CN113189573B - Phased array search radar sea surface target detection method - Google Patents

Phased array search radar sea surface target detection method Download PDF

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CN113189573B
CN113189573B CN202110427811.XA CN202110427811A CN113189573B CN 113189573 B CN113189573 B CN 113189573B CN 202110427811 A CN202110427811 A CN 202110427811A CN 113189573 B CN113189573 B CN 113189573B
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clutter
doppler
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CN113189573A (en
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鲁振兴
张焱
管吉兴
洪永彬
尹伟
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CETC 54 Research Institute
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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
    • G01S13/04—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
    • 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
    • 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
    • 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
    • G01S7/415—Identification of targets based on measurements of movement associated with the 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
    • 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
    • G01S2013/0236—Special technical features
    • G01S2013/0245—Radar with phased array antenna

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  • Radar, Positioning & Navigation (AREA)
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Abstract

The invention discloses a phased array search radar sea surface target detection method, and belongs to the technical field of target detection. The method is designed for constant false alarm detection methods under a clutter background and a noise background respectively through distance-speed two-dimensional clutter area division, meanwhile, the influence of sea peaks is eliminated by adopting binary accumulation in scanning, the false alarm probability is reduced, and meanwhile, the weak target detection performance under strong clutter is improved. The invention utilizes the space, time and frequency distribution characteristics of sea clutter in the phased array radar to solve the problem that sea clutter suppression and small target detection in the sea radar are difficult to consider at the same time. Compared with the traditional detection algorithm based on a single background, the method can realize the optimal detection of the target in both the clutter area and the non-clutter area, and improve the sea reconnaissance monitoring capability of the phased array search radar in the complex environment.

Description

Phased array search radar sea surface target detection method
Technical Field
The invention relates to the technical field of target detection, in particular to a phased array search radar sea surface target detection method.
Background
At present, the phased array search radar mainly adopts a traditional Constant False-Alarm Rate (CFAR) based on a noise background or a double-parameter Constant False-Alarm Rate detection method based on a clutter background for detecting a sea surface target. This is done assuming that the background of the target detection is rayleigh distributed receiver noise or complex gaussian distributed (e.g., lognormal or K-distributed) sea clutter. However, in a practical radar receiver, the clutter does not always dominate in the range-velocity two-dimensional plane, but the clutter intensity is large in the short-range and low-velocity regions. Because the amplitude distribution of the sea clutter is obviously trailing and seriously deviates from Rayleigh distribution, if the traditional CFAR algorithm is adopted in a clutter area, clutter false alarms are obviously increased. And a double-parameter CFAR detection method is adopted in a noise area, and the detection threshold is possibly raised due to larger error of shape parameter estimation, so that the detection performance of a long-distance target is reduced. Therefore, it is necessary to select a suitable target detection algorithm in different regions according to the distribution of the clutter, so as to improve the target detection capability while reducing the clutter false alarm.
Disclosure of Invention
In view of the above, the invention provides a method for detecting a sea target of a phased array search radar, which performs target detection based on binary accumulation between clutter area division and scanning, and can improve weak target detection performance under strong clutter while reducing false alarm probability.
