KR101745995B1 - Device and method for detecting moving object using high frequency radar - Google Patents

Device and method for detecting moving object using high frequency radar Download PDF

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KR101745995B1
KR101745995B1 KR1020150160533A KR20150160533A KR101745995B1 KR 101745995 B1 KR101745995 B1 KR 101745995B1 KR 1020150160533 A KR1020150160533 A KR 1020150160533A KR 20150160533 A KR20150160533 A KR 20150160533A KR 101745995 B1 KR101745995 B1 KR 101745995B1
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moving object
candidate
matrix
candidates
element analysis
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KR20170056991A (en
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고한석
박상욱
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고려대학교 산학협력단
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    • 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
    • G01S13/50—Systems of measurement based on relative movement of target
    • G01S13/505—Systems of measurement based on relative movement of target using Doppler effect for determining closest range to a target or corresponding time, e.g. miss-distance indicator
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging

Abstract

A moving object detection apparatus using a high frequency radar according to an embodiment of the present invention generates N RDMs (Range-Doppler Maps) at specific time points by using data received from N (N is at least 3 or more natural numbers) antennas An RDM generating unit; A matrix for calculating a covariance matrix of matrix conversion vectors for N vectors defined for the N RDMs and generating element analysis data to be used for candidate selection of moving objects using the analyzed unique components based on the covariance matrix; An operation unit; And selecting the candidates of the moving object using the element analysis data, generating a plurality of candidate clusters by density-based clustering based on the positions of the selected candidates, And an object detection unit for detecting the moving object based on the position of the final candidate community.

Description

TECHNICAL FIELD [0001] The present invention relates to a moving object detecting apparatus and a moving object detecting method using a high frequency radar,

Embodiments of the present invention relate to a moving object detection apparatus and method using a high frequency radar.

High frequency surface wave radar (HFSWR) is useful for continuously detecting and tracking ships, aircraft, icebergs, and other surface targets at coastal reference locations. Thus, high frequency surface wave radar (HFSWR) is being used to monitor maritime conditions, smuggling, drug trafficking, illegal fishing, smuggling, and piracy within the exclusive economic zone, as well as strengthening exploration and rescue operations.

A high frequency surface wave radar (HFSWR) system includes the hardware and software necessary for system operation, and a directional transmission antenna array and a receive antenna array that are oriented toward the ocean. The transmit antenna array generates a series of electromagnetic (EM) pulses that radiate to the desired surveillance zone. The receive antenna array preferably has the same high gain throughout the monitoring area.

An object in the surveillance zone reflects electromagnetic (EM) pulses to the receive antenna array, and the receive antenna array acquires radar data. Some objects are components that need to be detected and other objects are components that do not need to be detected. A very delicate pulse-coded or frequency-coded electromagnetic (EM) pulse is generated when a reflected electromagnetic (EM) pulse is received by a receive antenna array (in response to a previously transmitted electromagnetic (EM) ) Pulse may be used to compete with the range wrap after it is transmitted.

However, when a moving object such as a ship is detected, there is a problem that it is difficult to simultaneously observe the surface current and the ship. This is because the data generation cycle must be long because the surface ocean current does not change with time, but the data generation cycle must be short because the ship is relatively varied. Moreover, compact high frequency radar is also difficult to control the data generation cycle with geometry.

Related Prior Art Korean Patent Publication No. 10-2004-0091699 (entitled: Adaptive Detection System and Adaptive Detection Method in Radar Detection, published on Oct. 28, 2004) is available.

In order to simultaneously observe the sea water flow and the moving object using the same high frequency radar, one embodiment of the present invention analyzes the intrinsic components through signal processing using RDM (Range-Doppler Map) A moving object detection apparatus and method using the high frequency radar that can improve the detection performance of a moving object by detecting a moving object by determining a position of a final candidate community through the method.

The problems to be solved by the present invention are not limited to the above-mentioned problem (s), and another problem (s) not mentioned can be clearly understood by those skilled in the art from the following description.

A moving object detection apparatus using a high frequency radar according to an embodiment of the present invention generates N RDMs (Range-Doppler Maps) at specific time points by using data received from N (N is at least 3 or more natural numbers) antennas An RDM generating unit; A matrix for calculating a covariance matrix of matrix conversion vectors for N vectors defined for the N RDMs and generating element analysis data to be used for candidate selection of moving objects using the analyzed unique components based on the covariance matrix; An operation unit; And selecting the candidates of the moving object using the element analysis data, generating a plurality of candidate clusters by density-based clustering based on the positions of the selected candidates, And an object detection unit for detecting the moving object based on the position of the final candidate community.

