CN110632943A - Unmanned aerial vehicle obstacle avoidance radar tree contour detection method and device based on energy accumulation - Google Patents
Unmanned aerial vehicle obstacle avoidance radar tree contour detection method and device based on energy accumulation Download PDFInfo
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Abstract
The invention discloses an unmanned aerial vehicle obstacle avoidance radar tree contour detection method and device based on energy accumulation, wherein the method comprises the following steps: s1, acquiring an echo signal returned by an unmanned aerial vehicle obstacle avoidance radar, carrying out CFAR detection on the acquired echo signal, and when a tree target is detected, executing a step S2; s2, acquiring current continuous multi-frame radar signal echoes for time domain accumulation to obtain accumulated echo signals; and S3, carrying out target detection on the accumulated echo signal, and determining to obtain the outline of the tree target according to the currently detected target. The method can realize the detection of the tree outline based on the unmanned aerial vehicle obstacle avoidance radar, and has the advantages of simple realization method, high detection efficiency and detection precision, safety, reliability and the like.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle obstacle avoidance radar detection, in particular to an unmanned aerial vehicle obstacle avoidance radar tree contour detection method and device based on energy accumulation.
Background
In recent years, with the rapid growth of the market of industrial unmanned aerial vehicles such as agriculture, electric power and industry, the key technology of the unmanned aerial vehicle is also making a change of day and night, such as real-time image transmission, target identification, terrain following and other technologies, so that the industrial unmanned aerial vehicle tends to be more and more intelligent. In many technical trends, the automatic obstacle avoidance capability is the key to achieving the safety of the unmanned aerial vehicle. The unmanned aerial vehicle autonomous obstacle avoidance system can reduce the damage of the unmanned aerial vehicle, personal accidents or building accidents caused by human misoperation and sight problems to the maximum extent. Currently, the mainstream products generally adopt Frequency Modulated Continuous Wave (FMCW) signals which emit multiple periods, and each Frequency sweep period is TchirpThe method can measure the distance and the radial speed of each target in a multi-target scene at the same time, the emitted waveforms are shown in figure 1, and the waveform system can measure the distance and the speed of a plurality of targets at the same time, and is widely applied to the fields of ship detection, tsunami detection and the like of automobile radars and sky wave ultra-vision radars.
Install the radar on unmanned aerial vehicle, can measure from unmanned aerial vehicle to distance, angle and relative velocity etc. between the various barriers, if adopt the millimeter wave radar, possess stronger weather adaptability and job stabilization reliable. When the unmanned aerial vehicle obstacle avoidance millimeter wave radar works, each pulse is sampled by transmitting Linear Frequency Modulation (LFM) signals of a plurality of pulses, and distance dimension Fourier Transform (FFT) is performed to obtain a distance dimension processing result; after each pulse is processed similarly, corresponding to a certain distance unit, the speed dimension FFT processing is carried out on the echoes of a plurality of pulses, namely, the second FFT processing is carried out, and a distance-Doppler matrix can be obtained at the moment; after a distance-Doppler matrix is obtained, target detection is carried out by adopting a Constant False Alarm probability (CFAR) detection algorithm, target parameter information obtained at the moment comprises target distance and speed, target angle estimation is carried out among a plurality of array elements based on a detected target result, and after parameter information above a target is obtained, tracking filtering, track management and the like of the target are carried out.
