to realize.
Disclosure of Invention
Aiming at the problems, in order to ensure the accuracy and real-time performance of detection, the invention provides a radar multi-moving-target real-time tracking method, which comprises the following steps:
s1, acquiring original echo signals of the radar, and preprocessing the original echo signals to obtain processed echo data;
s2, carrying out segmentation processing on the echo data;
s3, inquiring each segment of data after the segmentation processing, and searching the best target matching point;
s4, adopting mean value type constant false alarm rate detection to judge whether the optimal target matching point of each segment of data is a moving target, and obtaining a target point of the moving target; and
and S5, performing track management on the target point to obtain a stable track, and outputting position information of the stable track.
In some embodiments, step S1 specifically includes the following steps:
s1a, acquiring an original echo signal of the radar;
s1b, performing band-pass filtering and logarithmic power gain control on the original echo signal;
s1c, extracting moving object information from the data processed in the step S1b by adopting a three-frame difference method; and
and S1d, extracting a signal envelope by using Hilbert transform to obtain the echo data.
In some embodiments, in step S2, the range image of the echo data is segmented, and the length of each segment of data increases with increasing distance.
In some embodiments, in step S3, the maximum power estimation value in each segment is found, and the position information of the maximum power estimation value is recorded and used as the optimal target matching point.
In some embodiments, step S4 further includes determining whether the best target matching point in the currently detected segment is located on the segment boundary and determining whether its power value is lower than a value on the other side of the boundary, and if so, determining it as a false target point and removing it.
In some embodiments, step S5 specifically includes the following steps:
s5a, establishing N track structures and initializing, wherein N is the number of segments for segmentation processing in the step S2;
s5b, judging whether the current track is effective, if not, directly judging the next track structure, and if so, entering the subsequent steps;
s5c, performing correlation matching between the trace points of the target points and the flight path;
s5d, predicting and updating the flight path;
s5e, performing state maintenance on the flight path to obtain a stable flight path;
returning to the step S5b, judging the next track structure until the N track structures are processed;
s5f, establishing a new track for a target point which is not matched with the track; and
and S5g, outputting the position information of the stable track.
In some embodiments, in step S5a, the track structure includes the following parameters: the ID of each track structure, whether the track is effective or not, whether a target mark is matched or not, a target position, a predicted position, a position record, a survival time parameter and a life quality parameter.
In some embodiments, in step S5c,
if a target point falls into the neighborhood of the flight path, setting the target position as the position information of the target point, adding 1 to the life quality parameter, and setting 1 to indicate whether the target point is matched or not, thereby completing the correlation matching of the point path of the target point and the flight path;
if no target point falls into the neighborhood of the flight path, setting the target position as a preset value, adding 0 to the life quality parameter, and setting the target mark as 0 if the target point is matched;
thereafter, the target position is added to the rear of the position record.
In some embodiments, in step S5d, a predicted value is obtained by using first-order linear prediction according to the position record, and the predicted position is updated according to the predicted value.
In some embodiments, in step S5e, a dynamic adjustment or deletion operation of the flight path is performed according to the time-to-live parameter and the quality-of-life parameter of the flight path.
Based on the technical scheme, the invention at least obtains the following beneficial effects:
the invention adopts the method of sectional processing and combining the detection and judgment of the mean constant false alarm rate, effectively reduces the complexity of operation under the condition of ensuring the accuracy, and ensures that the embedded low-power consumption singlechip can process in real time. The method is suitable for the real-time tracking of the ultra-wideband through-wall radar on the multiple moving targets in the complex environment.
Drawings
FIG. 1 is a flowchart illustrating steps of a radar multi-moving-object real-time tracking method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a radar multi-moving target real-time tracking method according to an embodiment of the present invention;
FIG. 3 is a flowchart of step S5 according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of track structure initialization according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention without any inventive step, are within the scope of protection of the invention.
Unless defined otherwise, technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs.
Fig. 1 is a flowchart illustrating steps of a radar multi-moving-target real-time tracking method according to an embodiment of the present invention, and referring to fig. 1, the radar multi-moving-target real-time tracking method according to the embodiment of the present invention includes the following steps:
and S1, acquiring the original echo signal of the radar, and preprocessing the original echo signal to obtain processed echo data.
And S2, carrying out segmentation processing on the echo data.
And S3, inquiring each segment of data after the segmentation processing, and searching the best target matching point.
And S4, adopting mean value type constant false alarm rate detection to judge whether the optimal target matching point of each segment of data is a moving target, and obtaining a target point of the moving target.