The RDM is pre-processed and stored in the form of auto-correlation when generated from one antenna, and is pre-processed and stored in a form of cross-correlation when generated for each of the N antennas, And can be sequentially stored according to the time according to the viewpoint.

The matrix calculator may calculate the covariance matrix by calculating an expected value for a product of the matrix conversion vector and a complex conjugate of the matrix conversion vector.

Wherein the matrix calculator divides the covariance matrix into N eigenvectors corresponding to N eigenvalues and N eigenvectors corresponding to each of the eigenvalues, The element analysis data can be generated by projecting the conversion vector.

Wherein the matrix operation unit derives an expected value obtained by averaging a product of a product of the eigenvector corresponding to the largest eigenvalue and the matrix conversion vector and a complex conjugate of the product of the eigenvector and the matrix conversion vector, Analysis data can be generated.

Wherein the object detecting unit compares a product of a threshold value and a background noise value of a reference window applied to a distance cell at each Doppler frequency of the element analysis data with a value of a verification cell defined as an expected value of the element analysis data, Can be selected.

The object detecting unit may select the verification cell as a candidate for the moving object when the comparison cell is larger in value than the product of the background noise value and the threshold value as a result of the comparison.

The object detector may estimate the value of the background noise based on the values of remaining cells excluding the verification cell among the distance cells included in the reference window.

Wherein the object detecting unit derives a Doppler frequency index and a distance index of the verification cell in the RDM to estimate a position of a candidate of the moving object when the verification cell is selected as a candidate for the moving object, Based on the density-based clustering, the plurality of candidate clusters can be generated.

Wherein the detection condition is set in advance that the number of the candidates belonging to the candidate community is equal to or greater than a predetermined number, and the object detection unit detects the moving object based on the position of the remaining candidate community excluding the candidate community that does not satisfy the detection condition can do.

A moving object detection apparatus using a high frequency radar according to an embodiment of the present invention may derive an angle in a clockwise direction with respect to a candidate of the moving object on the basis of a position at which the N antennas are installed, Wherein the object detecting unit derives the distance to the candidate of the moving object based on the cell in which the candidate of the moving object is located in the RDM, and calculates the distance based on the derived distance and the estimated direction Based on the density-based clustering, the plurality of candidate clusters can be generated.

The object detecting unit converts the position of the candidate of the moving object to a position on the map by using the distance and angle of the moving object to the candidate and the position where the N antennas are installed, The moving object located at the coast can be removed from the candidate by comparing the position on the map of the candidate of the moving object.

Wherein the object detecting unit applies a weight corresponding to a signal size of each candidate in the RDM to an average position calculated by averaging a position on a map of each candidate in the final candidate cluster to move the average position, The average position can be calculated as the final position of the final candidate cluster.

Wherein the object detecting unit applies a weight according to time in the RDM of each candidate to an average position calculated by averaging a position on a map of each candidate in the final candidate cluster to move the average position, The position can be calculated as the final position of the final candidate cluster.

Wherein the object detecting unit detects a position of the moving object based on a final position of the final candidate cluster based on a difference between a final position of the final candidate cluster and a position on a map of each candidate in the final candidate cluster The boundary region is calculated, and the moving object can be detected based on the calculated boundary region.

The N antennas are installed on the shore or installed on a ship, and the moving object detection device can correct the speed and distance of the ship when the N antennas are installed on the ship.

A moving object detection method using a high frequency radar according to an embodiment of the present invention generates N RDMs (Range-Doppler Map) at specific time points using data received from N (N is at least 3 or more natural numbers) antennas ; Calculating a covariance matrix of matrix conversion vectors for N vectors defined for the N RDMs and generating element analysis data to be used for candidate selection of moving objects using the analyzed unique components based on the covariance matrix ; Selecting candidates for the moving object using the element analysis data; Generating a plurality of candidate clusters by density-based clustering based on the positions of the selected candidates; And detecting the moving object based on a position of a final candidate cluster satisfying a predetermined detection condition among the candidate clusters.