When flying in the air, the unmanned aerial vehicle easily collides various buildings, crowns, electric wires and the like, and once the collision happens, the collision event of the unmanned aerial vehicle is possibly caused, even the unmanned aerial vehicle is crashed and damaged, and the accident of hurting people can also happen if the unmanned aerial vehicle is heavy. The unmanned aerial vehicle radar detection method has the advantages that trees are found to be important for unmanned aerial vehicle flight safety in time, detection of the trees is aimed at, and in comparison, detection difficulty of the unmanned aerial vehicle radar is not large for dense trees, but when the traditional CFAR processing method is used, only dense parts of the trees are actually detected by the radar, such as trunk main body parts, and sparser parts such as crowns and branches, due to the fact that echo of a reflection signal is weak, the covering effect of a strong reflection body of the trunk on a weak target can exist, the sparser parts such as the crowns and the branches in the trees are difficult to detect under the covering of the strong trunk, even if the trees are detected by the millimeter wave radar, the outline boundaries of the trees cannot be accurately detected due to the fact that the crowns and the branches cannot be detected, and the unmanned aerial vehicle still can collide with the branches to cause collision accidents. Therefore, for the unmanned aerial vehicle obstacle avoidance radar, the task to be realized is not only to detect the tree, judge the distance from the trunk to the unmanned aerial vehicle, and more importantly, the profile of the tree should be judged, so that the radar can provide the distance from the unmanned aerial vehicle to the tree, which is the nearest. However, as above-mentioned current traditional obstacle avoidance radar can only provide the distance from unmanned aerial vehicle to trunk main body region, has certain hidden danger to obstacle avoidance radar, if consider to reduce the detection threshold in the CFAR detection method in order to improve the detection of comparatively sparse parts such as crown, branch, will arouse a large amount of false alarm problems again, cause the puzzlement for practical application.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the technical problems in the prior art, the invention provides the tree profile detection method and device of the unmanned aerial vehicle obstacle avoidance radar based on the energy accumulation, which are simple in implementation method, high in detection efficiency and detection precision, safe and reliable.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
an unmanned aerial vehicle obstacle avoidance radar tree contour detection method based on energy accumulation is characterized by comprising the following steps:
s1, one-time CFAR detection: acquiring an echo signal returned by the unmanned aerial vehicle obstacle avoidance radar, performing CFAR detection on the acquired echo signal, and executing step S2 when a tree target is detected;
s2, time domain energy accumulation: obtaining echoes of current continuous multi-frame radar signals to perform time domain accumulation to obtain accumulated echo signals;
s3, secondary target detection: and carrying out target detection on the accumulated echo signals, and determining to obtain the outline of the tree target according to the currently detected target.
Further, in the step S3, CFAR detection is specifically performed on a secondary detection region determined in the cumulative echo signal, where the secondary detection region is determined according to the prior information of the tree target detected in the step S1.
Further, the secondary detection area is determined according to the radar distance resolution and the size of the tree target detected in step S1.
Further, in step S3, a constant threshold detection method is used when performing target detection.
Further, the threshold value in the constant threshold detection method is calculated by multiplying a factor coefficient by the current noise floor level.
Further, in step S2, the echo signals of the current detection time and several previous adjacent frames are specifically acquired for incoherent energy accumulation.
Further, when the tree target is detected in step S1, it is determined that the target is a strong reflection target including a trunk body portion in the corresponding tree; when the target is detected in step S3, it is determined that the target includes a weak reflection object of a leaf or a branch and an RCS flickering object, and the contour of the tree is determined from the currently detected target.
Further, the CFAR detection in step S1 specifically adopts a cell average constant false alarm CA-CFAR detection method.
The utility model provides an unmanned aerial vehicle keeps away barrier radar tree profile detection device based on energy accumulation, includes:
the primary CFAR detection module is used for acquiring echo signals returned by the unmanned aerial vehicle obstacle avoidance radar, performing CFAR detection on the acquired echo signals, and when a tree target is detected, executing the step S2;
the time domain energy accumulation module is used for acquiring current continuous multiframe radar signal echoes to perform time domain accumulation to obtain accumulated echo signals;
and the secondary target detection module is used for carrying out target detection on the accumulated echo signal and determining the outline of the tree target according to the currently detected target.
A computer readable storage medium storing a computer program, which when executed, implements the method for detecting tree contours of unmanned aerial vehicle obstacle avoidance radar based on energy accumulation as described above.
Compared with the prior art, the invention has the advantages that:
1. the invention relates to an unmanned aerial vehicle obstacle avoidance radar tree contour detection method and device based on energy accumulation, which fully consider the instability of the echo time domain of the branches and leaves and the flicker characteristic of RCS, after the tree target is determined to be detected by performing CFAR detection on the radar echo signal once, accumulating the energy of multi-frame data, performing the second target detection, adopting a time domain energy accumulation method, can obviously enhance the echoes with weaker echoes and stronger flicker characteristics of RCS, is convenient for detecting branches and leaves with weak reflection and strong flicker of RCS, avoids the influence of a strong reflection area of a trunk on the detection of the outline of the tree, thereby can realize the accurate detection of trees profile, effectively avoid unmanned aerial vehicle to survey unclear and lead to the mistake to hit the condition of branch and crown to trees profile, keep away the barrier for unmanned aerial vehicle based on millimeter wave radar and provide safer flight area.
