CN114488133B - Method for extracting and classifying multidimensional scattering characteristics of satellite-borne GNSS-S radar ship - Google Patents
Method for extracting and classifying multidimensional scattering characteristics of satellite-borne GNSS-S radar ship Download PDFInfo
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- 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
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- 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
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
- G01S13/9004—SAR image acquisition techniques
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- G01—MEASURING; TESTING
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- 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
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
- G01S13/9021—SAR image post-processing techniques
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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
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
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- G—PHYSICS
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- 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
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/35—Constructional details or hardware or software details of the signal processing chain
- G01S19/37—Hardware or software details of the signal processing chain
Abstract
The invention relates to a method for extracting and classifying multidimensional scattering characteristics of a satellite-borne GNSS-S radar ship, which comprises the following steps: a. receiving ship target scattering signals of a plurality of navigation satellite signals by using a satellite-borne GNSS-S radar (10), and performing double-station SAR imaging on the signals to obtain a multi-dimensional SAR image of the ship target; b. carrying out non-coherent fusion processing on the multi-dimensional SAR image, and carrying out ship target detection on the fused image to obtain position information of a ship target; c. extracting the length direction and the bow direction of the ship target, and calculating the multidimensional scattering coefficient of the ship target; d. and constructing a vectorized multidimensional electromagnetic scattering set, and classifying the types of the ship targets by using a target classification network. The invention fully utilizes the multidimensional electromagnetic scattering information of the ship target, utilizes the convolutional neural network model to realize the highly reliable and intelligent classification of the ship target, has higher classification accuracy and can solve the problem of ship target classification under the condition of medium and high sea.
Description
Technical Field
The invention relates to a method for extracting and classifying multidimensional scattering characteristics of a satellite-borne GNSS-S radar ship.
Background
The detection and classification of the sea surface ship targets are related to ocean safety and marine transportation safety, and the main modes of the existing sea surface ship target detection include an optical satellite, a Synthetic Aperture Radar (SAR) satellite and the like. The optical satellite has the advantages of high resolution imaging, strong interpretation capability, high classification accuracy and the like, so the optical satellite is widely applied to earth and sea observation, but is influenced by weather such as sea cloud, fog, rain and the like, and the advantages of high resolution imaging observation and target classification are difficult to exert continuously. The SAR satellite realizes high-resolution imaging by transmitting a high-power electromagnetic signal and carrying out long-time synthetic aperture processing, and the electromagnetic signal can penetrate through clouds and fog, so that the SAR satellite has the advantages of all-weather observation all-weather, but the SAR satellite has high power consumption, short working time of each orbit, difficulty in continuously carrying out imaging detection on the sea, high transmitted signal power and easiness in interception and interference, and the imaging detection quality is limited.
With the improvement and the comprehensive open utilization of the satellite navigation system technology, the mode of detecting and classifying the sea surface ship targets by means of the multi-dimensional electromagnetic signals of a plurality of navigation satellites gradually becomes an important way for solving various defects of the existing sea surface ship target detection mode, and particularly, the intelligent classification of the scattering signals (GNSS-S) of the global navigation satellite system with a plurality of angles, a plurality of frequency bands and a plurality of polarizations by combining the same ship target is performed. The satellite-borne GNSS-S radar can simultaneously receive multi-dimensional signals of a plurality of navigation satellites and provide rich information basis for sea surface ship target detection and classification, but the signal power of the navigation satellite signals is low, and the signal bandwidth is narrow, so that the imaging resolution is low, and high-performance detection and classification by directly utilizing low-resolution images are difficult.
Disclosure of Invention
The invention aims to provide a method for extracting and classifying multidimensional scattering characteristics of a satellite-borne GNSS-S radar ship.
In order to achieve the above object, the present invention provides a method for extracting and classifying multidimensional scattering characteristics of a satellite-borne GNSS-S radar ship, comprising the following steps:
a. receiving ship target scattering signals of a plurality of navigation satellite signals by using a satellite-borne GNSS-S radar, and performing double-station SAR imaging on the signals to obtain a multi-dimensional SAR image of the ship target;
b. carrying out non-coherent fusion processing on the multi-dimensional SAR image, and carrying out ship target detection on the fused image to obtain position information of a ship target;
c. extracting the length direction and the warship bow direction of the warship target, and calculating the multidimensional scattering coefficient of the warship target;
d. and constructing a vectorized multidimensional electromagnetic scattering set, and classifying the types of the ship targets by using a target classification network.
According to one aspect of the invention, in said step (a), receiving a ship target scatter signal to obtain multi-dimensional observation information, including multi-angle, multi-band, multi-polarization of the ship target;
in the step (c), estimating the length and the width of the ship target to obtain the length direction of the ship target;
calculating scattering coefficients of the ship target at different angles, different frequency bands and different polarizations by using an electromagnetic scattering characteristic calculation unit to obtain a multidimensional scattering coefficient of the ship target;
in the step (d), the target classification network is a convolutional neural network model, the vectorized multi-dimensional electromagnetic scattering set is input as the ship target, and the output is the type of the ship target.