The details of other embodiments are included in the detailed description and the accompanying drawings.

According to an embodiment of the present invention, a moving object candidate is selected by analyzing an intrinsic component through signal processing using an RDM (Range-Doppler Map), and a moving object is detected by determining a position of a final candidate community , The detection performance of the moving object can be improved.

According to an embodiment of the present invention, surface ocean current observation and vessel observation can be simultaneously achieved in a marine situation observation system using a compact high frequency radar. Particularly, according to an embodiment of the present invention, when observing a ship using a compact high-frequency radar for surface layer current observation, the ship detection rate can be improved.

Therefore, according to the embodiment of the present invention, it is possible to apply to illegal ship detection and port management system through effective ship movement path tracking and prediction.

FIG. 1 is a block diagram illustrating a moving object detection apparatus using a high frequency radar according to an embodiment of the present invention. Referring to FIG.
2 to 4 are diagrams illustrating an example of an RDM (Range-Doppler Map) generated sequentially (t-2, t-1, t; t is time) at specific time points according to an embodiment of the present invention .
5 is a block diagram showing the detailed configuration of the object detection unit 140 of FIG.
6 is a view for explaining a reference window applied to an embodiment of the present invention.
7 is a diagram illustrating a process of selecting candidates for moving objects using the CFAR detection algorithm according to an embodiment of the present invention.
8 is a flowchart illustrating a moving object detection method using a high frequency radar according to an embodiment of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS The advantages and / or features of the present invention, and how to accomplish them, will become apparent with reference to the embodiments described in detail below with reference to the accompanying drawings. It should be understood, however, that the invention is not limited to the disclosed embodiments, but is capable of many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, To fully disclose the scope of the invention to those skilled in the art, and the invention is only defined by the scope of the claims. Like reference numerals refer to like elements throughout the specification.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

FIG. 1 is a block diagram for explaining a moving object detection apparatus using a high frequency radar according to an embodiment of the present invention. FIGS. 2 to 4 illustrate a moving object detection apparatus using a high frequency radar according to an embodiment of the present invention, FIG. 5 is a block diagram showing the detailed configuration of the object detecting unit 140 of FIG. 1 (see FIG. 5) . For reference, in the present embodiment, the high frequency radar may be implemented as a compact high frequency radar.

1, a moving object detection apparatus 100 using a high frequency radar according to an embodiment of the present invention includes an RDM generation unit 110, a matrix operation unit 120, an orientation estimation unit 130, an object detection unit 140, and a controller 150.

The RDM generation unit 110 generates N RDMs (Range-Doppler Maps) at specific time points using data received from N (N is at least 3 or more natural numbers) antennas. For example, the RDM generator 110 may generate N RDMs every 256 seconds using data received from N antennas. In the present embodiment, N is limited to three. It is to be understood that the present invention is not limited to the scope of the present invention.

In this embodiment, the antenna may be composed of three antennas which are orthogonal to each other in a three-dimensional space. In this case, the antennas 1 and 2 are cross-loop antennas of a high frequency radar, . Data received from the antennas 1, 2, and 3 may be converted to a distance-Doppler map (RDM) shape every 256 seconds. Therefore, three RDMs can be generated simultaneously in one high frequency radar device.

In this embodiment, the RDM generated by the antenna i X i (d, r) , and defined as 1≤i≤3, wherein d, r are defined as each Doppler frequency index (index) and the distance indicator. The RDM generated at each antenna can be stored in the form of auto-correlation and cross-correlation.

That is, when the RDM is generated from one antenna, the RDM is preprocessed and stored in an auto-correlation form. If the RDM is generated for each of the N antennas, the RDM may be preprocessed and stored in a cross- have. At this time, the RDM can be sequentially stored according to the time according to the specific time point.

In this embodiment, since three RDMs sequentially generated are required for signal processing in the present embodiment, one RDM generated at the present time and two RDMs generated at the previous time can be used among the stored RDMs.

2 to 4, a moving object is detected using RDM (t-2) and RDM (t-1) generated at the previous time and RDM (t) It is possible to carry out the subsequent signal processing. In FIGS. 2 to 4, each RDM is three auto-correlations sequentially generated using signals (data) received from any one of the antennas. (R) -doppler (d) cell (cell) estimated by the moving object in each RDM, when the RDM generation period is long, because the moving object ) 210 can change rapidly (move quickly) with time.