The purpose of the invention is realized as follows:
a phased array search radar sea surface target detection method comprises the following steps:
step 1: obtaining amplitude information of a range-Doppler matrix formed by phased array search radar coherent accumulation to obtain a range-Doppler image;
step 2: measuring the motion speed v of the platform through inertial navigation equipment or GPS equipment of the platform, then calculating an included angle theta between a beam direction and the motion direction of the platform in real time according to angle information given by antenna servo, and calculating the Doppler frequency f corresponding to a static targetd0：
Figure BDA0003030199460000021
Wherein λ is the radar operating wavelength;
and step 3: moving the Doppler center of the range-Doppler image to fd0Obtaining a distance-Doppler image X after motion compensation;
and 4, step 4: the distance-Doppler image X obtained in the step 3 is differentiated along the distance direction, and the distance point R with the maximum average difference absolute value is obtained1；
And 5: differentiating the range-Doppler image X obtained in the step 3 along the Doppler direction to obtain a Doppler point f with the maximum average differential valued1Doppler point f with minimum average difference valued2；
Step 6:the distance in the distance-Doppler image X obtained in the step 3 is smaller than R1And the Doppler frequency is at fd1And fd2The region between the two is divided into clutter regions, and other regions in the range-Doppler image X are divided into non-clutter regions;
and 7: in the clutter areas divided in the step 6, target detection is carried out by adopting a K distribution-based two-parameter constant false alarm detection algorithm; in the non-clutter region divided in the step 6, target detection is carried out by adopting a conventional unit average constant false alarm detection algorithm;
and 8: 7, after the detection is finished, generating a range-Doppler binary image;
and step 9: compressing the range-Doppler binary image generated in the step 8 into a range dimension binary sequence, performing binary accumulation on range dimension detection results of multiple scanning, judging that the target exists if the number of times of the target at the same position in the multiple scanning is larger than a threshold value, and otherwise, judging that the target does not exist.
Further, in step 7, the specific way of detecting the target in the clutter region is as follows:
701: selecting a unit to be detected in a clutter region, taking a distance unit adjacent to the front and the back of the unit to be detected as a protection unit, taking a distance unit between 2 and P +1 in the front and the back of the unit to be detected as a reference unit, wherein the total number of the reference units is 2P, and P is more than or equal to 2;
702: estimating a clutter distribution shape parameter v according to a reference unit:
Figure BDA0003030199460000031
wherein the content of the first and second substances,
Figure BDA0003030199460000032
xiis the absolute value of the ith reference cell;
703: estimating average power of clutter from reference cells
Figure BDA0003030199460000033
Figure BDA0003030199460000034
704: selecting a threshold parameter T according to the clutter distribution shape parameter v and the following table relation1
Shape parameter v <0 0-0.1 0.1-0.3 0.3-0.5 0.5-0.8 0.8-1.2 1.2-2 2-5 >5
Threshold parameter T1 15dB 25dB 23dB 20dB 19dB 18dB 16dB 14dB 13dB
Setting the detection threshold of the clutter unit as follows:
Figure BDA0003030199460000035
if the amplitude of the unit to be detected is larger than T, the detection result of the unit to be detected is 1, otherwise, the detection result of the unit to be detected is 0;
705: repeating the steps 701-704 until the unit to be detected traverses all clutter areas;
the specific mode of target detection in the non-clutter region is as follows:
706: in a non-clutter region, selecting a unit to be detected, taking a distance unit adjacent to the front and the back of the unit to be detected as a protection unit, taking a distance unit between 2 and Q +1 in the front and the back of the unit to be detected as a reference unit, wherein the total number of the reference units is 2Q, and Q is more than or equal to 2;
707: estimating the average power of the background noise from a reference unit
Figure BDA0003030199460000041
Figure BDA0003030199460000042
Wherein x isiIs the absolute value of the ith reference cell;
708: according to false alarm probability PfaDetermining a threshold parameter T2：
Figure BDA0003030199460000043
And calculating the detection threshold of the unit as:
Figure BDA0003030199460000044
if the amplitude of the unit to be detected is larger than T, the detection result of the unit to be detected is 1, otherwise, the detection result of the unit to be detected is 0;
709: and repeating the steps 706-708 until the unit to be detected traverses all the non-clutter areas.
Further, the specific manner of step 9 is as follows:
901: compressing the range-Doppler binary image generated in the step 8 into a range dimension binary sequence, and only keeping the threshold passing point with the maximum amplitude when a plurality of threshold passing points exist in the same range unit;
902: performing multiple scanning, and generating a distance dimension binary sequence in each scanning;
903: adding all the distance dimension binary sequences obtained in the step 902 to form an accumulated distance dimension sequence;
904: performing sliding window accumulation on the distance dimension sequence generated by 903;
905: and detecting the distance dimensional sequence after the sliding window accumulation, judging that the target exists if the image value is greater than a threshold value, and otherwise, judging that the target does not exist.
