CN112198486A - Extremely narrow pulse radar distance correlation target echo space aggregation method - Google Patents
Extremely narrow pulse radar distance correlation target echo space aggregation method Download PDFInfo
- Publication number
- CN112198486A CN112198486A CN202010900311.9A CN202010900311A CN112198486A CN 112198486 A CN112198486 A CN 112198486A CN 202010900311 A CN202010900311 A CN 202010900311A CN 112198486 A CN112198486 A CN 112198486A
- Authority
- CN
- China
- Prior art keywords
- tar
- target
- threshold
- points
- aggregation
- 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
- 230000002776 aggregation Effects 0.000 title claims abstract description 48
- 238000004220 aggregation Methods 0.000 title claims abstract description 48
- 238000000034 method Methods 0.000 title claims abstract description 30
- 239000012634 fragment Substances 0.000 claims abstract description 41
- 238000002592 echocardiography Methods 0.000 claims abstract description 13
- 230000000750 progressive effect Effects 0.000 claims abstract description 11
- 230000002146 bilateral effect Effects 0.000 claims abstract description 7
- 238000001514 detection method Methods 0.000 claims description 17
- 238000006116 polymerization reaction Methods 0.000 claims description 14
- 230000001419 dependent effect Effects 0.000 claims description 4
- 230000008569 process Effects 0.000 claims description 3
- 230000008030 elimination Effects 0.000 claims description 2
- 238000003379 elimination reaction Methods 0.000 claims description 2
- 238000002372 labelling Methods 0.000 claims description 2
- 238000012216 screening Methods 0.000 abstract description 9
- 230000000379 polymerizing effect Effects 0.000 abstract description 3
- 230000011218 segmentation Effects 0.000 description 4
- 230000004931 aggregating effect Effects 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 230000001174 ascending effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
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
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention discloses a method for polymerizing a range-correlated target echo space of a very narrow pulse radar, which comprises the steps of setting two thresholds, and respectively detecting a strong scattering point and a weak scattering point of a target HRRP; according to a distance similarity criterion, correlating strong scattering points and weak scattering points of the same target to obtain a primary aggregation fragment; then, searching bilateral valley points of the preliminary aggregation segments to determine the starting positions and the ending positions of the aggregation segments, further combining the aggregation segments in a progressive correlation manner, and screening and removing the aggregation segments with unreasonable signal-to-noise ratios and lengths to obtain fine aggregation segments so as to finish target echo aggregation; the method can accurately, quickly and stably aggregate the extremely narrow pulse echoes of the targets in the radar echoes containing a plurality of targets, noise and clutter.
Description
Technical Field
The invention belongs to the field of data processing of a narrow pulse radar, and particularly relates to a method for aggregating echo space of a range-correlated target of the narrow pulse radar.
Background
The ultra-narrow pulse radar is a type of radar with a single echo pulse width far smaller than the size of a target after processing. For a very narrow pulse radar, the echo of a target contains a plurality of very narrow pulses, each corresponding to a different scattering point on the target. Thus, the target extremely narrow pulse echo can represent the distribution of the scattering points of the target along the radar line-of-sight direction, also commonly referred to as the high resolution range image (HRRP) of the target.
Target echo space aggregation (hereinafter referred to as "echo aggregation") refers to gathering, in a very narrow pulse radar echo including a target, clutter, and noise, very narrow pulses belonging to the same target, and extracting a target echo segment (i.e., a target HRRP) to obtain effective information of the target. Target echo aggregation is a precondition for target identification, target parameter measurement and target tracking of the ultra-narrow pulse radar. Since the scattering point distribution characteristics are closely related to the type and pose of the target, the echo aggregation method needs to have good robustness to the target scattering point distribution form.
