CN108008374A - The large-scale object detection method in sea based on energy intermediate value - Google Patents
The large-scale object detection method in sea based on energy intermediate value Download PDFInfo
- Publication number
- CN108008374A CN108008374A CN201711075843.8A CN201711075843A CN108008374A CN 108008374 A CN108008374 A CN 108008374A CN 201711075843 A CN201711075843 A CN 201711075843A CN 108008374 A CN108008374 A CN 108008374A
- Authority
- CN
- China
- Prior art keywords
- target
- mrow
- matrix
- detected
- unit
- 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.)
- Granted
Links
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
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/04—Systems determining presence of a target
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/414—Discriminating targets with respect to background clutter
Abstract
The invention discloses a kind of quick determination method of the large-scale target in the sea based on energy intermediate value, mainly solves the problems, such as that the prior art is not suitable for detecting the large-scale target in sea under high resolution mode.Implementation step is：1) echo data is obtained；2) echo data is subjected to incoherent processing along pulse dimension and obtains energy mean matrix z；3) logarithmic transformation is carried out to matrix Z and obtains log space matrix4) matrix is utilizedAsk and calculate its detection statistic ξ corresponding to unit d to be detected；5) matrix is utilizedAsk and calculate its detection threshold T corresponding to unit d to be detected；6) size of comparing check statistic ξ and detection threshold T, judges that target whether there is；7) length dimension estimation is carried out using testing result.The present invention improves detection performance and length dimension estimated accuracy of the radar to large-scale target, and the large-scale target in sea is managed available for Rapid Test Desk.
Description
Technical field
The invention belongs to signal processing technology field, more particularly to a kind of big object detection method, available for big to sea
The size estimation of target.
Background technology
Object detection method under sea clutter background is studied is respectively provided with wide application prospect at military, civilian aspect.Bank
Base radar and airborne radar are usually operated under high resolution mode.When distance resolution reaches meter level or sub-meter grade, islands and reefs, greatly
Type naval vessel, fishing boat etc. will all occupy multiple range cells.Such large-scale target is quickly and accurately detected, to interesting target
Follow-up relevant treatment is of great significance.
Document He, Y., Guan, J.:Meng,X.W.et al.:‘Radar target detection and CFAR
Processing ', (Tsinghua University Press, 2011,2st edn.), pp.30-50 and document Watts, S.:
‘Cell-averaging CFAR gain in spatially correlated K-distributed clutter’,IEEE
Radar Sonar Navig., 1996,143, the pp.321-327 all kinds of CFAR detection methods based on energy proposed due to
Easy to implement, calculating speed is widely used in Radar Targets'Detection soon.When interesting target is precision target, above-mentioned biography
System method is used as by the clutter unit chosen around precision target refers to unit, passes through the analysis to reference unit, Neng Gouyou
Effect ground clutter reduction, and then the detection result of satisfaction can be obtained.But when distance resolution reaches meter level or sub-meter grade, it is large-scale
Target tends to take up a range cells up to a hundred.For such large-scale target, above-mentioned conventional method is large-scale when selecting reference unit
Reference unit around target range unit remains as object element, can not carry out clutter recognition using reference unit information, most
Target can even be removed as clutter when detecting eventually, lead to not effectively detect large-scale target.
The content of the invention
It is an object of the invention to the deficiency for above-mentioned existing method, proposes that a kind of sea based on energy intermediate value is large-scale
The quick determination method of target, to complete under high resolution mode to the quick detection of the large-scale target in sea and size estimation.
To achieve the above object, technical scheme includes as follows：
(1) continuous pulse signal is launched using radar transmitter, radar receiver receives the echo data of M × I × N-dimensional
Matrix X, wherein, M represents that frame number scans number, and I represents range cell number, and N represents accumulation umber of pulse；
(2) arithmetic mean of instantaneous value of energy is sought echo data matrix X along pulse dimension, obtains energy mean matrix z, its
In, the data z (m, i) of the m rows i row of energy mean matrix z is：
Wherein, | | represent modulus, X (m, i, n) represents the echo data of n pages of the m row i row of echo data matrix X；
(3) logarithmic transformation is carried out to energy mean matrix Z, obtains log space matrix：
(4) in log space matrixIn, choose around unit to be detected w range cell as reference from distance dimension
Unit, then the detection statistic ξ of unit d to be detected be：
Wherein：Median is to take median operation；
(5) in log space matrixIn, choose around unit to be detected s range cell as reference from distance dimension
Unit, then the detection threshold T of unit d to be detected be：
Wherein：S ＞ w, β are adjustment parameter, and β is related with the Radar Cross Section SCR of target；
(6) size of comparing check statistic ξ and detection threshold T, judges that target whether there is：
If ξ >=T, show that range cell d to be detected has target,
If ξ ＜ T, show that range cell d to be detected does not have target；
(7) length dimension estimation is carried out using the testing result obtained in step (6)：
(7a) ties up the initial bit for marking each large-scale target in distance to the large-scale object detection results in step (6)
Put r1, final position r2；
(7b) then corresponds to length dimension rough estimate of the target on the direction corresponding to the frame and is calculated as：
L=| r2-r1| × Δ r,
Wherein, | | represent modulus, Δ r is distance resolution.