2. According to the method and the device for detecting the tree profile of the unmanned aerial vehicle obstacle avoidance radar based on energy accumulation, the secondary target detection is started and executed after the first CFAR detects the target, namely, the secondary target detection is carried out under the condition that the tree target is determined to exist in the primary target detection, so that the detection precision can be ensured, the false alarm is not increased, and the normal work of the radar is not influenced.
3. According to the method and the device for detecting the tree profile of the unmanned aerial vehicle obstacle avoidance radar based on energy accumulation, after time domain energy accumulation is carried out, a secondary detection area is determined by using prior information such as the size of a target when target detection is carried out on an accumulated echo signal, so that only sample points in a target distance area are detected, noise false alarm sample points outside the distance area are eliminated, the detection precision can be ensured, the detection efficiency is effectively improved, and unnecessary detection work is avoided.
Drawings
Fig. 1 is a waveform schematic diagram of a classical FMCW transmission sequence.
Fig. 2 is a schematic flow chart of the implementation of the tree contour detection method based on the energy accumulation for the obstacle avoidance radar of the unmanned aerial vehicle in the embodiment.
Fig. 3 is a schematic diagram illustrating the principle of CA-CFAR detection used in the present embodiment.
Fig. 4 is a diagram illustrating the result after performing CFAR detection once in the present embodiment.
Fig. 5 is a schematic diagram illustrating the principle of performing the incoherent accumulation of the time-domain energy of multiple frames of echoes in the embodiment.
Fig. 6 is a schematic diagram illustrating the principle of performing secondary target detection in the target area in the present embodiment.
Detailed Description
The invention is further described below with reference to the drawings and specific preferred embodiments of the description, without thereby limiting the scope of protection of the invention.
As shown in fig. 2, the method for detecting the tree contour of the obstacle avoidance radar of the unmanned aerial vehicle based on energy accumulation in the embodiment includes the following steps:
s1, one-time CFAR detection: acquiring an echo signal returned by the unmanned aerial vehicle obstacle avoidance radar, performing CFAR detection on the acquired echo signal, and executing step S2 when a tree target is detected;
s2, time domain energy accumulation: obtaining echoes of current continuous multi-frame radar signals to perform time domain accumulation to obtain accumulated echo signals;
s3, secondary target detection: and carrying out target detection on the accumulated echo signals, and determining to obtain the outline of the tree target according to the currently detected target.
Because the branches and the leaves can shake, the echo time domains of the branches and the leaves usually have instability, namely the Radar echo Cross-sectional area (RCS) has a flicker characteristic, the instability of the echo time domains of the branches and the leaves and the flicker characteristic of the RCS are fully considered in the embodiment, when the unmanned aerial vehicle obstacle avoidance Radar detects the trees, CFAR detection is performed on Radar echo signals firstly to determine the detected tree targets, then multi-frame data energy is accumulated to perform secondary target detection, the method of time domain energy accumulation is adopted, so that the echoes with weaker echoes and stronger flicker characteristics of the RCS can be obviously enhanced, the branches and the leaves with weak reflection and RCS flicker can be detected conveniently, the influence of a strong reflection area of a trunk on the detection of the tree profile is avoided, the accurate detection of the tree profile can be realized, and the situations that the unmanned aerial vehicle mistakenly collides the branches and crowns due to unclear tree profile detection are effectively avoided, and a safer flight area is provided for obstacle avoidance of the unmanned aerial vehicle based on the millimeter wave radar.
In the method of this embodiment, since the second target detection is performed after the first CFAR detects the target, that is, the second detection is performed when the tree target is determined to exist, a false alarm is not increased, only the radial distance of the tree may be shortened, that is, the flight danger area is enlarged, but no influence is caused on the practical application.
In this embodiment, when the tree target is detected in step S1, it is determined that the target is a strong reflection target including a trunk body portion in the corresponding tree; when the target is detected in step S3, it is determined that the target includes a weak reflection of leaves and branches and an RCS flickering target, and the contour of the tree is determined from the currently detected target. The embodiment executes CFAR detection for the radar echo signal once, and specifically can execute detection once by adopting a CFAR detection method in the prior art, the trunk strong reflection part of the tree can be detected after detection is finished, after a tree target is detected, time domain energy accumulation is performed to start secondary target detection, and at the moment, the weak reflection and the leaves and branches flickering in RCS can also be detected, so that the outline boundary of the tree can be accurately determined.