The distance-dependent echo aggregation is to use the distance-dependent correlation of different narrow pulses as a basis for judging whether the narrow pulses belong to the same target. The existing distance correlation echo aggregation method is a single threshold segmentation method, and the main idea is to set a fixed threshold, compare each data point in radar echo with the threshold, and mark the data point exceeding the threshold as a passing detection point; and taking the first and last passing detection points in the radar echo as the initial position and the final position of the target, and further extracting a target echo segment. In order to avoid false alarm, the threshold value of the single threshold segmentation method is often higher, and relatively weak echoes on a target may be missed, so that the aggregation result has deviation, and therefore, the accuracy and the robustness of the single threshold segmentation method are poor. Furthermore, the single threshold segmentation method cannot separately aggregate a plurality of targets, and has a great limitation.
Disclosure of Invention
In view of the above, the present invention provides a method for aggregating a space of extremely narrow pulse radar range-dependent target echoes, which can accurately, quickly and stably aggregate the extremely narrow pulse echoes of a target in radar echoes including multiple targets, noise and clutter.
The technical scheme for realizing the invention is as follows:
a method for spatial aggregation of echoes of an extremely narrow pulse radar distance correlation target comprises the following steps:
step one, calculating a signal-to-noise ratio of a radar echo peak point, and judging whether echo aggregation is performed or not;
determining a first threshold by using an order statistics constant false alarm rate detection (OS-CFAR) method; calculating a second threshold and a combination threshold based on the first threshold, wherein the second threshold is used for detecting strong scattering points, and the combination threshold is used for detecting weak scattering points; completing the detection of scattering points of the target HRRP by using a second threshold and a combination threshold; judging whether the adjacent detection points belong to the same target or not based on the detection result of the scattering points, and searching front and back bilateral valley points of a connected region where the same target is located to obtain a primary aggregation fragment;
and step three, carrying out progressive association on discrete fragments belonging to the same target, and removing the polymerization fragments with lower signal-to-noise ratio and length out of a reasonable range to obtain a final polymerization result.
Further, the specific process of the second step is as follows:
step 2.1, determining a first threshold G by using an ordered statistics constant false alarm detection method1；
Step 2.2, according to the power P of the radar echo peak pointpAnd a first threshold G1Calculating a second threshold G2And a combining threshold G3：
G2＝(Pp-G1)×c2 (8)
G3＝(Pp-G1)×c3 (9)
Wherein, c2、c3Respectively a second threshold coefficient and a combined threshold coefficient, and 0 < c3＜c2＜1；
Step 2.3, recording that the detected quantity V is greater than a threshold G2R points of (A) are strong scattering points
V＝P-G1，P＝[|x1|2,|x2|2,...,|xn|2]，x＝[x1,x2,...,xn],x∈R+X is the radar echo after the modulus is taken;
step 2.4, labeling
Head as first target1Then is followed by
Starting to traverse the strong scattering points;
step 2.5, recording the current traversal point
And
is a distance of
The current target is j; judgment of
And setting a threshold value T1The relationship of (1); if it is
Judging that the two points belong to the same target j, and performing step 2.8; if it is
Step 2.6 is carried out, and the relationship between the two points is further judged;
step 2.6, remember
And
all the pass-thresholds G between corresponding positions in x3S points of (a) are weak scattering points
Calculating the proportion R of the total points between two points:
judging R and setting threshold T2The relationship of (1): if R > T2If yes, judging that the two points belong to the same target j, and performing step 2.8; if R < T2If yes, judging that the two points do not belong to the same target, and performing step 2.7;
step 2.7, order
Tail of target jjThen target j is [ headj,tailj]The corresponding interval in the echo x; order to
Head for target j +1j+1And (5) performing step 2.8;
step 2.8, judging whether the traversal is finished or not, and if the traversal is finished, performing step 2.9; if the traversal is not finished, continuing the traversal and carrying out the step 2.5;
step 2.9, order
All target echo segments are obtained as the tail of the last target; and searching front and back bilateral valley points of a connected region where the same target is located to obtain a preliminary aggregation fragment.