The present invention has the following advantages compared with the prior art：
1) present invention is not related to covariance matrix due to being handled using the mean power of signal for noncoherent processing
Estimation, processing speed is fast, so being particularly suited under high resolution mode to the quick detection of the large-scale target in sea and length dimension
Estimation.
2) present invention is referred to as a result of the more reference unit of number is chosen, and can be efficiently extracted large-scale
Clutter information around target, restrained effectively clutter, can realize the detection under high resolution mode to the large-scale target in sea.
3) present invention is due to the use of taking the mode of intermediate value to calculate detection statistic and thresholding, compared to taking average, maximum, most
The statistics such as small value can effectively avoid crossing for large-scale target length size from estimating, improve sea as detection statistic and thresholding
The length dimension estimation accuracy of the large-scale target in face.
Brief description of the drawings
Fig. 1 realizes flow chart for the present invention's；
Fig. 2 is the Comparative result with the present invention and big target detection statistic of the existing method under Observed sea clutter
Figure；
Fig. 3 is the result figure estimated with the present invention the length dimension of the medium-and-large-sized target of measured data.
Embodiment
The invention will be further described below in conjunction with the accompanying drawings：
With reference to Fig. 1, step is as follows for of the invention realizing：
Step 1, echo data is obtained.
Launch continuous pulse signal using radar transmitter, radar receiver receives the echo data square of M × I × N-dimensional
Battle array X, wherein, M represents that frame number scans number, and I represents range cell number, and N represents accumulation umber of pulse.
Step 2, energy mean matrix z is calculated.
The pulse dimension energy for echo data matrix X being calculated as follows along pulse dimension echo data matrix X is averaged
Value, obtains energy mean matrix Z：
Wherein, X (m, i, n) represents the echo data of n pages of the m row i row of echo data matrix X, | X (m, i, n) | represent
To data X (m, i, n) modulus, Z (m, i) represents the data of the m rows i row of energy mean matrix Z, | Z (m, i) | represent data
Z (m, i) forms matrix form in order.
Step 3, log space matrix is calculated
Logarithmic transformation is carried out to energy mean matrix Z：Obtain log space matrix
Step 4, for unit d to be detected, its detection statistic ξ is calculated.
4.1) scope for assuming large-scale target length size interested is [Lmin,Lmax], returned without considering large-scale target
In the case that ripple extends, the scope for the range cell number M that large-scale target occupies is：Wherein Δ r is distance
Resolution ratio, then the reference estimation unit number w scopes chosen meet：
4.2) in log space matrixIn, choose around unit to be detected w range cell as reference from distance dimension
Unit, calculates the detection statistic ξ of unit d to be detected according to the following formula：
Wherein：Median is to take median operation.
Step 5, for unit d to be detected, its detection threshold T is calculated.
5.1) in log space matrixIn, choose around unit to be detected s range cell as reference from distance dimension
Unit, the value range of reference unit number s are pressed：Condition choose, wherein：Δ r is distance resolution, LmaxFor sense
The upper bound of the large-scale target length size range of interest；
5.2) the detection threshold T of unit d to be detected is calculated according to the following formula：
Wherein：S ＞ w, β are adjustment parameter, and β is related with the Radar Cross Section SCR of target.
Step 6, target is judged.
By the size of comparing check statistic ξ and detection threshold T, judge that target whether there is：
If ξ >=T, show that range cell d to be detected has target,
If ξ ＜ T, show that range cell d to be detected does not have target.
Step 7, length dimension estimation is carried out to large-scale target using testing result.