According to one aspect of the invention, a satellite-borne GNSS-S radar comprises:
the multi-channel antenna comprises an azimuth multi-channel antenna and a frequency band multi-channel antenna, wherein the azimuth multi-channel antenna is used for receiving multi-dimensional GNSS-S signals in a detection area, the antenna has three frequency band signal receiving capacity, the frequencies of the three frequency bands are fc1, fc2 and fc3 respectively, and the corresponding working bandwidths are Bw1, bw2 and Bw3 respectively; the antenna has left-hand circular polarization and right-hand circular polarization signal receiving capacity, and the polarization isolation degree is greater than 20dB; the whole antenna is divided into M receiving antennas along the azimuth direction, wherein M is 4-8, each receiving antenna consists of Na multiplied by Nr antenna units, na is 6-12, and Nr is 12-24;
the M six-channel receivers are used for performing low-noise amplification, band-pass filtering, down-conversion, intermediate-frequency low-noise amplification, intermediate-frequency analog-to-digital converter (ADC) sampling and digital domain IQ demodulation on GNSS-S signals of three frequency bands and two polarizations output by each receiving channel to obtain six baseband GNSS-S complex signals;
the digital beam forming processing unit is used for carrying out digital beam forming on GNSS-S signals of three frequency bands and two polarizations and forming P digital sub-beams in the azimuth direction;
the P matched filter groups are used for performing matched filtering on the GNSS-S signals output by each digital sub-beam by using signals emitted by N navigation satellites as reference signals to obtain N independent high signal-to-noise ratio GNSS-S signals and outputting P frame signal sets, and each frame signal set comprises 6N high signal-to-noise ratio GNSS-S signals;
after digital beam forming is carried out on the signals of the M receiving antennas, a plurality of high-gain narrow beams are formed, and the gain of each narrow beam is larger than 30dB.
According to an aspect of the invention, a digital beam forming processing unit includes:
a first P sets of complex multiply-add units for performing left-handed circularly polarized M GNSS-S signals SA at frequency band fc1 m (t) performing Digital Beamforming (DBF) processing along the azimuth direction to form P digital sub-beams, and outputting P high signal-to-noise ratio GNSS-S signals as:
a second P sets of complex multiply-add units for circularly polarized M GNSS-S signals SB at frequency band fc1 and right hand m (t) performing DBF processing along the azimuth direction to form P digital sub-beams, and outputting P GNSS-S signals with high signal-to-noise ratio as follows:
a third P sets of complex multiply-add units for performing left-handed circularly polarized M GNSS-S signals SC at frequency band fc2 m (t) performing DBF processing along the azimuth direction to form P digital sub-beams, and outputting P GNSS-S signals with high signal-to-noise ratio as follows:
a fourth P sets of complex multiplication and addition operation units for circularly polarizing M GNSS-S signals SD at the frequency band fc2 and the right hand m (t) performing DBF processing along the azimuth direction to form P digital sub-beams, and outputting P GNSS-S signals with high signal-to-noise ratio as follows:
a fifth P sets of complex multiplication and addition arithmetic units for performing left-handed circularly polarized M GNSS-S signals SE at the frequency band fc3 m (t) DBF processing is carried out along the azimuth direction to form P digital sub-beams, and P high signal-to-noise ratio GNSS-S signals are output as follows:
a sixth P complex multiplication and addition operation units for multiplying and adding the frequency band fc3 and the right-hand circularly polarized M GNSS-S signals SF m (t) performing DBF processing along the azimuth direction to form P digital sub-beams, and outputting P GNSS-S signals with high signal-to-noise ratio as follows:
a DBF weight calculating unit for calculating 6 sets of complex weights wa required by DBF processing p,m 、wb p,m 、wc p,m 、wd p,m 、we p,m 、wf p,m Calculating;
wherein t is a fast time variable; m is a serial number, and the value of M =1, 2., M; p is a serial number, and the value is P =1, 2.., P; SA m (t) are frequency band fc1, left-handed circularly polarized M GNSS-S signals; RA p (t) P high signal-to-noise ratio GNSS-S signals after DBF processing is carried out on the frequency band fc1 and the left-handed circularly polarized signals; wa (a) p,m The complex weight value is the complex weight value when DBF processing is carried out on the frequency band fc1 and the left-handed circularly polarized signal; SB (service bus) m (t) are frequency band fc1, right-hand circularly polarized M GNSS-S signals; RB (radio Beacon) p (t) P high signal-to-noise ratio GNSS-S signals after DBF processing is carried out on the frequency band fc1 and the right-hand circularly polarized signals; wb p,m The complex weight value is the complex weight value when the frequency band fc1 and the right-handed circularly polarized signal are subjected to DBF processing; SC (Single chip computer) m (t) M GNSS-S signals with frequency band fc2 and left-hand circular polarization; RC (resistor-capacitor) p (t) P high signal-to-noise ratio GNSS-S signals after DBF processing is carried out on the frequency band fc2 and the left-handed circularly polarized signals; wc c p,m The complex weight is the complex weight when DBF processing is carried out on the frequency band fc2 and the left-handed circularly polarized signal; SD m (t) M GNSS-S signals with frequency band fc2 and right-hand circular polarization; RD p (t) P high signal-to-noise ratio GNSS-S signals after DBF processing is carried out on the frequency band fc2 and the right-hand circularly polarized signals; wd p,m The complex weight value is the complex weight value when DBF processing is carried out on the frequency band fc2 and the right-hand circularly polarized signal; SE m (t) M GNSS-S signals with frequency band fc3 and left-hand circular polarization; RE p (t) P high signal-to-noise ratio GNSS-S signals after DBF processing is carried out on the frequency band fc3 and the left-handed circularly polarized signals; we p,m The complex weight is the complex weight when DBF processing is carried out on the frequency band fc3 and the left-handed circularly polarized signal; SF m (t) are frequency band fc3, right-hand circularly polarized M GNSS-S signals; RF (radio frequency) p (t) P high signal-to-noise ratio GNSS-S signals after DBF processing is carried out on the frequency band fc3 and the right-hand circularly polarized signals; wf p,m For frequency band fc3, right hand circularly polarized signalsComplex weights for row DBF processing. According to one aspect of the invention, bi-site SAR imaging includes:
a1, constructing a double-station SAR imaging geometric scene according to navigation satellite position and speed information output by a satellite navigation positioning system and position and speed information of a GNSS-S radar system, wherein the size of the imaging scene is X multiplied by Y, and the sizes of imaging grids are delta X multiplied by delta Y;
a2, performing parallelization time domain Back Projection (BP) double-station imaging processing on the GNSS-S signal by utilizing P parallelization BP imaging processing units to obtain 6N double-station SAR images;
the detection area is irradiated by N navigation satellite signals to form N double-station radars, each double-station radar comprises three frequency bands and two kinds of polarization information, and the parallelization BP imaging processing unit totally comprises 6N time domain BP double-station imaging processing sub-modules.