Further, in the third step, the step of progressively associating the discrete segments belonging to the same target specifically includes:
when the number of the initial aggregation segments is 1, skipping progressive association operation and directly performing elimination operation; when the number of the initial polymerization fragments is M, M is more than or equal to 2, and the current initial polymerization fragment is taken as TarmM1, 2,3,.., M-1, statistical TarmNumber of strong scattering points in
If it is
And is
Then TarmAnd Tarm+1No association is made;
memory fragment TarmAnd Tarm+1The distance between is Length (Tar)m,Tarm+1) Judging Length (Tar)m,Tarm+1) And setting a threshold value T3The relationship of (1); if Length (Tar)m,Tarm+1)＞T3Then Tar ismAnd Tarm+1No association is made;
if it is
And Length (Tar)m-1,Tarm)＞T4,Length(Tarm,Tarm+1)＞T4Then Tar ism-1And Tarm+1No association is made;
and merging the remaining adjacent aggregation fragments into the same target to finish progressive association.
Has the advantages that:
the method adopts a narrow pulse radar target echo space aggregation method based on double-threshold detection, and can quickly and accurately divide targets, clutter, noise backgrounds and other targets in radar echoes at the same time. Compared with the prior art, the invention has the following advantages:
1. strong robustness and high accuracy
The method utilizes two groups of thresholds to detect the strong scattering points and the weak scattering points of the target respectively, can still detect the strong scattering point information and the weak scattering point information of the target under the condition that the target echo has fluctuation, and has strong robustness on the type and the posture of the target.
2. Can respectively aggregate echoes of a plurality of targets
The invention adopts two groups of thresholds to detect the scattering points of the target, the information is more accurate, and whether different scattering points belong to the same target can be judged, so that a plurality of target echoes in one frame of radar echo can be respectively aggregated.
Therefore, the method and the engineering implementation have high popularization and application values in the field of radar information processing.
Drawings
FIG. 1 is a general flow diagram of the method of the present invention.
Fig. 2 is a flowchart of the preliminary aggregation process based on the dual-threshold detection result according to the present invention.
FIG. 3 is a flow chart of fragment association and screening according to the present invention.
FIG. 4 is a graph comparing the original HRRP and the polymerization results.
Detailed Description
The invention is described in detail below by way of example with reference to the accompanying drawings.
The invention provides a method for aggregating the space of an echo of a distance-related target of a very narrow pulse radar, as shown in figure 1, the technical idea for realizing the method is as follows: setting two thresholds, and respectively detecting a strong scattering point and a weak scattering point of a target HRRP; according to a distance similarity criterion, correlating strong scattering points and weak scattering points of the same target to obtain a primary aggregation fragment; and then, carrying out bilateral valley point search (namely determining the starting position and the ending position of the aggregation fragment), progressive association (namely further merging the aggregation fragments) and screening (namely rejecting the aggregation fragments with unreasonable signal-to-noise ratios and lengths) on the preliminary aggregation fragments to obtain fine aggregation fragments so as to finish target echo aggregation.
Without loss of generality, it is assumed here that the radar echo includes multiple target echoes and noise. When clutter is present, it can be considered together with noise, as it is also random, and the target echo aggregation is done using the following steps, as "noise".
The invention mainly comprises the following four steps:
step one, judging whether radar echo needs to be subjected to target space aggregation
101. The modulo radar echo is recorded as x, which is a non-negative real vector and can be expressed by the following formula:
x＝[x1,x2,...,xn],x∈R+ (1)
102. finding the maximum value of x, and calculating the peak point power and recording as Pp：
Pp＝|max{x}|2 (2)
103. Ordering x by amplitude, resulting in an ascending sequence x':
x′＝[x′1,x′2,...,x′n] (3)
104. taking the first L points in x'1,x′2,...,x′L]Calculating the noise power Pc：
Wherein,
for rounding-down, 0<γ < 1 represents the proportion of data points used to calculate the noise power as described above.
105. Taking the ratio of the peak point power to the noise power as the peak point signal-to-noise ratio SNR:
106. comparing the SNR of the peak point with a set threshold T; if the SNR is greater than T, performing the processing of the second step; otherwise, the target does not exist in the current radar echo, target echo aggregation is not needed, and the current radar echo is stopped.