(7.1) to the large-scale object detection results in step (6), be marked in distance dimension, i.e., will in distance dimension
Each section continuously has object element to be considered as a large-scale target, to each its initial position of large-scale target label r1And termination
Position r2；
(7.2) according to initial position r1With final position r2, the corresponding length dimension rough estimate of each large-scale target is calculated as：
L=| r2-r1| × Δ r,
Wherein, | | represent modulus, Δ r is distance resolution.
The effect of the present invention is described further with reference to emulation experiment.
One, experimental datas
This example uses the S-band Observed sea clutter that land-based radar gathers, and radar pulse repetition frequency is 1800 hertz
Hereby, distance resolution is 1 meter.It is 6 to choose data pulse cumulative number, and range cell is from 7700 to 8400, frame number 48, big target
Average signal to noise ratio is 35 decibels.
Two, emulation experiments
Emulation 1, using the detection method of the of the invention and existing CA-CFAR based on energy, respectively carries out large-scale target
Detection, the results are shown in Figure 2, wherein：
Fig. 2 (a) be measured data in contain big target raw radar data clutter map, target be located at 8009-8184 away from
At unit；
Fig. 2 (b) is the detection statistic knot being detected using existing CA-CFAR non-coherent detection methods to large-scale target
Fruit is schemed, and wherein protection location number is 4, and reference unit number is 30；
Fig. 2 (c) is the detection statistic result figure being detected using the present invention to large-scale target, wherein, parameter w=
80；
As it is clear from fig. 2 that the present invention is compared to the ruler that big target can be efficiently extracted based on CA-CFAR non-coherent detection methods
It is very little and clutter is effectively inhibited, and traditional CA-CFAR methods, chosen due to its reference unit and remain as target list
Member, so big clarification of objective cannot be extracted effectively, can not carry out follow-up large-scale target detection.
Emulation 2, length dimension estimation is carried out using the present invention to large-scale target, and the results are shown in Figure 3, wherein：
Relativenesses of the Fig. 3 (a) between detection statistic and thresholding, wherein dotted line are detection statistic, and solid line is door
Limit value, wherein, parameter s=200, β=1.1；
The length dimension estimated result of Fig. 3 (b) large-scale targets for this, 0, which represents this range cell, does not have target, and 1 represents this
Range cell contains target；
From figure 3, it can be seen that thresholding set by the present invention can efficiently differentiate out large-scale target and common sea clutter with
And weak target, length dimension estimated result 8009-8191 more coincide with actual big target location 8009-8184.
To sum up, the present invention estimates essence to the detection performance and length dimension of the large-scale target in sea under high resolution mode
Degree is superior to existing detection method.
Claims (3)
1. a kind of quick determination method of the large-scale target in sea based on energy intermediate value, including：
(1) continuous pulse signal is launched using radar transmitter, radar receiver receives the echo data matrix of M × I × N-dimensional
X, wherein, M represents that frame number scans number, and I represents range cell number, and N represents accumulation umber of pulse；
(2) arithmetic mean of instantaneous value of energy is sought echo data matrix X along pulse dimension, obtains energy mean matrix z, wherein, energy
The data z (m, i) of the m rows i row of amount mean matrix z is：
<mrow>
<mi>z</mi>
<mrow>
<mo>(</mo>
<mi>m</mi>
<mo>,</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mi>N</mi>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>n</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<msup>
<mrow>
<mo>|</mo>
<mi>X</mi>
<mrow>
<mo>(</mo>
<mi>m</mi>
<mo>,</mo>
<mi>i</mi>
<mo>,</mo>
<mi>n</mi>
<mo>)</mo>
</mrow>
<mo>|</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>,</mo>
<mi>m</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<mi>M</mi>
<mo>,</mo>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<mi>I</mi>
<mo>,</mo>
</mrow>
Wherein, | | represent modulus, X (m, i, n) represents the echo data of n pages of the m row i row of echo data matrix X；
(3) logarithmic transformation is carried out to energy mean matrix Z, obtains log space matrix：
(4) in log space matrixIn, from distance dimension choose around unit to be detected w range cell and be used as and refer to unit,