In this embodiment, the CFAR detection in step S1 specifically adopts a Cell Average-Constant False Alarm Rate (CA-CFAR) detection method. When the unit average constant false alarm CA-CFAR is detected, a window is used for placing a unit needing to be detected at present at the middle position of the window, the environment except the unit to be detected in the window is estimated (averaged), a threshold coefficient obtained according to the false alarm rate is multiplied by the average of the threshold coefficient and the noise floor to obtain a threshold value, then the unit to be detected is compared with the threshold value, if the current detection unit is larger than the threshold value, the current detection unit is determined as a target, and otherwise, the current detection unit is determined as the noise floor. By adopting the unit average constant false alarm CA-CFAR, the target detection precision can be further improved, and the influence of a large number of false alarms caused by noise is avoided.
The CA-CFAR detection specifically adopted in this embodiment is shown in fig. 3, and the background clutter power level is estimated from the average of a plurality of reference unit samples output by the linear detector; the two protection units are nearest to the detection unit and are mainly used in the single-target condition to prevent target energy from leaking to the reference unit and influencing two local estimation values of a leading edge sliding window and a trailing edge sliding window of the detector; and obtaining clutter power level estimation according to the two estimation values and different CFAR (constant false alarm rate) criteria, namely average clutter envelope estimation in a reference sliding window, wherein T is a normalization factor. The adaptive decision criterion is specifically:
wherein H1Indicates the presence of the target hypothesis, H0Indicating a no-target hypothesis.
X is called total clutter power level estimation, detection probability PdThe relationship to the normalization factor T is:
Pd＝[1+T/(1+C)]-N (3)
wherein C is the average power to clutter power ratio of the target signal; when C is 0, the false alarm probability P can be obtainedfaRelationship to the normalization factor T:
T＝(Pfa)-1/N-1 (4)
as mentioned above, neither the detection probability nor the false alarm probability depends on the clutter power level μ, i.e. CA-CFAR has constant false alarm.
After CFAR processing, a fixed threshold is used for judging the existence of the target (threshold passing detection), the threshold value is determined by clutter distribution characteristics and false alarm probability, namely the power ratio of X to a detection point is solved and then is compared with a detection threshold T, and whether the target is detected or not can be judged according to whether the threshold is exceeded or not.
After the target detection is performed once in step S1, the trunk portion may be detected, but branches and crowns may not be detected due to the covering effect of the strong trunk reflector on the weak target, as shown in fig. 4, after CA-CFAR detection is performed once, a large amount of false alarms (such as high amplitude points caused by noise in fig. 4) caused by noise can be avoided, but at the same time, objects with weak reflection, RCS flicker, and the like, such as branches, leaves, and the like, may not be effectively detected. In this embodiment, when it is determined that the tree target is detected after the CFAR detection is performed once, step S2 is further performed to acquire echoes of the currently connected multiframe signals for time domain accumulation, so that echoes with a weaker echo and RCS with a stronger flicker characteristic can be significantly enhanced. In this embodiment, specifically, echo signals of the current detection time and the previous adjacent frames are obtained for incoherent energy accumulation, taking the example of obtaining signal echoes of the current detection time and the previous two frames for incoherent accumulation, assuming that the current detection time is n, and the signal echoes are xnIndicating that the signal echo of the previous frame is xn-1Echo of the first two frames is xn-2That is, the signal echo for the second target detection in the subsequent step S3 is:
yn＝|xn|+|xn-1|+|xn-2| (5)
the signal accumulation results obtained at this time are shown in fig. 5.
It can be understood that the above-mentioned energy accumulation can determine the number of echo signal frames to be accumulated according to actual requirements.
After the time domain energy accumulation of the currently connected multi-frame echo signals is completed in the step S2, echoes of leaves, branches and the like with weak echoes and RCS with strong flicker characteristics are significantly enhanced, and the second target detection is performed in the step S3 on the basis, so that the outline of the tree can be accurately detected. As shown in fig. 6, in step S3 of this embodiment, a secondary detection region determined in the cumulative echo signal is specifically subjected to a second target detection, and the secondary detection region is determined according to the prior information of the tree target detected in step S1, that is, only the sampling points in the target distance region are detected, and the noise false alarm sampling points outside the distance region are excluded, so that the detection accuracy can be ensured, the detection efficiency can be effectively improved, and unnecessary detection work can be avoided.