Meanwhile, the three antennas can be installed on the shore or installed on the ship. When the three antennas are installed on the ship, the moving object detection apparatus 100 can correct the speed and distance of the ship for detection of the ship.

The matrix calculator 120 analyzes the intrinsic components from the three RDMs and generates element analysis data to be used for candidate selection of a moving object.

For this, the matrix calculator 120 calculates a covariance matrix of matrix conversion vectors for the three vectors defined for the three RDMs, and analyzes the intrinsic components based on the covariance matrix, Component analysis data to be used in the candidate selection of the moving object.

Specifically, the matrix computing section 120 are the vectors X 1, X 2, X 3, defining the vectors X 1, X 2, X 3 for the RDM generated by the three antennas, as shown in Equation 1 below The matrix transformation vector X can be calculated by performing matrix transformation. The matrix calculator 120 calculates an expectation value of a product of the matrix conversion vector X and a complex conjugate of the matrix conversion vector X as shown in the following Equation 2 to calculate a covariance matrix X for the matrix conversion vector X A covariance matrix can be calculated.

[Equation 1]

Figure 112015111470510-pat00001

Here, d is a Doppler frequency index, r is a distance index, and X 1 , X 2 , and X 3 are vectors for RDM generated by three antennas. In addition, superscript T denotes matrix transformation, and X denotes a vector obtained by matrix transformation of X 1 , X 2 , and X 3 , that is, the matrix transformation vector.

&Quot; (2) "

Figure 112015111470510-pat00002

Here, d is a Doppler frequency index, r is a distance index, and X represents the matrix conversion vector. The superscript H denotes a complex conjugate transform, E denotes an expectation value, and C denotes a covariance matrix for the matrix transformation vector X.

The matrix calculator 120 divides the covariance matrix into three eigenvalues and three eigenvectors corresponding to the respective eigenvalues, as shown in Equation (3) .

&Quot; (3) "

Figure 112015111470510-pat00003

here,

Figure 112015111470510-pat00004
,
Figure 112015111470510-pat00005
,
Figure 112015111470510-pat00006
Represents the eigenvalue of the covariance matrix C for the matrix transformation vector X,
Figure 112015111470510-pat00007
,
Figure 112015111470510-pat00008
,
Figure 112015111470510-pat00009
Represents an eigenvector corresponding to each of the eigenvalues.

The matrix calculator 120 computes the three eigenvalues

Figure 112015111470510-pat00010
,
Figure 112015111470510-pat00011
,
Figure 112015111470510-pat00012
The largest eigenvalue of
Figure 112015111470510-pat00013
≪ / RTI >
Figure 112015111470510-pat00014
(Distance-Doppler cell) that has the greatest influence on the selection of the moving object by projecting the matrix transformation vector X into the matrix transformation vector X, thereby generating the element analysis data.

Specifically, the matrix calculator 120 computes the three eigenvalues

Figure 112015111470510-pat00015
,
Figure 112015111470510-pat00016
,
Figure 112015111470510-pat00017
The largest eigenvalue of
Figure 112015111470510-pat00018
≪ / RTI >
Figure 112015111470510-pat00019
And the matrix switching vector X
Figure 112015111470510-pat00020
And a complex conjugate of the product of the eigenvector and the matrix conversion vector
Figure 112015111470510-pat00021
Multiplied by
Figure 112015111470510-pat00022
As shown in the following equation (5)
Figure 112015111470510-pat00023
≪ / RTI >
Figure 112015111470510-pat00024
To generate the element analysis data. At this time,
Figure 112015111470510-pat00025
Are generated in chronological order. In Equation 5,
Figure 112015111470510-pat00026
Lt; RTI ID = 0.0 >
Figure 112015111470510-pat00027
And therefore only one value is generated regardless of the time.

&Quot; (4) "

Figure 112015111470510-pat00028

&Quot; (5) "

Figure 112015111470510-pat00029

The element analysis data generated as described above can be utilized as a distance element when estimating the position of the moving object with respect to the candidate. Therefore, in order to accurately estimate the position of the moving object with respect to the candidate, a direction element is required together with the distance element. Hereinafter, the direction estimating unit 130 for estimating the direction elements will be described.