According to one aspect of the invention, the multidimensional SAR image fusion processing comprises the following steps:
b1, compensating the power path loss of the double-station SAR images at different observation angles;
and b2, performing weight superposition on the 6N double-station SAR images by using a non-coherent processing method to obtain a high signal-to-noise ratio SAR image.
According to an aspect of the present invention, the length direction extraction includes:
c11, carrying out slice extraction on the detected ship target, and removing a sea clutter background;
c12, calculating the length-width ratio of the ship target;
c13, selecting the direction with the largest length-width ratio as the long axis direction of the ship target;
the extraction of the ship bow direction comprises the following steps:
c21, calculating the ship target position in each frame of the fused two-station SAR image;
c22, correlating the P frame ship target position information to obtain the motion track and the motion direction of the ship target;
c23, obtaining the ship bow direction according to the long axis direction and the motion direction of the ship target, and calculating the azimuth clamp of the qth ship and the GNSS-S radar receiving antenna beam centerCorner
The multi-dimensional scattering property calculation includes:
c31, respectively carrying out azimuth multi-view processing on the 6N double-station SAR images before fusion, wherein the multi-view times K are 3-10;
c32, respectively calculating scattering coefficients of the ship target in the 6N multi-view processed double-station SAR images to obtain scattering coefficients sigma 1 of the ship target to N double-station angles, three frequency bands and two polarizations q,n 、σ2 q,n 、σ3 q,n 、σ4 q,n 、σ5 q,n 、σ6 q,n And respectively representing a frequency band fc1 and a left-hand circularly polarized scattering coefficient, a frequency band fc1 and a right-hand circularly polarized scattering coefficient, a frequency band fc2 and a left-hand circularly polarized scattering coefficient, a frequency band fc2 and a right-hand circularly polarized scattering coefficient, a frequency band fc3 and a left-hand circularly polarized scattering coefficient, and a frequency band fc3 and a right-hand circularly polarized scattering coefficient, which are formed by the irradiation of the nth navigation satellite on the qth ship target.
According to an aspect of the present invention, in step (d), a multidimensional scattering characteristic set of the ship target is constructed according to an observation angle, a working frequency band, a polarization mode, a position and a speed of a navigation satellite, a position and a speed of a satellite-borne GNSS-S radar, and a ship bow direction of the ship target, and then a multidimensional scattering characteristic set Ω q of a qth ship target is:
wherein, delta theta s q,n 、
Respectively the incident angle and the azimuth angle of the nth navigation satellite signal irradiated on the qth ship target, wherein the azimuth angle takes the ship bow direction of the ship target as referenceThen, Δ θ s q,n ＝θs n ，
Δθr q 、
Respectively receiving an incident angle and an azimuth angle of the qth ship target by the satellite-borne GNSS-S radar, wherein the azimuth angle is based on the ship bow direction of the ship target, and the delta theta r q ＝θr in ，
L lp 、L rp Left hand circular polarization and right hand circular polarization.
According to one aspect of the invention, in the step (d), the received triple-frequency point dual-polarization information of each navigation transmitting satellite and the corresponding scattering coefficient are standardized and then are jointly input into the target classification network;
the object classification network includes:
the multilayer convolution is used for sequentially performing convolution, activation and pooling on input, extracting characteristics including three-frequency point dual polarization and corresponding scattering coefficients, and sharing multilayer convolution parameters corresponding to a plurality of navigation transmitting satellites;
the multilayer perceptron is used for carrying out multilayer perception on the characteristic information obtained by multilayer convolution in a parameter sharing mode;
realizing the feature fusion of a plurality of launching satellites;
and the classifier is used for judging the class of the ship according to the fusion characteristics.
According to one aspect of the invention, the track height H of the satellite-borne GNSS-S radar is 200-800km, and a ship multi-dimensional scattered signal of a plurality of navigation satellite signals is received by adopting a three-frequency-band, dual-polarization and azimuth multi-channel array antenna;
the antenna receives multi-dimensional GNSS-S signals of a ship target in a front side view mode, the multi-dimensional GNSS-S signals consist of M receiving channels in the azimuth direction, the wave beam in the azimuth direction of the antenna of each receiving channel is a wide wave beam, and the wave beam angle theta is larger than the wave beam angle theta a Greater than 5 deg. and total synthetic aperture length of L S ；
Obtaining P sub-beams after digital beam forming processing is carried out on the M receiving channels, wherein each sub-beam corresponds to multiple frames of different position information of a ship target, and multiple frames of SAR images are obtained after double-station SAR imaging;
angle of incidence thetar at the center of the antenna beam in 25-55 degrees, and the azimuth angle of the center of the antenna beam is 0;
the detection area is simultaneously irradiated by N navigation satellite signals, N double-station radar systems are formed by the detection area and the satellite-borne GNSS-S radar, and the incident angle of the nth navigation satellite signal is theta S n In an azimuth of
According to the concept of the invention, a ship multidimensional scattering characteristic extraction and intelligent classification method based on a satellite-borne GNSS-S radar is provided. The satellite-borne GNSS-S radar can simultaneously receive ship target scattering signals of multiple navigation satellite signals and obtain multi-dimensional observation information of sea surface ship targets, such as multi-angle, multi-frequency band, multi-polarization and the like. Firstly, performing parallelization double-station SAR imaging on a multi-dimensional GNSS-S signal of a ship target to obtain a multi-dimensional SAR image of the ship target; secondly, carrying out non-coherent fusion processing on the multi-dimensional SAR image so as to improve the contour information of the ship target, realize the detection of the ship target and determine the length direction of the ship target; thirdly, judging the motion direction of the ship target by using the continuous multi-frame images, determining the ship bow direction of the ship target, calculating the multi-dimensional equivalent scattering coefficient of the ship target, and establishing a multi-dimensional scattering characteristic set of the ship target; and finally, intelligently classifying the ship target by using the convolutional neural network, inputting the SAR image slice and the multi-dimensional scattering characteristic set of the ship target, and outputting the SAR image slice and the multi-dimensional scattering characteristic set as the type of the ship target. Therefore, compared with the existing ship target classification method based on SAR images, the ship target classification method based on the SAR images fully utilizes multi-dimensional electromagnetic scattering information of the ship target, such as multi-angle, multi-frequency band, multi-polarization and the like, acquired by the satellite-borne GNSS-S radar, establishes a multi-dimensional scattering characteristic set of the ship target, realizes reliable and intelligent classification of the ship target elevation by utilizing the convolutional neural network model, has higher classification accuracy, can solve the problem of ship target classification under the condition of medium and high sea, does not need to actively transmit high-power signals, can realize detection and classification of the ship target by only receiving and processing ship target scattering signals of a plurality of navigation satellite signals, and has the advantages of low cost, low power consumption, light weight and the like.