Then the detection statistic ξ of unit d to be detected is：
<mrow>
<mi>&xi;</mi>
<mo>=</mo>
<mi>m</mi>
<mi>e</mi>
<mi>d</mi>
<mi>i</mi>
<mi>a</mi>
<mi>n</mi>
<mo>{</mo>
<msub>
<mover>
<mi>z</mi>
<mo>&OverBar;</mo>
</mover>
<mi>i</mi>
</msub>
<mo>}</mo>
<mo>,</mo>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<mi>w</mi>
<mo>,</mo>
</mrow>
Wherein：Median is to take median operation；
(5) in log space matrixIn, from distance dimension choose around unit to be detected s range cell and be used as and refer to unit,
Then the detection threshold T of unit d to be detected is：
<mrow>
<mi>T</mi>
<mo>=</mo>
<mi>&beta;</mi>
<mo>&times;</mo>
<mi>m</mi>
<mi>e</mi>
<mi>d</mi>
<mi>i</mi>
<mi>a</mi>
<mi>n</mi>
<mo>{</mo>
<msub>
<mover>
<mi>z</mi>
<mo>&OverBar;</mo>
</mover>
<mi>j</mi>
</msub>
<mo>}</mo>
<mo>,</mo>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<mi>s</mi>
<mo>,</mo>
<mi>&beta;</mi>
<mo>></mo>
<mn>1</mn>
<mo>,</mo>
</mrow>
Wherein：S ＞ w, β are adjustment parameter, and β is related with the Radar Cross Section SCR of target；
(6) size of comparing check statistic ξ and detection threshold T, judges that target whether there is：
If ξ >=T, show that range cell d to be detected has target,
If ξ ＜ T, show that range cell d to be detected does not have target；
(7) length dimension estimation is carried out using the testing result obtained in step (6)：
(7a) ties up the initial position r for marking each large-scale target in distance to the large-scale object detection results in step (6)1,
Final position r2；
(7b) then corresponds to length dimension rough estimate of the target on the direction corresponding to the frame and is calculated as：
L=| r2-r1| × Δ r,
Wherein, | | represent modulus, Δ r is distance resolution.
2. the method as described in claim 1, it is characterised in that choose w around unit to be detected from distance dimension in step (4)
A range cell, carries out according to the following rules：
Assuming that the scope of large-scale target length size interested is [Lmin,Lmax], what is extended without considering large-scale target echo
In the case of, the scope for the range cell number M that large-scale target occupies is：Wherein Δ r is distance resolution, then
The reference estimation unit number w scopes of selection meet：
3. the method as described in claim 1, it is characterised in that choose s around unit to be detected from distance dimension in step (5)
A range cell, is the value range satisfaction according to reference unit number s：Condition choose, wherein：Δ r is distance
Resolution ratio, LmaxFor the upper bound of large-scale target length size range interested.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711075843.8A CN108008374B (en) | 2017-11-06 | 2017-11-06 | Sea surface large target detection method based on energy median |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711075843.8A CN108008374B (en) | 2017-11-06 | 2017-11-06 | Sea surface large target detection method based on energy median |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108008374A true CN108008374A (en) | 2018-05-08 |
| CN108008374B CN108008374B (en) | 2021-05-04 |
Family
ID=62051283
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711075843.8A Active CN108008374B (en) | 2017-11-06 | 2017-11-06 | Sea surface large target detection method based on energy median |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108008374B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110736971A (en) * | 2019-11-05 | 2020-01-31 | 中国电波传播研究所（中国电子科技集团公司第二十二研究所） | real-time recognition method for non-cooperative targets in sea clutter measurement area of shore-based radar |
| CN113281729A (en) * | 2021-05-31 | 2021-08-20 | 中国科学院声学研究所 | Target automatic detection method and system based on multi-frame spatial spectrum joint processing |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120195214A1 (en) * | 2011-01-28 | 2012-08-02 | Nec Laboratories America, Inc. | Multitaper spectrum sensing systems and methods |
| CN103399310A (en) * | 2013-08-07 | 2013-11-20 | 中国人民解放军海军航空工程学院 | Method for detecting radar weak moving target based on PD (Phase Differentiation) RLVD (Radon-Lv Distribution) |
| CN103954951A (en) * | 2014-05-13 | 2014-07-30 | 西安电子科技大学 | Power mid value and normalization covariance estimation based sea-surface target detection method |
| CN104155646A (en) * | 2014-08-22 | 2014-11-19 | 西安电子科技大学 | Sea surface low-speed motion object detection method based on Doppler spectrum signature |
| CN105425230A (en) * | 2015-11-11 | 2016-03-23 | 西安电子科技大学 | Range-extended target adaptive detection method under inverse Gaussian texture complex Gaussian clutter |