The direction estimating unit 130 estimates the direction of the candidate of the moving object by deriving an angle in the clockwise direction with respect to the candidate of the moving object based on the position where the three antennas are installed. A MUSIC (Multiple Signal Classification) algorithm can be used to estimate the direction of the moving object candidate. The direction information estimated by the direction estimating unit 130 may be utilized as a basic data when the object detecting unit 140, which will be described later, generates a candidate cluster of the moving object.

The object detecting unit 140 may include a candidate selecting unit 510, a candidate group generating unit 520, and a moving object detecting unit 530 as shown in FIG.

The candidate selection unit 510 selects the candidate of the moving object using the element analysis data. In order to select a candidate for the moving object, the candidate selecting unit 510 may use a Constant False Alarm Rate (CFAR) detection algorithm. As shown in FIG. 6, the CFAR detection algorithm

Figure 112015111470510-pat00030
The reference window 610 is applied to the distance cell at each Doppler frequency of the window. Hereinafter, a candidate selection process using the CFAR detection algorithm will be described with reference to FIG. 7 and FIG.

Based on the values of the neighbor cells 1 616 and the adjacent cells 2 618, the candidate selecting unit 510 selects one of the distance cells included in the reference window 610 excluding the verification cell 612, Through Equation (6), the value (Z) of the background noise can be estimated (710). Reference numeral 614 in FIG. 7 denotes a protection cell.

That is, the candidate selecting unit 510 may verify the product 720 of the background noise value Z of the reference window 610 and the threshold value T by using the comparator 730 as shown in Equation (6) It is possible to select a candidate for the moving object (for example, a ship) by comparing it with the value of the cell 612.

At this time, the candidate selecting unit 510 determines whether the value of the verification cell 612 is greater than the product of the background noise value Z and the threshold value T as a result of the comparison in the comparator 730 The verification cell 612 can be selected as a candidate for the moving object (ship). Here, the value 612 of the verification cell is the expected value of the element analysis data

Figure 112015111470510-pat00031
. ≪ / RTI > Here, the value of the verify cell 612 refers to the size of the signal in the verify cell.

&Quot; (6) "

Figure 112015111470510-pat00032

here,

Figure 112015111470510-pat00033
Represents the value of the neighbor cell 1 (Y 1, Y 2, ... Y M) (616) and the adjacent cell 2 (Y M +1, Y M + 2, ... Y N) (616) , And Z represents the value of the background noise. At this time, the value of the adjacent cell refers to the size of the signal in the adjacent cell.

The candidate cluster generating unit 520 may generate a plurality of candidate clusters based on the density-based clustering based on the positions of the candidates selected by the candidate selecting unit 510.

Specifically, when the verification cell (see 612 in FIG. 7) is selected as a candidate for the moving object, the candidate cluster generation unit 520 generates a candidate cluster, which is a distance from the Doppler frequency index An index may be derived to estimate the position of the moving object with respect to the candidate. The candidate cluster generator 520 may generate the plurality of candidate clusters through the density-based clustering based on the estimated positions.

In other words, the candidate cluster generating unit 520 derives the distance to the candidate of the moving object based on the cell in which the candidate of the moving object is located in the RDM, and outputs the derived distance and the direction estimating unit 130, Based on the direction estimated by the density-based clustering method.

At this time, the candidate community generation unit 520 converts the position of the candidate of the moving object into a position on the map by using the distance and the angle (direction) with respect to the candidate of the moving object and the position where the three antennas are installed The moving object located on the coast can be removed from the candidate by comparing the coastal location information indicating the position of the coast with the position on the map of the candidate of the moving object.

The moving object detection unit 530 detects the moving object based on the position of the final candidate community satisfying the preset detection condition among the candidate clusters. Here, the detection condition may be set in advance such that the number of candidates belonging to the candidate cluster is equal to or greater than a predetermined number.

That is, the moving object detection unit 530 can detect the moving object based on the positions of the remaining candidate clusters excluding the candidate clusters that do not satisfy the detection condition.

At this time, the moving object detection unit 530 calculates a position of each candidate in the final candidate cluster,

Figure 112015111470510-pat00034
At the average position, a signal size in the RDM of each of the candidates or a weight
Figure 112015111470510-pat00035
To move the average position to a final position of the final candidate cluster
Figure 112015111470510-pat00036
.