According to one scheme of the invention, the observation time of a ship target on the sea surface is prolonged by utilizing a satellite-borne GNSS-S radar azimuth multi-channel technology, a multi-frame image of the ship target is obtained by adopting an azimuth digital beam forming and double-station SAR imaging method, the motion direction and the ship bow direction of the ship target are extracted, and a reference azimuth angle is provided for the construction of a multi-dimensional scattering characteristic set of the ship target.
Drawings
FIG. 1 is a flow chart of a process of a method for intelligently classifying ship targets according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating a multi-dimensional scattering information acquisition of a satellite-borne GNSS-S radar according to an embodiment of the present invention;
FIG. 3 is a block diagram schematically illustrating the configuration of an on-board GNSS-S radar system in accordance with an embodiment of the present invention;
FIG. 4 is a flow diagram schematically illustrating an azimuth multi-channel digital beamforming process according to an embodiment of the present invention;
FIG. 5 is a flow chart of a process for fusion of two-station SAR imaging and a multi-dimensional SAR image according to an embodiment of the present invention;
FIG. 6 is a flow chart of the bow direction extraction and multi-dimensional scattering property set construction of a ship target according to an embodiment of the present invention;
fig. 7 schematically shows a flowchart for implementing ship target classification using a convolutional neural network according to an embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can also be derived from them without inventive effort.
The present invention is described in detail below with reference to the drawings and the specific embodiments, which are not repeated herein, but the embodiments of the present invention are not limited to the following embodiments.
Referring to fig. 1, the ship target multidimensional scattering characteristic extraction and intelligent classification method based on the satellite-borne GNSS-S radar of the invention utilizes navigation satellite signals to realize detection and classification of ship targets under complex sea conditions, and is suitable for technical research of space-based distributed high-resolution wide-range SAR imaging systems.
The method comprises the steps of firstly utilizing the satellite-borne GNSS-S radar 10 to simultaneously receive ship target scattering signals of a plurality of navigation satellite signals, and accordingly obtaining multi-dimensional observation information of the ship target, such as multi-angle, multi-frequency band, multi-polarization and the like. Subsequently, a parallelization double-station SAR imaging 20 is carried out on the multi-dimensional GNSS-S signal of the ship target by adopting a BP imaging method, and a multi-dimensional SAR image of the sea surface ship target is obtained. Then, multi-dimensional SAR image fusion processing 30 is carried out, namely, non-coherent fusion processing is carried out on the multi-dimensional SAR image of the ship target so as to obtain a high signal-to-noise ratio SAR image, and therefore the contour information of the ship target is enhanced. And then carrying out ship target detection 40 on the SAR image after fusion processing to obtain the position information of the ship target. Then, carrying out length direction extraction 50 and warship bow direction extraction 60 on the ship target, namely, roughly estimating the length and width of the ship target to obtain the length direction of the ship target; and extracting the ship bow direction of the ship target to obtain a scattering characteristic calculation reference azimuth of the ship target. And the electromagnetic scattering property calculation unit 70 is utilized to calculate the scattering coefficients of the ship target at different angles, different frequency bands and different polarizations, so as to obtain the multidimensional scattering coefficient of the ship target. And then, constructing 80 a multi-dimensional electromagnetic scattering set, namely constructing a vectorized multi-dimensional electromagnetic scattering set of the ship target according to the position and the speed of the navigation satellite, the position and the speed of the satellite-borne GNSS-S radar, the ship bow direction of the ship target and the like. And finally, intelligently classifying the type of the ship target by using a target classification network, wherein the target classification network is a convolutional neural network model 90, the input (layer) of the target classification network is a vectorized multi-dimensional electromagnetic scattering set of the ship target, and the output (layer) of the target classification network is the type of the ship target.