Compared with the background technology, the invention has the advantages that:
1. the invention designs a two-stage target detection algorithm of region detection in a scanning period and binary detection in a scanning period aiming at the difference of detection background amplitude distribution and time-varying characteristics of sea peaks of Doppler regions of different distances of a phased array search radar.
2. By using the method, the sea clutter false alarm can be reduced in the clutter area, and meanwhile, the detection performance of the small target can be improved; in a non-clutter region, the method can still realize the optimal detection of the target under the noise background, and provides technical support for the enhancement of the sea surface reconnaissance monitoring capability of the phased array search radar.
Drawings
FIG. 1 is a flow chart of a method of sea surface target detection in an embodiment of the present invention;
FIG. 2 is a flow chart of range-Doppler partition detection processing in an embodiment of the invention;
FIG. 3 is a flowchart illustrating an exemplary inter-scan binary detection process according to an embodiment of the present invention.
Detailed Description
The technical solution of the present invention will be further described in detail with reference to the accompanying drawings and the detailed description.
A phased array search radar sea surface target detection method is realized by two-stage processing of region detection in a scanning period and binary detection in the scanning period, and comprises the following specific steps:
step 1: the phased array search radar transmits a coherent pulse string in a certain scanning beam, the radar performs pulse compression and coherent accumulation on a received echo to form a complex range-Doppler matrix, matrix amplitude information is obtained, and a range-Doppler image X is obtained0；
In the embodiment, the scanning beam width is 3 °, the distance element number of the distance-doppler matrix is 2000, the doppler element number is 128, and the size of the obtained distance-doppler image is 2000 × 128.
Step 2: measuring the motion speed v of the platform by inertial navigation equipment or GPS equipment of the platform, then calculating the included angle theta between the beam direction and the motion direction of the platform in real time according to angle information given by antenna servo, and calculating the Doppler frequency corresponding to a static target
Figure BDA0003030199460000061
Wherein λ is the radar operating wavelength;
and step 3: moving the Doppler center of the range-Doppler image to fd0Obtaining a motion compensated range-Doppler image X;
in the examples, fd0Corresponding to the 10 th Doppler cell, X0＝[x1,x2,...,x128]，X＝[x10,x11,...,x128,x1,x2,...,x9]Wherein x isiA 2000 x 1 column vector.
And 4, step 4: differentiating the range-Doppler images obtained in the step 3 along the range direction, and solving the range point R with the maximum average difference absolute value1；
And 5: differentiating the range-Doppler images obtained in the step (3) along the Doppler direction to obtain a Doppler point f with the maximum average differential valued1Doppler point f with minimum average difference valued2；
Step 6: the distance in the distance-Doppler image obtained in the step 3 is smaller than R1Doppler frequency at fd1And fd2The region between the two is divided into a clutter region, and other regions in the range-Doppler image are divided into a non-clutter region;
and 7: in the clutter areas divided in the step 6, target detection is carried out by adopting a K distribution-based two-parameter CFAR algorithm; in the non-clutter areas divided in the step 6, target detection is carried out by adopting a conventional CA-CFAR algorithm;
the method specifically comprises the following steps:
701: selecting a unit to be detected in the clutter region, taking a distance unit adjacent to the front and the back of the unit to be detected as a protection unit, taking a distance unit between 2 and P +1 in the front and the back of the unit to be detected as a reference unit, wherein the total number of the reference units is 2P;
in an embodiment, P is 16 and the total number of reference cells is 32.