For example, in Equation (7)

Figure 112015111470510-pat00037
The moving object detection unit 530 assigns a weight to a candidate having a large signal size in the RDM, moves the average position toward a larger signal size, and outputs the final position of the final candidate group
Figure 112015111470510-pat00038
Can be calculated.

&Quot; (7) "

Figure 112015111470510-pat00039

here,

Figure 112015111470510-pat00040
Is the kth cluster
Figure 112015111470510-pat00041
(The final candidate community)
Figure 112015111470510-pat00042
A signal size or time weight of each candidate in the RDM,
Figure 112015111470510-pat00043
Lt; RTI ID = 0.0 >
Figure 112015111470510-pat00044
The position of each candidate on the map.

On the other hand, in Equation (7)

Figure 112015111470510-pat00045
The moving object detection unit 530 applies a weight according to time in the RDM to each of the candidates at an average position obtained by averaging the positions on the map of the respective candidates belonging to the final candidate group By moving the average position, the moved average position may be calculated as the final position of the final candidate cluster.

That is, when there are more candidates in the RDMs near the current point in the RDM including the candidates, the moving object detector 530 assigns weights to the candidates over time, moves the average position toward the candidate, Final position of final candidate clusters

Figure 112015111470510-pat00046
Can be calculated.

For example, if 5 and 3 arbitrary candidates A and B are included in the current RDM (t) and 3 and 5 are included in the RDM (t-1) at the previous time, respectively, The moving object detection unit 530 moves the average position of the final candidate group to the position of the candidate A by giving a weight according to time to the candidate A,

Figure 112015111470510-pat00047
Can be calculated.

Alternatively, the moving object detector 530 may calculate a weight based on the signal size in the RDM and a weight according to the time of each candidate in the average position obtained by averaging the positions on the map of the respective candidates belonging to the final candidate cluster The average position may be calculated as the final position of the final candidate cluster by moving the average position.

The moving object detection unit 530 calculates the final position of the final candidate group

Figure 112015111470510-pat00048
And the final candidate community
Figure 112015111470510-pat00049
On the map of each candidate belonging to
Figure 112015111470510-pat00050
Based on the difference between the final position of the final candidate cluster
Figure 112015111470510-pat00051
A boundary region indicating a range in which the moving object can exist,
Figure 112015111470510-pat00052
, And the calculated boundary area
Figure 112015111470510-pat00053
It is possible to detect the moving object.

&Quot; (8) "

Figure 112015111470510-pat00054

here,

Figure 112015111470510-pat00055
A boundary region indicating a range in which the moving object can exist,
Figure 112015111470510-pat00056
Is the kth cluster
Figure 112015111470510-pat00057
(The final candidate community)
Figure 112015111470510-pat00058
A weight according to a signal size in the RDM of each of the candidates,
Figure 112015111470510-pat00059
Lt; RTI ID = 0.0 >
Figure 112015111470510-pat00060
The position of each candidate on the map.

For reference, in Equation (8)

Figure 112015111470510-pat00061
Is a 2 * 1 matrix
Figure 112015111470510-pat00062
Is a 1 * 2 matrix,
Figure 112015111470510-pat00063
Becomes a 2 * 2 matrix. Accordingly, the moving object detection unit 530 detects the moving object of the 2 * 2 matrix
Figure 112015111470510-pat00064
The moving object
Figure 112015111470510-pat00065
It is possible to calculate the extent to which the moving object can exist based on the result of the calculation.

The control unit 150 may be configured to detect the moving object detection apparatus 100 using the high frequency radar according to an embodiment of the present invention or the RDM generation unit 110, the matrix operation unit 120, the direction estimating unit 130, The operation of the object detection unit 140 and the like can be generally controlled.

8 is a flowchart illustrating a moving object detection method using a high frequency radar according to an embodiment of the present invention. The moving object detecting method may be performed by the moving object detecting apparatus 100 of FIG.

Referring to FIG. 8, in step 810, the moving object detection apparatus generates N RDMs at specific time points using data received from N antennas.

Next, in step 820, the moving object detection apparatus calculates a covariance matrix of matrix conversion vectors for N vectors defined for the N RDMs.

Next, in step 830, the moving object detection apparatus analyzes eigen components (N eigenvalues and N eigenvectors corresponding thereto) based on the covariance matrix.

Next, in step 840, the moving object detection apparatus generates element analysis data to be used for selecting a candidate of a moving object using the inherent component. That is, the moving object detection apparatus can generate the element analysis data by projecting the matrix conversion vector to an eigenvector corresponding to the largest eigenvalue among the N eigenvalues.