Referring to FIG. 2, the orbit height of the satellite-borne GNSS-S radar 10 is H, which is 200-800km, and a triple-band (fc 1, fc2, fc 3) and dual-polarization (L-circular polarization left-handed) lp And right hand circular polarization L rp ) And the azimuth multi-channel array antenna receives multi-dimensional scattering signals of the ships of the navigation satellite signals at the same time. The antenna receives multi-dimensional GNSS-S signals in a front side view mode, and consists of M receiving channels in the azimuth direction, the antenna azimuth beam of each receiving channel is a wide beam, and the beam angle theta a Is more than 5 degrees, increases the observation time of the ship target, and has the total synthetic aperture length of L S . After digital beam forming processing is carried out on the M receiving channels, P sub-beams are obtained, each sub-beam corresponds to multiple frames of different position information of a ship target, and multiple frames of SAR images are obtained after double-station SAR imaging. Recording the incident angle of the center of the antenna beam as thetar in Taking 25-55 degrees, the azimuth angle of the antenna beam center is 0, and the azimuth included angle between the qth ship target and the antenna beam center is
The detection area is simultaneously irradiated by N navigation satellite signals, N double-station radar systems are formed by the detection area and the satellite-borne GNSS-S radar 10, and the incident angle of the nth navigation satellite signal is recorded as theta S n In an azimuth of
Δθs q,n 、
Respectively the incident angle and the azimuth angle of the nth navigation satellite signal irradiated on the qth ship target, wherein the azimuth angle is in reference to the ship bow direction of the ship target, and the delta theta s q,n ＝θs n ，
Δθr q 、
The incident angle and azimuth angle of the qth ship target received by the satellite-borne GNSS-S radar 10 are respectively delta theta r q ＝θr in ，
Referring to fig. 3, the GNSS-S radar 10 on board includes: the azimuth multi-channel antenna 101 is used for receiving multi-dimensional GNSS-S signals of a detection area, the antenna has three frequency band signal receiving capacity, the frequencies of the three frequency bands are fc1, fc2 and fc3 respectively, fc1 is 1.191GHz, fc2 is 1.268GHz, fc3 is 1.575GHz, the corresponding working bandwidths are Bw1, bw2 and Bw3 respectively, bw1 and Bw2 are 2.046MHz, and Bw3 is 20.46MHz; the antenna has left-hand circular polarization and right-hand circular polarization signal receiving capacity, and the polarization isolation degree is greater than 20dB; the whole antenna is divided into M receiving antennas along the azimuth direction, wherein M is 4-8, each receiving antenna consists of Na multiplied by Nr antenna units, and Na is 6-12, so that each receiving antenna has a wide beam angle in the azimuth direction, the observation time of a ship target is increased, and multi-frame sub-aperture imaging processing is facilitated; nr takes 12-24, so that each receiving antenna has the characteristics of narrow beam angle, high gain and the like in the distance direction; after digital beam forming is carried out on signals of M receiving antennas, a plurality of high-gain narrow beams are formed, and the gain of each narrow beam is larger than 30dB; the M six-channel receivers 102 are configured to perform low-noise amplification, band-pass filtering, down-conversion, intermediate-frequency low-noise amplification, intermediate-frequency ADC sampling, digital domain IQ demodulation, and the like on GNSS-S signals of three frequency bands and two polarizations output by each receiving channel, so as to obtain six baseband GNSS-S complex signals; the 6 digital beam forming processing units 103 are configured to perform digital beam forming on GNSS-S signals of three frequency bands and two polarizations, and form P digital sub-beams in an azimuth direction; and the P matched filter banks 104 are configured to perform matched filtering on the GNSS-S signals output by each digital sub-beam by using signals emitted by N navigation satellites as reference signals to obtain N independent GNSS-S signals with high signal-to-noise ratio, and output P frame signal sets, where each frame signal set includes 6N GNSS-S signals with high signal-to-noise ratio.
Referring to fig. 4, the digital beam forming processing unit 103 includes: a first P sets of complex multiply-add units 1031 for left-handed circularly polarized M GNSS-S signals SA in frequency band fc1 m (t) performing DBF processing along the azimuth direction to form P digital sub-beams and output P high signal-to-noise ratio GNSS-S signals, that is,
a second P complex multiply-add unit 1032 for performing right-hand circular polarization M GNSS-S signals SB at frequency band fc1 m (t) performing DBF processing along the azimuth direction to form P digital sub-beams and output P GNSS-S signals with high signal-to-noise ratio, namely,
a third P sets of complex multiply-add units 1033 for left-handed circularly polarized M GNSS-S signals SC for the frequency band fc2 m (t) performing DBF processing along the azimuth direction to form P digital sub-beams and output P GNSS-S signals with high signal-to-noise ratio, namely,
a fourth P complex multiply-add unit 1034 for generating M GNSS-S signals SD of band fc2 and right-hand circular polarization m (t) performing DBF processing along the azimuth direction to form P digital sub-beams and output P GNSS-S signals with high signal-to-noise ratio, namely,
a fifth P sets of complex multiply-add units 1035 for left-handed circularly polarized M GNSS-S signals SE of frequency band fc3 m (t) performing DBF processing along the azimuth direction to form P digital sub-beams and output P GNSS-S signals with high signal-to-noise ratio, namely,
a sixth P sets of complex multiply-add units 1036 for right-hand and left-hand circularly polarized M GNSS-S signals SF at the frequency band fc3 m (t) performing DBF processing along the azimuth direction to form P digital sub-beams and output P GNSS-S signals with high signal-to-noise ratio, namely,
a DBF weight calculation unit 1037 for calculating 6 sets of complex weights wa required for DBF processing p,m 、wb p,m 、wc p,m 、wd p,m 、we p,m 、wf p,m And (6) performing calculation. Wherein t is a fast time variable; m is a serial number, and the value of M =1, 2., M; p is a serial number, and the value is P =1, 2., P; SA m (t) are frequency band fc1, left-handed circularly polarized M GNSS-S signals; RA p (t) P high signal-to-noise ratio GNSS-S signals after DBF processing is carried out on the frequency band fc1 and the left-handed circularly polarized signals; wa (a) p,m The complex weight is the complex weight when DBF processing is carried out on the frequency band fc1 and the left-handed circularly polarized signal; SB (bus bar) m (t) are frequency band fc1, right-hand circularly polarized M GNSS-S signals; RB (radio B) p (t) P high signal-to-noise ratio GNSS-S signals after DBF processing is carried out on the frequency band fc1 and the right-hand circularly polarized signals; wb p,m The complex weight value is the complex weight value when the frequency band fc1 and the right-handed circularly polarized signal are subjected to DBF processing; SC (Single chip computer) m (t) are frequency band fc2, left-handed circularly polarized M GNSS-S signals; RC (resistor-capacitor) capacitor p (t) P high signal-to-noise ratio GNSS-S signals after DBF processing is carried out on the frequency band fc2 and the left-handed circularly polarized signals; wc c p,m The complex weight is the complex weight when DBF processing is carried out on the frequency band fc2 and the left-handed circularly polarized signal; SD m (t) M GNSS-S signals with frequency band fc2 and right-hand circular polarization; RD p (t) P high signal-to-noise ratio GNSS-S signals after DBF processing is carried out on the frequency band fc2 and the right-hand circularly polarized signals; wd p,m The complex weight value is the complex weight value when the frequency band fc2 and the right-hand circularly polarized signal are subjected to DBF processing; SE m (t) are frequency band fc3, left-handed circularly polarized M GNSS-S signals; RE p (t) P high signal-to-noise ratio GNSS-S signals after DBF processing is carried out on the frequency band fc3 and the left-handed circularly polarized signals; we p,m The complex weight is the complex weight when DBF processing is carried out on the frequency band fc3 and the left-handed circularly polarized signal; SF m (t) are frequency band fc3, right-hand circularly polarized M GNSS-S signals; RF (radio frequency) p (t) P high signal-to-noise ratio GNSS-S signals after DBF processing is carried out on the frequency band fc3 and the right-hand circularly polarized signals; wf p,m The complex weight is the complex weight when DBF processing is performed on the frequency band fc3 and the right-hand circularly polarized signal.