In this embodiment, the secondary detection area is specifically determined according to prior information including the radar distance resolution, the size of the tree target detected in step S1, and the secondary detection area can be accurately determined based on the prior information such as radar parameters and target information, so that the detection efficiency and accuracy are further improved, and the influence of noise false alarms is reduced.
In this embodiment, when the target detection is performed in step S3, a constant threshold detection method is adopted, that is, the target detection is performed according to a fixed threshold value, and a detection point greater than the fixed threshold value is determined as the target, where the threshold value may be specifically calculated by multiplying a factor coefficient by a current noise floor level, and certainly, other methods may be adopted to determine the threshold value according to actual requirements.
This embodiment unmanned aerial vehicle keeps away barrier radar tree profile detection device based on energy accumulation includes:
the primary CFAR detection module is used for acquiring echo signals returned by the unmanned aerial vehicle obstacle avoidance radar, performing CFAR detection on the acquired echo signals, and when a tree target is detected, executing the step S2;
the time domain energy accumulation module is used for acquiring current continuous multiframe radar signal echoes to perform time domain accumulation to obtain accumulated echo signals;
and the secondary target detection module is used for carrying out target detection on the accumulated echo signals and determining the outline of the tree target according to the currently detected target.
The obstacle-avoidance radar tree contour detection device of the unmanned aerial vehicle based on energy accumulation and the obstacle-avoidance radar tree contour detection method of the unmanned aerial vehicle based on energy accumulation are in one-to-one correspondence, and are not repeated one by one here.
The present embodiment is a computer-readable storage medium storing a computer program, and when the computer program is executed, the method for detecting the tree contour of the obstacle avoidance radar of the unmanned aerial vehicle based on energy accumulation is implemented.
The foregoing is considered as illustrative of the preferred embodiments of the invention and is not to be construed as limiting the invention in any way. Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical spirit of the present invention should fall within the protection scope of the technical scheme of the present invention, unless the technical spirit of the present invention departs from the content of the technical scheme of the present invention.
Claims (10)
1. An unmanned aerial vehicle obstacle avoidance radar tree contour detection method based on energy accumulation is characterized by comprising the following steps:
s1, one-time CFAR detection: acquiring an echo signal returned by the unmanned aerial vehicle obstacle avoidance radar, performing CFAR detection on the acquired echo signal, and executing step S2 when a tree target is detected;
s2, time domain energy accumulation: obtaining echoes of current continuous multi-frame radar signals to perform time domain accumulation to obtain accumulated echo signals;
s3, secondary target detection: and carrying out target detection on the accumulated echo signals, and determining to obtain the outline of the tree target according to the currently detected target.
2. The unmanned aerial vehicle obstacle avoidance radar tree profile detection method based on energy accumulation as claimed in claim 1, wherein in step S3, CFAR detection is specifically performed on a secondary detection region determined in the accumulated echo signal, and the secondary detection region is determined according to prior information of the tree target detected in step S1.
3. The unmanned aerial vehicle obstacle avoidance radar tree profile detection method based on energy accumulation as claimed in claim 2, wherein the secondary detection area is determined according to a size of the tree target detected in step S1, wherein the size includes radar distance resolution.
4. The unmanned aerial vehicle obstacle avoidance radar tree profile detection method based on energy accumulation as claimed in claim 1, wherein a constant threshold detection method is adopted when target detection is performed in step S3.
5. The unmanned aerial vehicle obstacle avoidance radar tree profile detection method based on energy accumulation as claimed in claim 4, wherein the threshold value in the constant threshold detection method is calculated by multiplying a factor coefficient by a current noise floor level.
6. The tree contour detection method based on the energy accumulation for the obstacle avoidance unmanned aerial vehicle radar as claimed in any one of claims 1 to 5, wherein in step S2, echo signals of a current detection time and previous adjacent frames are specifically acquired for incoherent energy accumulation.