Next, in step 850, the moving object detection apparatus selects the candidate of the moving object using the element analysis data.

Next, in step 860, the moving object detection apparatus generates a plurality of candidate clusters through density-based clustering based on the positions of the selected candidates.

Next, in step 870, the moving object detection apparatus detects the moving object based on the position of the final candidate community satisfying the preset detection condition among the candidate clusters.

Embodiments of the present invention include computer readable media including program instructions for performing various computer implemented operations. The computer-readable medium may include program instructions, local data files, local data structures, etc., alone or in combination. The media may be those specially designed and constructed for the present invention or may be those known to those skilled in the computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floppy disks, and ROMs, And hardware devices specifically configured to store and execute the same program instructions. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like.

While the present invention has been described in connection with what is presently considered to be practical exemplary embodiments, it is to be understood that the invention is not limited to the disclosed embodiments. Therefore, the scope of the present invention should not be limited to the described embodiments, but should be determined by the scope of the appended claims and equivalents thereof.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments, but, on the contrary, Modification is possible. Accordingly, the spirit of the present invention should be understood only by the appended claims, and all equivalent or equivalent variations thereof are included in the scope of the present invention.

110: RDM generating unit
120: matrix operation unit
130:
140: Object detection unit
150:
510: Candidate selection unit
520: Candidate community generation unit
530: Moving object detection unit

Claims (17)