Referring to fig. 5, the two-station SAR imaging 20 comprises: constructing 201 a double-station SAR imaging geometric scene, namely constructing a double-station SAR imaging geometric coordinate system and imaging scene size on a detection area, specifically constructing the double-station SAR imaging geometric scene according to the position and speed information of a navigation satellite output by a satellite navigation positioning system and the position and speed information of a GNSS-S radar system, wherein the imaging scene size is X multiplied by Y, and the imaging grid sizes are delta X multiplied by delta Y; performing parallelization time domain BP double-station SAR imaging processing on three frequency bands and two polarization GNSS-S signals of N double-station radars by using P parallelization BP imaging processing units 202 to obtain 6N double-station SAR images; the detection area (imaging scene) is irradiated by N navigation satellite signals to form N double-station radars, each double-station radar comprises three frequency bands and two kinds of polarization information, and the parallelization BP imaging processing unit 202 comprises 6N time domain BP double-station imaging processing sub-modules in total.
The multi-dimensional SAR image fusion process 30 includes: image power compensation 301, namely, the path loss of the power of the double-station SAR image at different observation angles is compensated, so as to reduce the power error of the ship target caused by the attenuation of different navigation satellite routes; and (2) performing non-coherent fusion processing 302, namely performing fusion processing on the 6N double-station SAR images to obtain a high signal-to-noise-ratio double-station SAR image, specifically performing weight superposition on the 6N double-station SAR images by using a non-coherent processing method to improve the timeliness of the fusion processing, improve the contour information of a ship target, realize ship target detection, and further facilitate the extraction of the length direction of the ship target.
Referring to fig. 6, the length direction extraction 50 includes: ship slice extraction 501, namely, ship target detection is performed on the high signal-to-noise ratio bistatic SAR image (i.e., the fused bistatic SAR image), the ship target position is determined, and the detected ship target is sliced and extracted to remove the sea clutter background; ship aspect ratio calculation 502, i.e., calculating the aspect ratio of the ship target, as for civil ships, its aspect ratio is 4-7; and (3) calculating the length direction 503, namely determining the long axis direction of the ship target, and specifically selecting the direction with the largest length-width ratio as the long axis direction of the ship target.
The warship bow direction extraction 60 is to determine the motion direction of the ship target by using continuous multi-frame sub-aperture images, and certainly, beam synthesis needs to be performed on M receiving antennas of the GNSS-S radar by using a digital beam forming method, P sub-beams are formed in the azimuth direction, and the GNSS-S signal of each sub-beam is subjected to the above processing to obtain P-frame SAR images of the ship target, so as to obtain P positions of the ship target. Specifically, the method comprises the following steps: extracting 601 a ship target position, namely calculating the ship target position in each frame of the fused two-station SAR image; extracting 602 a ship target motion track, namely, correlating the position information of the P frame ship target to obtain a motion track and a motion direction of the ship target; calculating the ship bow direction 603, namely determining the ship bow direction of the ship target, specifically obtaining the ship bow direction according to the long axis direction and the motion direction of the ship target, and calculating the azimuth included angle between the qth ship and the GNSS-S radar receiving antenna beam center
The multidimensional scattering characteristic calculation by the electromagnetic scattering characteristic calculation unit 70 includes: azimuth multi-view processing 701, namely, azimuth multi-view processing is respectively carried out on the 6N double-station SAR images before fusion, speckle noise is reduced, and the multi-view times K are 3-10; calculating the ship scattering coefficient 702, namely calculating the scattering coefficient of the ship target in the 6N multi-view processed double-station SAR images respectively, specifically calculating the ship target radar sectional area of the 6N double-station SAR images by using a radar equation, and calculating the ship target non-existence state according to the imaging resolution of the double-station SAR imagesObtaining the electromagnetic scattering coefficient sigma 1 of the ship target on multidimensional electromagnetic signals with N double station angles, three frequency bands, two polarizations and the like under the conditions of the same observation angle, different frequency bands and different polarizations q,n 、σ2 q,n 、σ3 q,n 、σ4 q,n 、σ5 q,n 、σ6 q,n And respectively representing a frequency band fc1 and a left-hand circularly polarized scattering coefficient, a frequency band fc1 and a right-hand circularly polarized scattering coefficient, a frequency band fc2 and a left-hand circularly polarized scattering coefficient, a frequency band fc2 and a right-hand circularly polarized scattering coefficient, a frequency band fc3 and a left-hand circularly polarized scattering coefficient, and a frequency band fc3 and a right-hand circularly polarized scattering coefficient, which are formed by the irradiation of the nth navigation satellite on the qth ship target.