Step two, obtaining a target echo primary aggregation segment
The method comprises the following steps of obtaining a target scattering point by adopting an ordered statistics constant false alarm rate (OS-CFAR) idea; and then clustering the target scattering points together according to a proximity criterion to obtain a preliminary clustering fragment, as shown in FIG. 2.
201. K-th ordered value x ' in the sequence x ' with ascending amplitude 'kAs an estimate of the noise power level, a first threshold G is set1Is the value and the threshold factor c1The product of (a) and (b), namely:
G1＝c1·|x′k|2 (6)
202. calculating the power of each point of x and recording the power as a vector P [ | x [ ]1|2,|x2|2,...,|xn|2]Using P and a first threshold G1Calculating a detection quantity V:
V＝P-G1 (7)
203. according to the power P of the radar echo peak pointpAnd a first threshold G1Calculating a second threshold G2(for detecting strong scattering points) and a combining threshold G3(for detection of weak scattering points):
G2＝(Pp-G1)×c2 (8)
G3＝(Pp-G1)×c3 (9)
wherein c is2、c3Respectively a second threshold coefficient and a combined threshold coefficient, and 0 < c3＜c2＜1。
204. Recording the detected quantity V is greater than the threshold G2R points of (A) are strong scattering points
205. Marking
Head as first target1Then is followed by
The strong scattering points start to be traversed.
206. Recording current traversal point
And
is a distance of
The current target is j. Judgment of
And setting a threshold value T1The relationship (2) of (c). If it is
Judging that the two points belong to the same target j, and performing step 209; if it is
Then step 207 is performed to further determine the two-point relationship.
207. Note the book
And
all the pass-thresholds G between corresponding positions in x3S points of (a) are weak scattering points
Calculating the proportion R of the total points between two points:
judging R and setting threshold T2The relationship (2) of (c). If R > T2If yes, the two points belong to the same target j, and go to step 209; if R < T2If yes, then the two points are judged not to belong to the same target, and step 208 is performed.
208. Order to
Tail of target jjThen target j is [ headj,tailj]Corresponding intervals in the echo x. Order to
Head for target j +1j+1Proceed to step 209.
209. Judging whether the traversal is completed or not, and if the traversal is completed, performing step 210; if the traversal has not been completed, the traversal continues and step 206 is performed.
210. Order to
And all target echo segments are obtained as the tail of the last target.
211. And (3) carrying out front and back double-edge valley point search (namely determining a local minimum point) on the echo segment of each target in the echo x, and then expanding 2 points outwards in the echo x to obtain an initial aggregation segment.
Step three, carrying out progressive association and screening on initial aggregation fragments
301. When the initial number of aggregated fragments is 1, performing step 302; when the number of the initial polymerization fragments is M, M is more than or equal to 2, and the current initial polymerization fragment is taken as TarmM1, 2,3,.., M-1, statistical TarmNumber of strong scattering points in
If it is
And is
Then TarmAnd Tarm+1No association is made.
Memory fragment TarmAnd Tarm+1The distance between is Length (Tar)m,Tarm+1) Judging Length (Tar)m,Tarm+1) And setting a threshold value T3The relationship (2) of (c). If Length (Tar)m,Tarm+1)＞T3Then Tar ismAnd Tarm+1No association is made.
If it is
And Length (Tar)m-1,Tarm)＞T4,Length(Tarm,Tarm+1)＞T4Then Tar ism-1And Tarm+1No association is made.
And merging the remaining adjacent aggregation fragments into the same target to finish progressive association.
302. Note the current aggregation fragment TarmIs Length (Tar)m) The minimum length of the segment is L1Screening out Length (Tar)m)＜L1The polymeric fragment of (a);
303. computing Tar according to the steps 102-105mScreening out the aggregation fragments with the SNR less than T from the SNR of the peak point;
304. noting that the maximum length of the fragment is L2Judgment of TarmFront and rear L2And (4) whether other aggregated fragments with the number of strong scattering points being 1 exist or not, and screening the aggregated fragments if the aggregated fragments exist.
305. Screening for Length (Tar)m)＞L2And (4) outputting a final polymerization result.
Examples
The effect of the present invention can be illustrated by the following experiment of measured data
Setting a scene:
the radar echo used in this example contains 2 targets P.