An RDM generator for generating N RDMs (Range-Doppler Maps) at specific time points using data received from N antennas (N is at least 3 or more natural numbers);
A matrix for calculating a covariance matrix of matrix conversion vectors for N vectors defined for the N RDMs and generating element analysis data to be used for candidate selection of moving objects using the analyzed unique components based on the covariance matrix; An operation unit; And
Selecting candidate candidates of the moving object using the element analysis data, generating a plurality of candidate clusters based on density-based clustering based on the positions of the selected candidates, An object detecting unit for detecting the moving object based on the position of the candidate cluster;
Lt; / RTI >
The object detection unit
The candidate of the moving object is selected by comparing the product of the background noise value of the reference window applied to the distance cell and the threshold value at each Doppler frequency of the element analysis data with the value of the verification cell defined by the expected value of the element analysis data Wherein the moving object detecting unit detects the moving object by using the high frequency radar.
The method according to claim 1,
The RDM
Processed in the form of auto-correlation if generated from one antenna, and stored in a form of cross-correlation when generated for each of the N antennas, Wherein the moving object detecting unit is sequentially stored according to time.
The method according to claim 1,
The matrix calculator
And calculates an expected value for a product of the matrix conversion vector and a complex conjugate of the matrix conversion vector to calculate the covariance matrix.
The method according to claim 1,
The matrix calculator
Dividing the covariance matrix into N eigenvalues and N eigenvectors corresponding to each of the eigenvalues and for projecting the matrix conversion vector into eigenvectors corresponding to the largest eigenvalues among the N eigenvalues, And generates the element analysis data by using the high frequency radar.
5. The method of claim 4,
The matrix calculator
An expected value obtained by averaging the product of the product of the eigenvector corresponding to the largest eigenvalue and the matrix conversion vector and the complex conjugate of the product of the eigen vector and the matrix conversion vector is derived to generate the element analysis data Wherein the moving object detecting unit detects the moving object by using the high frequency radar.
delete The method according to claim 1,
The object detection unit
Wherein the verification cell is selected as a candidate for the moving object when the value of the verification cell is larger than the product of the background noise value and the threshold value as a result of the comparison. .
The method according to claim 1,
The object detection unit
Wherein the background noise estimation unit estimates the value of the background noise based on a value of cells other than the verification cell among the distance cells included in the reference window.
The method according to claim 1,
The object detection unit
If the verification cell is selected as a candidate for the moving object, deriving a Doppler frequency index and a distance index of the verification cell in the RDM to estimate a position of the moving object with respect to the candidate, Wherein the plurality of candidate clusters are generated through density-based clustering.
The method according to claim 1,
The detection condition is
The number of the candidates belonging to the candidate cluster is set in advance to a predetermined number or more,
The object detection unit
And detects the moving object based on the positions of the remaining candidate clusters excluding the candidate clusters that do not satisfy the detection condition.
The method according to claim 1,
A direction estimating unit that derives an angle in a clockwise direction with respect to a candidate of the moving object based on a position at which the N antennas are installed and estimates a direction with respect to the candidate of the moving object,
Further comprising:
The object detection unit
Deriving a distance to the candidate of the moving object on the basis of the cell in which the candidate of the moving object is located in the RDM, calculating the distance based on the derived distance and the estimated direction, Wherein the moving object detecting unit detects the moving object by using the high frequency radar.
12. The method of claim 11,
The object detection unit
The position of the candidate of the moving object is converted into a position on the map by using the distance and angle of the moving object to the candidate and the position where the N antennas are installed and the position of the moving object And comparing the position of the candidate on the map to remove the moving object located at the coast from the candidate.
The method according to claim 1,
The object detection unit
Averaging the positions of the candidates belonging to the final candidate cluster by applying a weight according to signal magnitudes of the RDMs of the candidates to the average position to move the average position, As the final position of the final candidate community.
The method according to claim 1,
The object detection unit
And applying the weights according to time in the RDMs of the candidates to an average position obtained by averaging the positions of the candidates belonging to the final candidate cluster to move the average position, As the final position of the candidate cluster.
The method according to claim 1,
The object detection unit
A boundary region indicating a range in which the moving object can exist is calculated based on a final position of the final candidate cluster and a position on a map of each candidate in the final candidate cluster based on a final position of the final candidate cluster And the moving object is detected based on the calculated boundary area.
The method according to claim 1,
The N antennas
It is installed on the shore or installed on the ship,
The moving object detection device
Wherein when the N antennas are installed on the ship, the speed and distance of the ship are corrected.
Generating N RDM (Range-Doppler Map) data at a specific point in time using data received from N antennas (N is at least 3 or more natural numbers) antennas;
Calculating a covariance matrix of matrix conversion vectors for N vectors defined for the N RDMs and generating element analysis data to be used for candidate selection of moving objects using the analyzed unique components based on the covariance matrix ;
Selecting candidates for the moving object using the element analysis data;
Generating a plurality of candidate clusters by density-based clustering based on the positions of the selected candidates; And
Detecting the moving object based on a position of a final candidate cluster satisfying a predetermined detection condition among the candidate clusters
Lt; / RTI >
The step of selecting candidates of the moving object
The candidate of the moving object is selected by comparing the product of the background noise value of the reference window applied to the distance cell and the threshold value at each Doppler frequency of the element analysis data with the value of the verification cell defined by the expected value of the element analysis data And detecting the moving object by using the high frequency radar.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20190089292A (en) * 2018-01-22 2019-07-31 삼성전자주식회사 Method and apparatus for determinig object distance using radar
RU2726321C1 (en) * 2019-11-29 2020-07-13 Федеральное государственное бюджетное образовательное учреждение высшего образования "Рязанский государственный радиотехнический университет имени В.Ф. Уткина" Method of determining spatial position and speed in a group of objects by a system of doppler receivers

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR102312890B1 (en) * 2020-03-02 2021-10-15 국방과학연구소 Apparatus and method for detecting a small unmanned aerial vehicle(uav)
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101432932B1 (en) 2013-04-15 2014-09-23 광운대학교 산학협력단 Method and apparatus for estimating target in jammer scenario
JP5701106B2 (en) 2011-03-04 2015-04-15 富士通テン株式会社 Radar device and method of calculating angle of arrival of radar device

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP5701106B2 (en) 2011-03-04 2015-04-15 富士通テン株式会社 Radar device and method of calculating angle of arrival of radar device
KR101432932B1 (en) 2013-04-15 2014-09-23 광운대학교 산학협력단 Method and apparatus for estimating target in jammer scenario

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20190089292A (en) * 2018-01-22 2019-07-31 삼성전자주식회사 Method and apparatus for determinig object distance using radar
KR102455634B1 (en) 2018-01-22 2022-10-17 삼성전자주식회사 Method and apparatus for determinig object distance using radar
RU2726321C1 (en) * 2019-11-29 2020-07-13 Федеральное государственное бюджетное образовательное учреждение высшего образования "Рязанский государственный радиотехнический университет имени В.Ф. Уткина" Method of determining spatial position and speed in a group of objects by a system of doppler receivers

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