In the multidimensional electromagnetic scattering set construction 80, a multidimensional scattering characteristic set of a ship target is constructed according to information such as an observation angle, a working frequency band, a polarization mode, a position and a speed of a navigation satellite, a position and a speed of the satellite-borne GNSS-S radar 10, a ship bow direction of the ship target and the like of 6N double-station SAR images, and then the multidimensional scattering characteristic set of a qth ship target is as follows:
wherein, delta theta s q,n 、
Respectively determining the incident angle and the azimuth angle of the nth navigation satellite signal irradiated on the qth ship target; delta theta r q 、
Receiving an incident angle and an azimuth angle of a qth ship target by the satellite-borne GNSS-S radar 10 respectively, wherein the azimuth angle is based on the ship bow direction of the ship target as reference; l is lp 、L rp Left hand circular polarization and right hand circular polarization.
Referring to fig. 7, received triple-frequency point dual-polarization information of each navigation transmitting satellite and a corresponding scattering coefficient are normalized and then input into (a multilayer convolution of) a ship target classification network together. Wherein the target classification network comprises: the multilayer convolution 901 is used for sequentially performing convolution, activation and pooling on input, extracting characteristics including three-frequency point dual polarization and corresponding scattering coefficients, sharing parameters of the multilayer convolution 901 corresponding to a plurality of navigation transmitting satellites and realizing a network structure with variable transmitting satellite number; the multilayer perceptron 902 is used for performing multilayer perception on the characteristic information obtained by the multilayer convolution 901 in a parameter sharing mode so as to further reduce the dimension of high-dimensional information, and the parameter sharing mode can realize a perception structure with variable transmitting satellite number; feature matching fusion 903, namely, realizing feature fusion of a plurality of transmitting satellites so as to comprehensively utilize three-frequency point and dual-polarization scattering features obtained by observing a ship target from a plurality of angles; the classifiers 904 of a plurality of ship classes are used for judging the classes of ships according to the fusion characteristics, and the process is mainly realized by multilayer perception.
In conclusion, the satellite-borne GNSS-S radar acquires and utilizes multi-dimensional information of a ship target, such as multi-angle, multi-frequency band, multi-polarization and the like, establishes a multi-dimensional scattering characteristic set of the ship target, and utilizes a convolutional neural network to realize highly reliable intelligent classification of the ship target. Compared with the existing image-based ship classification method, the ship target multi-dimensional electromagnetic scattering information acquired by the satellite-borne GNSS-S radar can be fully utilized to construct a multi-dimensional scattering characteristic set of the ship target, the method can have better classification accuracy by means of the convolutional neural network model, the ship target detection and classification can be realized only by receiving and processing ship target scattering signals of a plurality of navigation satellite signals without actively transmitting high-power signals, and the method has the advantages of low cost, low power consumption, light weight and the like.
The above description is only one embodiment of the present invention, and is not intended to limit the present invention, and it is apparent to those skilled in the art that various modifications and variations can be made in 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 (8)
1. A method for extracting and classifying multidimensional scattering characteristics of a satellite-borne GNSS-S radar ship comprises the following steps:
a. receiving ship target scattering signals of a plurality of navigation satellite signals by using a satellite-borne GNSS-S radar (10), and performing double-station SAR imaging on the signals to obtain a multi-dimensional SAR image of the ship target;
b. carrying out non-coherent fusion processing on the multi-dimensional SAR image, and carrying out ship target detection on the image subjected to fusion processing to obtain position information of a ship target;
c. extracting the length direction and the bow direction of the ship target, and calculating the multidimensional scattering coefficient of the ship target;
d. constructing a vectorized multi-dimensional electromagnetic scattering set, and classifying the types of the ship targets by using a target classification network;
in the step (a), receiving a ship target scattering signal to obtain multidimensional observation information, including multi-angle, multi-frequency band and multi-polarization of the ship target;
in the step (c), estimating the length and width of the ship target to obtain the length direction of the ship target;
calculating scattering coefficients of the ship target at different angles, different frequency bands and different polarizations by using an electromagnetic scattering characteristic calculation unit (70) to obtain a multidimensional scattering coefficient of the ship target;
in the step (d), the target classification network is a convolutional neural network model, the vectorized multidimensional electromagnetic scattering set is input as a ship target, and the output is the type of the ship target;
in the step (d), a multi-dimensional scattering characteristic set of the ship target is constructed according to the observation angle, the working frequency band, the polarization mode, the position and the speed of the navigation satellite, the position and the speed of the satellite-borne GNSS-S radar (10) and the ship bow direction of the ship target, and then the multi-dimensional scattering characteristic set omega of the qth ship target q Comprises the following steps:
wherein, delta theta s q,n 、
Respectively the incident angle and the azimuth angle of the nth navigation satellite signal irradiated on the qth ship target, wherein the azimuth angle is based on the ship bow direction of the ship target, and the delta theta s is q,n ＝θs n ，
Δθr q 、
Respectively receiving the incident angle and the azimuth angle of the qth ship target by the satellite-borne GNSS-S radar (10), wherein the azimuth angle is based on the ship bow direction of the ship target, and the delta theta r is q ＝θr in ，
L lp 、L rp Left-hand circular polarization and right-hand circular polarization respectively; sigma 1 q,n 、σ2 q,n 、σ3 q,n 、σ4 q,n 、σ5 q,n 、σ6 q,n Respectively representing a frequency band fc1 and a left-hand circularly polarized scattering coefficient, a frequency band fc1 and a right-hand circularly polarized scattering coefficient, a frequency band fc2 and a left-hand circularly polarized scattering coefficient, a frequency band fc2 and a right-hand circularly polarized scattering coefficient, a frequency band fc3 and a left-hand circularly polarized scattering coefficient, and a frequency band fc3 and a right-hand circularly polarized scattering coefficient, which are formed by the irradiation of the nth navigation satellite on the qth ship target; the nth navigation satellite signal incidence angle is thetas n In an azimuth of
An azimuth included angle between the qth ship and the center of a GNSS-S radar receiving antenna beam is formed; theta r in The angle of incidence at the center of the antenna beam.