Parameter selection:
signal-to-clutter ratio preset threshold T of 14dB
OS-CFAR order statistic order number k 178
Threshold factor c1＝20
Threshold factor c2＝0.0632
Threshold factor c3＝0.0316
Distance preset threshold T1＝2.5m
Weak scattering point ratio preset threshold T2＝0.3
Neighboring segment distance threshold T3＝10m
Neighboring point to fragment distance threshold T4＝4m
Minimum fragment length threshold L1＝1m
Threshold value L for maximum fragment length2＝10m
The experimental results are as follows:
fig. 4 shows the aggregation result, and it can be seen that the present invention accurately divides the target, the noise background, and other targets in one frame of radar echo at the same time.
The invention provides a method for polymerizing a target echo space of a narrow pulse radar based on double-threshold detection, which comprises the steps of respectively detecting strong scattering points and weak scattering points of a target by utilizing two groups of thresholds to obtain a primary polymerized segment, searching bilateral valley points, performing progressive association and screening on the primary polymerized segment to obtain a fine polymerized segment, and eliminating clutter and noise data to finish target space polymerization. The invention is an effective method for polymerizing the target echo space of the extremely narrow pulse radar, and can perform target space polymerization more optimally.
In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (3)
1. A method for spatial aggregation of echoes of an extremely narrow pulse radar distance correlation target is characterized by comprising the following steps:
step one, calculating a signal-to-noise ratio of a radar echo peak point, and judging whether echo aggregation is performed or not;
determining a first threshold by using an ordered statistics constant false alarm detection method; calculating a second threshold and a combination threshold based on the first threshold, wherein the second threshold is used for detecting strong scattering points, and the combination threshold is used for detecting weak scattering points; completing the detection of scattering points of the target HRRP by using a second threshold and a combination threshold; judging whether the adjacent detection points belong to the same target or not based on the detection result of the scattering points, and searching front and back bilateral valley points of a connected region where the same target is located to obtain a primary aggregation fragment;
and step three, carrying out progressive association on discrete fragments belonging to the same target, and removing the polymerization fragments with lower signal-to-noise ratio and length out of a reasonable range to obtain a final polymerization result.
2. The method for spatial aggregation of echoes of an extremely narrow pulse radar distance-dependent target according to claim 1, wherein the specific process of the second step is as follows:
step 2.1, determining a first threshold G by using an ordered statistics constant false alarm detection method1；
Step 2.2, according to the power P of the radar echo peak pointpAnd a first threshold G1Calculating a second threshold G2And a combining threshold G3：
G2＝(Pp-G1)×c2 (8)
G3＝(Pp-G1)×c3 (9)
Wherein, c2、c3Respectively a second threshold coefficient and a combined threshold coefficient, and 0 < c3＜c2＜1；
Step 2.3, recording that the detected quantity V is greater than a threshold G2R points of (A) are strong scattering points
V＝P-G1，P＝[|x1|2,|x2|2,...,|xn|2]，x＝[x1,x2,...,xn],x∈R+X is the radar echo after the modulus is taken;
step 2.4, labeling
Head as first target1At the same timeRear slave
Starting to traverse the strong scattering points;
step 2.5, recording the current traversal point
And
is a distance of
The current target is j; judgment of
And setting a threshold value T1The relationship of (1); if it is
Judging that the two points belong to the same target j, and performing step 2.8; if it is
Step 2.6 is carried out, and the relationship between the two points is further judged;
step 2.6, remember
And
all the pass-thresholds G between corresponding positions in x3S points of (a) are weak scattering points
Calculating the proportion R of the total points between two points:
judging R and setting threshold T2The relationship of (1): if R > T2If yes, judging that the two points belong to the same target j, and performing step 2.8; if R < T2If yes, judging that the two points do not belong to the same target, and performing step 2.7;
step 2.7, order
Tail of target jjThen target j is [ headj,tailj]The corresponding interval in the echo x; order to
Head for target j +1j+1And (5) performing step 2.8;
step 2.8, judging whether the traversal is finished or not, and if the traversal is finished, performing step 2.9; if the traversal is not finished, continuing the traversal and carrying out the step 2.5;
step 2.9, order
All target echo segments are obtained as the tail of the last target; and searching front and back bilateral valley points of a connected region where the same target is located to obtain a preliminary aggregation fragment.