And S5, performing track management on the target point to obtain a stable track, and outputting the position information of the stable track.
The invention adopts the method of sectional processing and combining the detection and judgment of the mean constant false alarm rate, effectively reduces the complexity of operation under the condition of ensuring the accuracy, and ensures that the embedded low-power consumption singlechip can process in real time.
According to some embodiments, step S1 specifically includes the following steps:
and S1a, acquiring a raw echo signal of the radar.
And S1b, performing band-pass filtering and logarithmic power gain control on the original echo signal.
In this embodiment, first, the interference noise in the original echo signal is removed by band-pass filtering. Since the echo signal of the far target is weak and the echo signal of the near target is strong, the far target is difficult to detect, and therefore the echo signal of the far target needs to be enhanced. In this embodiment, logarithmic power gain control is performed using the following equation:
x′＝x*[log(m)]n。
where x is a signal before amplification, x' is a signal after amplification, m is 1, 2. Conventional linear or power function gain control amplifies the echo signal of a distant target while causing the noise level at the distance to far exceed the echo signal of the target; the logarithmic power function gain control adopted in the embodiment of the invention can reasonably control the noise level at a far distance, so that the target echo signal is not submerged by noise.
S1c, extracting the moving object information from the data processed in step S1b by using a three-frame difference method. In this embodiment, a mathematical expression for extracting moving object information by using a three-frame difference method is as follows:
zk＝(xk+1-xk)-(xk-xk-1)＝(xk+1+xk-1)-2xk。
wherein xk represents the reception echo value at the kth time, and is an Nx 1 vector, zkIs an echo signal containing information of a moving object.
S1d, extracting signal envelopes by using Hilbert transform to obtain echo data.
For example, first of all, the echo signal zkPerforming Hilbert transform, hereinafter replacing z with x (t)kTo represent the echo signal at this time, the hilbert transform is expressed as follows:
then, a signal envelope u (t) of x (t) is obtained as echo data, as shown in the following formula:
where A (t) is a function of amplitude,
is a function of phase, and
after the required echo data is obtained, referring to fig. 2, according to some embodiments, in step S2, a range profile of the echo data is segmented, and the length of each segment of data increases with increasing distance, for example, the segment is divided into N segments as shown in the figure. The positioning error of the radar is increased along with the increase of the distance, so that the radar echo data is divided into N sections from small to large, and the influence of the positioning error can be reduced.
According to some embodiments, in step S3, the maximum power estimation value in each segment is found, and the position information of the maximum power estimation value is recorded and used as the optimal target matching point.
Next, in step S4, mean-class simple constant false alarm rate detection (ML _ SCFAR) is performed on the optimal target matching point in each segment, and whether the optimal target matching point in the segment is a moving target is determined, the principle of which is shown in fig. 2. The judgment criterion of the comparator is as follows, namely whether the target is a moving target is judged by the following formula:
d is the maximum power estimation value of the optimal target matching point in the currently detected section; h1The representation being a moving object, H0Indicating not a moving object; z is background average power, and the value of Z is estimated by signals in reference sliding windows at two sides of the optimal target matching point; t is a normalization factor, preset by reference sliding window length RProbability of false alarm PfaT and Z together constitute the detection threshold.
According to some embodiments, step S4 further includes determining whether the best target matching point in the currently detected segment is located at the segment boundary and determining whether its power value is lower than the value on the other side of the boundary, and if so, determining it as a false target point and removing it. Since the segmented intervals are fixed, the echo signal of the moving target may fall into two adjacent intervals at the same time, and the moving target is determined as the moving target at the same time, so that a false target point is generated. Since this type of false target point is introduced by segmentation and the echo signal of the same moving target is continuously changing, the false target point will appear at the boundary of the segment. The present embodiment improves reliability by eliminating false target points by determining whether the target is located at a segment boundary and below the value at the other side of the boundary.
Thereafter, the process proceeds to step S5, where the trajectory management is performed on the target point of the moving target obtained by removing the pseudo target point.
In the prior art, in the flight path management scheme, the point path-flight path association matching is the most complex problem in multi-target tracking, and particularly, in the case of point path "robbing" or "conflict", that is, one point path falls into multiple target flight paths, or multiple point paths simultaneously fall into a wave gate of a certain flight path.