702: and estimating the distribution shape parameter v of the clutter according to the reference unit, wherein the calculation mode is as follows:
Figure BDA0003030199460000071
wherein
Figure BDA0003030199460000072
xiIs the absolute value of the ith reference cell;
703: estimating the average power of the clutter according to the reference unit:
Figure BDA0003030199460000073
704: calculating a threshold parameter T based on the clutter distribution shape parameter v and the false alarm probability1The detection threshold of the clutter unit is:
Figure BDA0003030199460000074
if the amplitude of the unit to be detected is larger than T, the detection result of the unit to be detected is 1, otherwise, the detection result of the unit to be detected is 0;
in an embodiment, the threshold parameter T1And the shape parameter v as follows:
shape parameter v Less than 0 0-0.1 0.1-0.3 0.3-0.5 0.5-0.8 0.8-1.2 1.2-2 2-5 ＞5
Detection threshold T1 15dB 25dB 23dB 20dB 19dB 18dB 16dB 14dB 13dB
705: repeating the steps 701-704 until the unit to be detected traverses all clutter areas;
706: in a non-clutter region, selecting a unit to be detected, taking a distance unit adjacent to the front and the back of the unit to be detected as a protection unit, taking a distance unit between 2 and Q +1 in the front and the back of the unit to be detected as a reference unit, wherein the total number of the reference units is 2Q;
in an embodiment, Q is 16 and the total number of reference units is 32.
707: estimating the average power of the background noise according to the reference unit:
Figure BDA0003030199460000075
wherein xiIs the absolute value of the ith reference cell;
708: determining a threshold parameter T based on the false alarm probability2And calculating the detection threshold of the unit:
Figure BDA0003030199460000076
if the amplitude of the unit to be detected is larger than T, the detection result of the unit to be detected is 1, otherwise, the detection result of the unit to be detected is 0;
in an embodiment, the threshold parameter T2Take 13 dB.
709: and repeating the steps 706-708 until the unit to be detected traverses all the non-clutter areas.
And 8: 7, after the detection is finished, generating a range-Doppler binary image;
and step 9: compressing the range-Doppler binary image generated in the step 8 into range dimension binary data, performing binary accumulation on range dimension detection results of multiple scanning, judging that the target exists if the number of times of the target at the same position in N times of scanning is more than or equal to M, and otherwise, judging that the target does not exist.
Wherein, the step 9 specifically comprises the following 5 steps:
901: compressing the range-Doppler binary image generated in the step 8 into a range dimension binary sequence, and only keeping the threshold passing point with the maximum amplitude when a plurality of threshold passing points exist in the same range unit;
902: after N times of scanning are finished, generating N distance dimension binary sequences;
903: adding the N distance dimension binary sequences to form an accumulated distance dimension sequence;
904: performing sliding window accumulation on the distance dimension sequence generated by 903;
905: and detecting the distance dimensional sequence after the sliding window accumulation, judging that the target exists if the image value is greater than or equal to M, and otherwise, judging that the target does not exist.
In the examples, N is 5 and M is 3.
In a word, the method designs a constant false alarm detection method under a clutter background and a noise background by distance-speed two-dimensional clutter area division, and simultaneously eliminates the influence of sea peaks by adopting binary accumulation in a scanning process, so that the false alarm probability can be reduced, and the weak target detection performance under strong clutter can be improved.

Claims (3)

1. A phased array search radar sea surface target detection method is characterized by comprising the following steps:
step 1: obtaining amplitude information of a range-Doppler matrix formed by phased array search radar coherent accumulation to obtain a range-Doppler image;
step 2: measuring the motion speed v of the platform through inertial navigation equipment or GPS equipment of the platform, then calculating an included angle theta between a beam direction and the motion direction of the platform in real time according to angle information given by antenna servo, and calculating the Doppler frequency f corresponding to a static targetd0：
Figure FDA0003030199450000011
Wherein λ is the radar operating wavelength;
and step 3: moving the Doppler center of the range-Doppler image to fd0Obtaining a distance-Doppler image X after motion compensation;
and 4, step 4: the distance-Doppler image X obtained in the step 3 is differentiated along the distance direction, and the distance point R with the maximum average difference absolute value is obtained1；
And 5: differentiating the range-Doppler image X obtained in the step 3 along the Doppler direction to obtain a Doppler point f with the maximum average differential valued1Doppler point f with minimum average difference valued2；
Step 6: the distance in the distance-Doppler image X obtained in the step 3 is smaller than R1And the Doppler frequency is at fd1And fd2The region between the two is divided into clutter regions, and other regions in the range-Doppler image X are divided into non-clutter regions;
and 7: in the clutter areas divided in the step 6, target detection is carried out by adopting a K distribution-based two-parameter constant false alarm detection algorithm; in the non-clutter region divided in the step 6, target detection is carried out by adopting a conventional unit average constant false alarm detection algorithm;
and 8: 7, after the detection is finished, generating a range-Doppler binary image;
and step 9: compressing the range-Doppler binary image generated in the step 8 into a range dimension binary sequence, performing binary accumulation on range dimension detection results of multiple scanning, judging that the target exists if the number of times of the target at the same position in the multiple scanning is larger than a threshold value, and otherwise, judging that the target does not exist.