3. The method according to claim 1 or 2, wherein in the third step, the step of progressively correlating discrete segments belonging to the same target specifically comprises:
when the number of the initial aggregation segments is 1, skipping progressive association operation and directly performing elimination operation; when the number of the initial polymerization fragments is M, M is more than or equal to 2, and the current initial polymerization fragment is taken as TarmM1, 2,3,.., M-1, statistical TarmNumber of strong scattering points in
If it is
And is
Then TarmAnd Tarm+1No association is made;
memory fragment TarmAnd Tarm+1The distance between is Length (Tar)m,Tarm+1) Judging Length (Tar)m,Tarm+1) And setting a threshold value T3The relationship of (1); if Length (Tar)m,Tarm+1)＞T3Then Tar ismAnd Tarm+1No association is made;
if it is
m is greater than or equal to 2, and Length (Tar)m-1,Tarm)＞T4,Length(Tarm,Tarm+1)＞T4Then Tar ism-1And Tarm+1No association is made;
and merging the remaining adjacent aggregation fragments into the same target to finish progressive association.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010900311.9A CN112198486B (en) | 2020-08-31 | 2020-08-31 | Extremely narrow pulse radar distance correlation target echo space aggregation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010900311.9A CN112198486B (en) | 2020-08-31 | 2020-08-31 | Extremely narrow pulse radar distance correlation target echo space aggregation method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112198486A true CN112198486A (en) | 2021-01-08 |
| CN112198486B CN112198486B (en) | 2021-07-20 |
Family
ID=74005196
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010900311.9A Active CN112198486B (en) | 2020-08-31 | 2020-08-31 | Extremely narrow pulse radar distance correlation target echo space aggregation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112198486B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115166649A (en) * | 2022-09-08 | 2022-10-11 | 北京理工大学 | Polarization detection method for feature aggregation target of scattering point of extremely narrow pulse radar |
| CN116299401A (en) * | 2023-05-19 | 2023-06-23 | 成都航空职业技术学院 | Constant false alarm method and device based on target scattering point position and storage medium thereof |
| CN118348484A (en) * | 2024-06-18 | 2024-07-16 | 成都天地一格科技有限公司 | Target fragment extraction method and device, electronic equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5465095A (en) * | 1994-08-05 | 1995-11-07 | The United States Of America As Represented By The Secretary Of The Air Force | Time efficient method for processing adaptive target detection thresholds in doppler radar systems |
| CN102628938A (en) * | 2012-04-29 | 2012-08-08 | 西安电子科技大学 | Combined Gaussian model radar target steady recognition method based on noise apriority |
| CN106597400A (en) * | 2016-11-15 | 2017-04-26 | 北京无线电测量研究所 | Ground moving vehicle target classification and recognition method and system based on high-resolution distance image |
| CN111256697A (en) * | 2020-02-24 | 2020-06-09 | 哈尔滨工业大学 | Unmanned aerial vehicle flight path planning method aiming at path point clustering machine learning |
| CN111413682A (en) * | 2020-05-08 | 2020-07-14 | 北京理工大学重庆创新中心 | Synthetic extremely narrow pulse radar detection threshold calculation method based on sequence statistics |
-
2020
- 2020-08-31 CN CN202010900311.9A patent/CN112198486B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5465095A (en) * | 1994-08-05 | 1995-11-07 | The United States Of America As Represented By The Secretary Of The Air Force | Time efficient method for processing adaptive target detection thresholds in doppler radar systems |
| CN102628938A (en) * | 2012-04-29 | 2012-08-08 | 西安电子科技大学 | Combined Gaussian model radar target steady recognition method based on noise apriority |
| CN106597400A (en) * | 2016-11-15 | 2017-04-26 | 北京无线电测量研究所 | Ground moving vehicle target classification and recognition method and system based on high-resolution distance image |
| CN111256697A (en) * | 2020-02-24 | 2020-06-09 | 哈尔滨工业大学 | Unmanned aerial vehicle flight path planning method aiming at path point clustering machine learning |