In view of the above problem, further, referring to fig. 3, according to some embodiments, step S5 specifically includes the following steps:
and S5a, establishing N track structures and initializing, wherein N is the number of the segments subjected to the segmentation processing in the step S2. Those skilled in the art will appreciate that "Y" after each decision block in fig. 3 indicates yes and "N" indicates no, unlike the number of segments described above.
Preferably, with further reference to fig. 4, the Track structure Struct _ Track includes, but is not limited to, the following parameters: the trackID is the ID of each track structure, the mark is _ Valid of whether the track is Valid, the mark is _ FindTarget of whether the track is matched with the target mark, the target Position, the predicted Position, the Position record Position, the Time-to-live parameter Life _ Time and the quality of Life parameter Life _ Value. The initialized values of the parameters are shown in fig. 4.
In this embodiment, since the ML _ SCFAR method is adopted before, at most one target is detected in each segment, and thus there are at most N targets, memory spaces of N tracks need to be opened up, and the ID range of the track structure is 1 to N.
And S5b, judging whether the current track is effective, if not, directly judging the next track structure, and if so, entering the subsequent steps.
For example, whether the current track is Valid is judged by judging whether the track Valid flag is _ Valid is equal to 1, and if not, the next track structure is directly judged; and if the number is equal to 1, performing subsequent operations such as matching of the trace points and the track, track management and prediction and the like. In this embodiment, since the mark is _ Valid is 0 for each of the tracks of the N track structures after initialization, the subsequent step S5f is directly performed in the first round of loop; in step S5f, a track is established for the target point, and after the flag is _ Valid is set to 1, the process returns to step S5b to start the second round of loop.
And S5c, performing correlation matching of the trace of the target point and the flight path.
In this embodiment, since the ML _ SCFAR detection method is adopted before, there are only 1 target at most in the neighborhood of each track. For example, if a target point falls within the neighborhood of the track, the target position is set as the position information of the target point, that is, the target point is located in the neighborhood of the track
Struct_Track(i).Position＝Position_CFAR，
Wherein, Position _ CFAR is the CFAR detection result, namely the Position information of the target point;
and adding 1 to the quality of life parameter, i.e.
Struct_Track(i).Life_Value＝Life_Value+ 1；
And whether the target mark is _ FindTarget is matched or not is set to be 1, and the correlation matching of the trace point of the target point and the flight path is completed.
If no target point falls into the neighborhood of the flight path, setting the target position as a preset value, namely
Struct_Track(i).Position＝PrePosition；
And adding 0 to the quality of life parameter, i.e.
Struct_Track(i).Life_Value＝Life_Value+ 0；
And sets the target flag is _ FindTarget to 0.
Thereafter, the target Position is added to the rear of the Position recording Position _ History.
And S5d, predicting and updating the flight path.
For example, according to the Position record Position _ History, a predicted value is obtained by adopting a first-order linear prediction mode, and the predicted Position PrePosition is updated according to the predicted value.
The formula for first order linear prediction is as follows:
The final prediction formula is:
y is a + bx. That is, the target position y at the next time x can be predicted from the above expression.
And S5e, performing state maintenance on the flight path.
For example, referring to fig. 3, a dynamic adjustment or deletion operation of the track is performed according to the Life Time parameter Life _ Time and the quality of Life parameter Life _ Value of the track.
In one embodiment, as shown in fig. 3, if Life _ Time is 4 and Life _ Value is 4, or Life _ Time is a multiple of 7 and Life _ Value ≧ 5, the track is upgraded directly to a Stable track, i.e., the track Attribute Struct _ track (i).
If the above conditions are not satisfied and Life _ Time is not a multiple of 7, the state of the track is not changed, and the next track structure is directly judged.
If the above conditions are not met, but Life _ Time is a multiple of 7, and the Value of quality of Life Life _ Value is less than 5, the state of the flight path is degraded. The degrading operation is specifically as follows: the stable track becomes an UnStable track, namely a track Attribute Struct _ track (i) Attribute ═ ultrastable'; the unstable track is changed into a New track, namely a track Attribute Struct _ track (i) Attribute ═ New'; the new track will be deleted and initialized by the track structure as shown in fig. 4.
Thereafter, the process returns to step S5b, and the next track structure is determined, and the process is repeated until the N track structures are processed.
And S5f, establishing a new track for the target point which is not matched with the track.
And establishing a New track for a target point detected by the ML _ SCFAR which is not matched with the track, and setting a mark of whether the track is Valid as Struct _ Track (i) is _ Valid to be 1 and setting track Attribute Struct _ Track (i) Attribute to be 'New'.
And S5g, outputting the position information of the stable track.