2. The method for detecting the phased array search radar sea surface target according to claim 1, wherein in the step 7, the specific way of detecting the target in the clutter region is as follows:
701: selecting a unit to be detected in a clutter region, taking a distance unit adjacent to the front and the back of the unit to be detected as a protection unit, taking a distance unit between 2 and P +1 in the front and the back of the unit to be detected as a reference unit, wherein the total number of the reference units is 2P, and P is more than or equal to 2;
702: estimating a clutter distribution shape parameter v according to a reference unit:
Figure FDA0003030199450000021
wherein the content of the first and second substances,
Figure FDA0003030199450000022
xiis the absolute value of the ith reference cell;
703: estimating average power of clutter from reference cells
Figure FDA0003030199450000023
Figure FDA0003030199450000024
704: selecting a threshold parameter T according to the clutter distribution shape parameter v and the following table relation1
Shape parameter v <0 0-0.1 0.1-0.3 0.3-0.5 0.5-0.8 0.8-1.2 1.2-2 2-5 >5 Threshold parameter T1 15dB 25dB 23dB 20dB 19dB 18dB 16dB 14dB 13dB
Setting the detection threshold of the clutter unit as follows:
Figure FDA0003030199450000025
if the amplitude of the unit to be detected is larger than T, the detection result of the unit to be detected is 1, otherwise, the detection result of the unit to be detected is 0;
705: repeating the steps 701-704 until the unit to be detected traverses all clutter areas;
the specific mode of target detection in the non-clutter region is as follows:
706: in a non-clutter region, selecting a unit to be detected, taking a distance unit adjacent to the front and the back of the unit to be detected as a protection unit, taking a distance unit between 2 and Q +1 in the front and the back of the unit to be detected as a reference unit, wherein the total number of the reference units is 2Q, and Q is more than or equal to 2;
707: estimating the average power of the background noise from a reference unit
Figure FDA0003030199450000026
Figure FDA0003030199450000027
Wherein x isiIs the absolute value of the ith reference cell;
708: according to false alarm probability PfaDetermining a threshold parameter T2：
Figure FDA0003030199450000031
And calculating the detection threshold of the unit as:
Figure FDA0003030199450000032
if the amplitude of the unit to be detected is larger than T, the detection result of the unit to be detected is 1, otherwise, the detection result of the unit to be detected is 0;
709: and repeating the steps 706-708 until the unit to be detected traverses all the non-clutter areas.
3. The phased array search radar sea surface target detection method according to claim 1, wherein the specific manner of the step 9 is as follows:
901: compressing the range-Doppler binary image generated in the step 8 into a range dimension binary sequence, and only keeping the threshold passing point with the maximum amplitude when a plurality of threshold passing points exist in the same range unit;
902: performing multiple scanning, and generating a distance dimension binary sequence in each scanning;
903: adding all the distance dimension binary sequences obtained in the step 902 to form an accumulated distance dimension sequence;
904: performing sliding window accumulation on the distance dimension sequence generated by 903;
905: and detecting the distance dimensional sequence after the sliding window accumulation, judging that the target exists if the image value is greater than a threshold value, and otherwise, judging that the target does not exist.
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