| CN105548986A (en) * | 2016-01-08 | 2016-05-04 | 西安电子科技大学 | Prewhitening ratio median detector based object detection method in sea cluster background |
-
2017
- 2017-11-06 CN CN201711075843.8A patent/CN108008374B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120195214A1 (en) * | 2011-01-28 | 2012-08-02 | Nec Laboratories America, Inc. | Multitaper spectrum sensing systems and methods |
| CN103399310A (en) * | 2013-08-07 | 2013-11-20 | 中国人民解放军海军航空工程学院 | Method for detecting radar weak moving target based on PD (Phase Differentiation) RLVD (Radon-Lv Distribution) |
| CN103954951A (en) * | 2014-05-13 | 2014-07-30 | 西安电子科技大学 | Power mid value and normalization covariance estimation based sea-surface target detection method |
| CN104155646A (en) * | 2014-08-22 | 2014-11-19 | 西安电子科技大学 | Sea surface low-speed motion object detection method based on Doppler spectrum signature |
| CN105425230A (en) * | 2015-11-11 | 2016-03-23 | 西安电子科技大学 | Range-extended target adaptive detection method under inverse Gaussian texture complex Gaussian clutter |
| CN105548986A (en) * | 2016-01-08 | 2016-05-04 | 西安电子科技大学 | Prewhitening ratio median detector based object detection method in sea cluster background |
Non-Patent Citations (2)
| Title |
|---|
| PENG-LANG SHUI ET AL.: "Shape-Parameter-Dependent Coherent Radar Target Detection in K-distributed Clutter", 《IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS》 * |
| 尚海燕 等: "长观测时间内机动目标检测方法", 《系统工程与电子技术》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110736971A (en) * | 2019-11-05 | 2020-01-31 | 中国电波传播研究所（中国电子科技集团公司第二十二研究所） | real-time recognition method for non-cooperative targets in sea clutter measurement area of shore-based radar |
| CN113281729A (en) * | 2021-05-31 | 2021-08-20 | 中国科学院声学研究所 | Target automatic detection method and system based on multi-frame spatial spectrum joint processing |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108008374B (en) | 2021-05-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104569949B (en) | Radar target detection method based on combined adaptive normalized matched filter | |
| CN105785340B (en) | The angle measurement and recognition methods that monopulse radar interferes target in main lobe and bait | |
| CN105425230A (en) | Range-extended target adaptive detection method under inverse Gaussian texture complex Gaussian clutter | |
| CN108919224B (en) | Broadband radar target self-adaptive fusion detection method based on oblique symmetrical structure | |
| CN106127110B (en) | A kind of human body fine granularity motion recognition method based on UWB radar and optimal SVM | |
| CN107861107A (en) | A kind of double threshold CFAR suitable for continuous wave radar and Plot coherence method | |
| CN104316914B (en) | Radar target self-adaptation detection method depending on shape parameters | |
| CN102879767B (en) | Detection method of interference target for space-time adaptive processing | |
| CN108919223B (en) | Broadband radar target self-adaptive structured matching filtering method | |
| CN103412290B (en) | Knowledge-assisted APR non-uniform sample detection method | |
| CN107576959B (en) | High repetition frequency radar target tracking method before detection based on area mapping deblurring | |
| CN103954951A (en) | Power mid value and normalization covariance estimation based sea-surface target detection method | |
| CN106872958A (en) | Radar target self-adapting detecting method based on linear fusion | |
| CN109100696B (en) | Slow-speed moving target elimination method based on dot clutter map | |
| CN104849707B (en) | Sea clutter suppression method based on multi-radar distributed detection | |
| CN113221631B (en) | Sequence pulse anti-interference target detection method based on convolutional neural network | |
| CN102608587B (en) | Air mobile target detection method based on nonlinear least square | |
| CN103197297B (en) | Radar moving target detection method based on cognitive framework | |
| CN104714225A (en) | Dynamic programming tracking-before-detection method based on generalized likelihood ratios | |
| CN103308910B (en) | Method for detecting offshore non-navigational state ship target by using high-frequency ground wave radar | |
| CN107942324A (en) | Multi-frame joint Small object double check method based on Doppler's guiding | |
| CN104714215A (en) | Clutter intensity zoning based constant-false-alarm detection method | |
| CN108181620A (en) | A kind of three-dimensional radar point mark method for evaluating quality | |
| CN114660585B (en) | Method and device for determining noise floor estimation value, electronic equipment and storage medium | |
| CN108008374A (en) | The large-scale object detection method in sea based on energy intermediate value |
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 |