Counting the number of Stable tracks, namely the number of tracks of a track Attribute Struct _ track (i), wherein Attribute is Stable, and then outputting the ID information and the position information of the Stable tracks.
The invention adopts the method of sectional processing and combining the detection and judgment of the mean constant false alarm rate, effectively reduces the complexity of operation under the condition of ensuring the accuracy, and ensures that the embedded low-power consumption singlechip can process in real time. Meanwhile, the flight path management method effectively solves the problems of flight path-flight path conflict and robbery in the flight path management. The method is suitable for the real-time tracking of the ultra-wideband through-wall radar on the multiple moving targets in the complex environment.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (10)
1. A radar multi-moving target real-time tracking method is characterized by comprising the following steps:
s1, acquiring original echo signals of the radar, and preprocessing the original echo signals to obtain processed echo data;
s2, carrying out segmentation processing on the echo data;
s3, inquiring each segment of data after the segmentation processing, and searching the best target matching point;
s4, adopting mean value type constant false alarm rate detection to judge whether the optimal target matching point of each segment of data is a moving target, and obtaining a target point of the moving target; and
and S5, performing track management on the target point to obtain a stable track, and outputting position information of the stable track.
2. The method according to claim 1, wherein step S1 specifically comprises the steps of:
s1a, acquiring an original echo signal of the radar;
s1b, performing band-pass filtering and logarithmic power gain control on the original echo signal;
s1c, extracting moving object information from the data processed in the step S1b by adopting a three-frame difference method; and
and S1d, extracting a signal envelope by using Hilbert transform to obtain the echo data.
3. The method according to claim 1, wherein in step S2, the range profile of the echo data is segmented, and the length of each segment of data increases with increasing distance.
4. The method according to claim 1, wherein in step S3, the maximum power estimation value in each segment is found, and the position information of the maximum power estimation value is recorded and used as the optimal target matching point.
5. The method according to claim 4, wherein step S4 further comprises determining whether the best target matching point in the currently detected segment is located at the segment boundary and determining whether its power value is lower than the value at the other side of the boundary, and if so, determining it as a false target point and removing it.
6. The method according to claim 1, wherein step S5 specifically comprises the steps of:
s5a, establishing N track structures and initializing, wherein N is the number of segments for segmentation processing in the step S2;
s5b, judging whether the current track is effective, if not, directly judging the next track structure, and if so, entering the subsequent steps;
s5c, performing correlation matching between the trace points of the target points and the flight path;
s5d, predicting and updating the flight path;
s5e, performing state maintenance on the flight path to obtain a stable flight path;
returning to the step S5b, judging the next track structure until the N track structures are processed;
s5f, establishing a new track for a target point which is not matched with the track; and
and S5g, outputting the position information of the stable track.
7. The method according to claim 6, wherein in step S5a, the track structure comprises the following parameters: the ID of each track structure, whether the track is effective or not, whether a target mark is matched or not, a target position, a predicted position, a position record, a survival time parameter and a life quality parameter.
8. The method of claim 7, wherein, in step S5c,
if a target point falls into the neighborhood of the flight path, setting the target position as the position information of the target point, adding 1 to the life quality parameter, and setting 1 to indicate whether the target point is matched or not, thereby completing the correlation matching of the point path of the target point and the flight path;
if no target point falls into the neighborhood of the flight path, setting the target position as a preset value, adding 0 to the life quality parameter, and setting the target mark as 0 if the target point is matched;
thereafter, the target position is added to the rear of the position record.
9. The method according to claim 8, wherein in step S5d, a predicted value is obtained by means of first-order linear prediction according to the position record, and the predicted position is updated according to the predicted value.