7. The unmanned aerial vehicle obstacle avoidance radar tree profile detection method based on energy accumulation as claimed in any one of claims 1 to 5, wherein when a tree target is detected in step S1, the target is determined to be a strong reflection target including a trunk body part in a corresponding tree; when the target is detected in step S3, it is determined that the target includes a weak reflection object of a leaf or a branch and an RCS flickering object, and the contour of the tree is determined from the currently detected target.
8. The tree contour detection method based on the energy accumulation for the obstacle avoidance unmanned aerial vehicle radar as claimed in any one of claims 1 to 5, wherein a CA-CFAR detection method is specifically adopted for CFAR detection in step S1.
9. The utility model provides an unmanned aerial vehicle keeps away barrier radar tree profile detection device based on energy accumulation which characterized in that includes:
the primary CFAR detection module is used for acquiring echo signals returned by the unmanned aerial vehicle obstacle avoidance radar, performing CFAR detection on the acquired echo signals, and when a tree target is detected, executing the step S2;
the time domain energy accumulation module is used for acquiring current continuous multiframe radar signal echoes to perform time domain accumulation to obtain accumulated echo signals;
and the secondary target detection module is used for carrying out target detection on the accumulated echo signal and determining the outline of the tree target according to the currently detected target.
10. A computer-readable storage medium storing a computer program which, when executed, implements a method as claimed in any one of claims 1 to 8.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070252748A1 (en) * | 2004-11-03 | 2007-11-01 | Flight Safety Technologies, Inc. | Collision alerting and avoidance system |
| CN102323577A (en) * | 2011-09-08 | 2012-01-18 | 北京理工雷科电子信息技术有限公司 | High-resolution radar dual-threshold detector based on energy accumulation |
| CN103995261A (en) * | 2014-05-23 | 2014-08-20 | 广东电网公司电力科学研究院 | Target signal processing device of unmanned aerial vehicle evadible system and unmanned aerial vehicle evadible system |
| CN107678019A (en) * | 2017-08-16 | 2018-02-09 | 成都纳雷科技有限公司 | Radar signal multi-target detection method and device based on CA CFAR |
| CN107783114A (en) * | 2016-08-25 | 2018-03-09 | 大连楼兰科技股份有限公司 | The remote complex environment anticollision MMW RADAR SIGNAL USING processing system of rotor wing unmanned aerial vehicle and method |
-
2019
- 2019-09-29 CN CN201910933988.XA patent/CN110632943A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070252748A1 (en) * | 2004-11-03 | 2007-11-01 | Flight Safety Technologies, Inc. | Collision alerting and avoidance system |
| CN102323577A (en) * | 2011-09-08 | 2012-01-18 | 北京理工雷科电子信息技术有限公司 | High-resolution radar dual-threshold detector based on energy accumulation |
| CN103995261A (en) * | 2014-05-23 | 2014-08-20 | 广东电网公司电力科学研究院 | Target signal processing device of unmanned aerial vehicle evadible system and unmanned aerial vehicle evadible system |
| CN107783114A (en) * | 2016-08-25 | 2018-03-09 | 大连楼兰科技股份有限公司 | The remote complex environment anticollision MMW RADAR SIGNAL USING processing system of rotor wing unmanned aerial vehicle and method |
| CN107678019A (en) * | 2017-08-16 | 2018-02-09 | 成都纳雷科技有限公司 | Radar signal multi-target detection method and device based on CA CFAR |
Non-Patent Citations (7)
| Title |
|---|
| 孙栋 等: "直升机相控阵毫米波防撞雷达信号处理技术研究", 《电子技术与软件工程》 * |
| 李刚 等: "基于混合积累的SAR微弱运动目标检测", 《电子学报》 * |
| 李品 等: "跨距离门目标的时频积累及检测方法研究", 《现代雷达》 * |
| 杨建桥 等: "具有非相参积累的双门限CA―CFAR检测器", 《哈尔滨工业大学学报》 * |
| 王威 等: "带有非相参积累的序列统计恒虚警检测器", 《系统工程与电子技术》 * |
| 王晓红 等: "基于实测数据的有源噪声干扰抑制算法研究", 《雷达与对抗》 * |
| 邓云开 等: "基于毫米波雷达系统的昆虫RCS测量与迂飞监测", 《2014年全国博士生学术论坛（信息与电子技术）论文集》 * |
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Application publication date: 20191231 |