| CN111413682A (en) * | 2020-05-08 | 2020-07-14 | 北京理工大学重庆创新中心 | Synthetic extremely narrow pulse radar detection threshold calculation method based on sequence statistics |
Non-Patent Citations (2)
| Title |
|---|
| TENG LONG等: "Geometrical Structure Classification of Target HRRP Scattering Centers Based on Dual Polarimetric H Features", 《IEEE ACCESS》 * |
| 宋益恒等: "基于深度生成网络的雷达HRRP生成技术", 《信号处理》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115166649A (en) * | 2022-09-08 | 2022-10-11 | 北京理工大学 | Polarization detection method for feature aggregation target of scattering point of extremely narrow pulse radar |
| CN116299401A (en) * | 2023-05-19 | 2023-06-23 | 成都航空职业技术学院 | Constant false alarm method and device based on target scattering point position and storage medium thereof |
| CN116299401B (en) * | 2023-05-19 | 2023-10-17 | 成都航空职业技术学院 | Constant false alarm method and device based on target scattering point position and storage medium thereof |
| CN118348484A (en) * | 2024-06-18 | 2024-07-16 | 成都天地一格科技有限公司 | Target fragment extraction method and device, electronic equipment and storage medium |
| CN118348484B (en) * | 2024-06-18 | 2024-09-03 | 成都天地一格科技有限公司 | Target fragment extraction method and device, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112198486B (en) | 2021-07-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112198486B (en) | Extremely narrow pulse radar distance correlation target echo space aggregation method | |
| Kim et al. | Gaussian process regression flow for analysis of motion trajectories | |
| CN109948684B (en) | Quality inspection method, device and equipment for laser radar point cloud data labeling quality | |
| CN101702200B (en) | Automatic classification method of airborne laser radar point cloud data | |
| CN108171193B (en) | Polarized SAR (synthetic aperture radar) ship target detection method based on super-pixel local information measurement | |
| CN106778680B (en) | A kind of hyperspectral image band selection method and device based on critical bands extraction | |
| CN110297232A (en) | Monocular distance measuring method, device and electronic equipment based on computer vision | |
| CN112986926B (en) | PD radar ghost suppression method based on trace point attribute association discrimination | |
| CN110018461B (en) | Group target identification method based on high-resolution range profile and monopulse angle measurement | |
| WO2021135390A1 (en) | Working mode real-time classification method and apparatus suitable for monopulse lfm radar | |
| CN109613483B (en) | Multi-target track initiation method based on Hough transformation | |
| CN112215154B (en) | Mask-based model evaluation method applied to face detection system | |
| CN112213697B (en) | Feature fusion method for radar deception jamming recognition based on Bayesian decision theory | |
| CN111999726B (en) | Personnel positioning method based on millimeter wave radar | |
| CN109100697B (en) | Target condensation method based on ground monitoring radar system | |
| CN108344997B (en) | Road guardrail rapid detection method based on trace point characteristics | |
| CN111210458B (en) | Moving target tracking-before-detection method based on pre-detection confidence | |
| Schlecht et al. | Contour-based object detection. | |
| Huang et al. | Superpixel-based change detection in high resolution sar images using region covariance features | |
| CN105223559A (en) | A kind of long-range radar track initiation method switched that walks abreast | |
| CN108983194A (en) | A kind of Objective extraction and condensing method based on ground surveillance radar system | |
| CN104732190B (en) | A kind of synthetic aperture sonar object detection method based on orthogonal texture correlation analysis | |
| CN112198488B (en) | Extremely narrow pulse radar angle-associated target echo space aggregation method | |
| Lin et al. | Incorporating appearance and edge features for vehicle detection in the blind-spot area | |
| CN109283507B (en) | Radar target identification method and system based on time-frequency domain characteristics |
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 |