10. The method of claim 9, wherein in step S5e, the dynamic adjustment or deletion of the flight path is performed according to the time-to-live parameter and the quality-of-life parameter of the flight path.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811153436.9A CN109298414B (en) | 2018-09-29 | 2018-09-29 | Radar multi-moving-target real-time tracking method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811153436.9A CN109298414B (en) | 2018-09-29 | 2018-09-29 | Radar multi-moving-target real-time tracking method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109298414A CN109298414A (en) | 2019-02-01 |
| CN109298414B true CN109298414B (en) | 2020-11-20 |
Family
ID=65161413
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811153436.9A Active CN109298414B (en) | 2018-09-29 | 2018-09-29 | Radar multi-moving-target real-time tracking method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109298414B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110208767B (en) * | 2019-06-05 | 2022-12-13 | 哈尔滨工程大学 | Radar target rapid detection method based on fitting correlation coefficient |
| WO2021087782A1 (en) * | 2019-11-05 | 2021-05-14 | 深圳市大疆创新科技有限公司 | Obstacle detection method and system, ground end device, and autonomous mobile platform |
| CN111665483B (en) * | 2020-05-07 | 2023-08-15 | 隔空（上海）智能科技有限公司 | Moving target detection method of Doppler radar |
| CN113093145B (en) * | 2021-06-09 | 2021-10-01 | 深圳市万集科技有限公司 | Target detection method and target detection device |
| CN113807484B (en) * | 2021-08-31 | 2024-06-28 | 中国科学院上海微***与信息技术研究所 | Stable moving object counting method based on sensor array |
| CN116430345B (en) * | 2023-06-12 | 2023-09-12 | 中国民航管理干部学院 | 5G network-based low-altitude unmanned aerial vehicle multi-target reflection source separation method |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104991235A (en) * | 2015-06-15 | 2015-10-21 | 南京航空航天大学 | Method for rapid tracking target based on radar trace points |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102338874B (en) * | 2011-06-24 | 2013-05-08 | 浙江大学 | Global probability data correlation method used for passive multi-sensor target tracking |
| CN103852756B (en) * | 2012-11-30 | 2017-02-22 | 中国科学院沈阳自动化研究所 | Target detecting and tracking method by utilization of continuous wave radar |
| CN104101875B (en) * | 2014-07-11 | 2017-07-11 | 西北工业大学 | A kind of air traffic control radar multi-object tracking method |
-
2018
- 2018-09-29 CN CN201811153436.9A patent/CN109298414B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104991235A (en) * | 2015-06-15 | 2015-10-21 | 南京航空航天大学 | Method for rapid tracking target based on radar trace points |
Non-Patent Citations (2)
| Title |
|---|
| 一种航迹恒虚警的目标检测跟踪一体化算法;刘红亮 等;《电子与信息学报》;20160531;第38卷(第5期);1072-1078 * |
| 多雷达***数据融合与航迹预测;吴显亮 等;《数学的实践与认识》;20100831;第40卷(第15期);161-173 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109298414A (en) | 2019-02-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109298414B (en) | Radar multi-moving-target real-time tracking method | |
| CN111580084B (en) | Multi-distance extended target-oriented multi-Bernoulli pre-detection tracking method | |
| CN109946671B (en) | Underwater maneuvering weak target detection and tracking method based on double-threshold judgment | |
| CN109917378B (en) | Video SAR moving object detection method using space-time correlation | |
| CN105842687B (en) | Detecting and tracking integral method based on RCS predictive information | |
| CN109597065B (en) | False alarm suppression method and device for through-wall radar detection | |
| CN111948657B (en) | Maneuvering weak target tracking-before-detection method based on multimode particle filtering | |
| CN106291534B (en) | A kind of improved track confirmation method | |
| CN113702979B (en) | Cross-region target tracking track segment space-time splicing method | |
| CN111157953B (en) | Two-stage threshold constant false alarm detection algorithm under strong ground clutter | |
| CN111610501A (en) | Sea radar small target detection method | |
| CN108010066B (en) | Multi-hypothesis tracking method based on infrared target gray level cross-correlation and angle information | |
| CN109100697A (en) | A kind of target condensing method based on ground surveillance radar system | |
| CN108983194B (en) | Target extraction and condensation method based on ground monitoring radar system | |
| CN111796266B (en) | Tracking method before plane detection of uniformly accelerated moving target RD | |
| CN105652256B (en) | A kind of high-frequency ground wave radar TBD methods based on polarization information | |
| CN111833377A (en) | TBD-based small moving target detection method in complex environment | |
| CN113126086B (en) | Life detection radar weak target detection method based on state prediction accumulation | |
| CN112698295B (en) | Knowledge-assisted radar detection and tracking integrated method and system | |
| CN114444538B (en) | Improved automatic line spectrum extraction method | |
| CN113625266A (en) | Method, device, storage medium and equipment for detecting low-speed target by using radar | |
| CN111337894B (en) | Average constant false alarm rate detection method for intelligent reference unit | |
| Zhang et al. | Adaptive rectification filter for detecting small IR targets | |
| CN111025251B (en) | Multi-target composite detection method based on dynamic programming | |
| Berenkov et al. | Reliable detection of dynamic anomalies with application to extracting faint space object streaks from digital frames |
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 |