2. The method according to claim 1, characterized in that the on-board GNSS-S radar (10) comprises:
the multi-channel antenna comprises an azimuth multi-channel antenna (101) and a frequency band selection unit, wherein the azimuth multi-channel antenna is used for receiving multi-dimensional GNSS-S signals in a detection area, the antenna has three frequency band signal receiving capacity, the frequencies of the three frequency bands are fc1, fc2 and fc3 respectively, and the corresponding working bandwidths are Bw1, bw2 and Bw3 respectively; the antenna has the capability of receiving left-hand circularly polarized signals and right-hand circularly polarized signals, and the polarization isolation degree is greater than 20dB; the whole antenna is divided into M receiving antennas along the azimuth direction, wherein M is 4-8, each receiving antenna consists of Na multiplied by Nr antenna units, na is 6-12, and Nr is 12-24;
the M six-channel receivers (102) are used for performing low-noise amplification, band-pass filtering, down-conversion, intermediate-frequency low-noise amplification, intermediate-frequency ADC (analog-to-digital converter) sampling and digital domain IQ (in-phase quadrature) demodulation on the GNSS-S signals of three frequency bands and two polarizations output by each receiving channel to obtain six-baseband GNSS-S complex signals;
the digital beam forming processing unit (103) is used for carrying out digital beam forming on GNSS-S signals of three frequency bands and two polarizations and forming P digital sub-beams in the azimuth direction;
the P matched filter banks (104) are used for performing matched filtering on the GNSS-S signals output by each digital sub-beam by using signals emitted by N navigation satellites as reference signals to obtain N independent GNSS-S signals with high signal-to-noise ratio and outputting P frame signal sets, and each frame signal set comprises 6N GNSS-S signals with high signal-to-noise ratio;
after digital beam forming is carried out on the signals of the M receiving antennas, a plurality of high-gain narrow beams are formed, and the gain of each narrow beam is larger than 30dB.
3. The method according to claim 2, characterized in that the digital beam forming processing unit (103) comprises:
a first P complex multiply-add unit (1031) for left-hand circularly polarized M GNSS-S signals SA in frequency band fc1 m (t) performing DBF processing along the azimuth direction to form P digital sub-beams, and outputting P GNSS-S signals with high signal-to-noise ratio as follows:
a second P complex multiply-add units (1032) for right-hand circularly polarized M GNSS-S signals SB of frequency band fc1 m (t) performing DBF processing along the azimuth direction to form P digital sub-beams, and outputting P GNSS-S signals with high signal-to-noise ratio as follows:
a third P sets of complex multiply-add units (1033) for left-handed circularly polarized M GNSS-S signals SC in the frequency band fc2 m (t) performing DBF processing along the azimuth direction to form P digital sub-beams, and outputting P GNSS-S signals with high signal-to-noise ratio as follows:
a fourth P complex multiply-add unit (1034) for performing right-hand and left-hand circularly polarized M GNSS-S signals SD in the frequency band fc2 m (t) DBF processing is carried out along the azimuth direction to form P digital sub-beams, and P high signal-to-noise ratio GNSS-S signals are output as follows:
a fifth P sets of complex multiply-add units (1035) for left-hand circularly polarized M GNSS-S signals SE at frequency band fc3 m (t) at DBF in azimuthForming P digital sub-beams, and outputting P high signal-to-noise ratio GNSS-S signals as follows:
a sixth P sets of complex multiply add units (1036) for right-hand and left-hand circularly polarized M GNSS-S signals SF at frequency band fc3 m (t) performing DBF processing along the azimuth direction to form P digital sub-beams, and outputting P GNSS-S signals with high signal-to-noise ratio as follows:
a DBF weight calculation unit (1037) for processing 6 sets of complex weights wa required for DBF p,m 、wb p,m 、wc p,m 、wd p,m 、we p,m 、wf p,m Calculating;
wherein t is a fast time variable; m is a serial number, and the value is M =1, 2.., M; p is a serial number, and the value is P =1, 2.., P; SA m (t) are frequency band fc1, left-handed circularly polarized M GNSS-S signals; RA p (t) P high signal-to-noise ratio GNSS-S signals after DBF processing is carried out on the frequency band fc1 and the left-handed circularly polarized signals; wa (a) p,m The complex weight value is the complex weight value when DBF processing is carried out on the frequency band fc1 and the left-handed circularly polarized signal; SB (bus bar) m (t) are frequency band fc1, right-hand circularly polarized M GNSS-S signals; RB (radio B) p (t) P high signal-to-noise ratio GNSS-S signals after DBF processing is carried out on the frequency band fc1 and the right-hand circularly polarized signals; wb p,m The complex weight value is the complex weight value when DBF processing is carried out on the frequency band fc1 and the right-hand circularly polarized signal; SC (Single chip computer) m (t) are frequency band fc2, left-handed circularly polarized M GNSS-S signals; RC (resistor-capacitor) p (t) P high signal-to-noise ratio GNSS-S signals after DBF processing is carried out on the frequency band fc2 and the left-handed circularly polarized signals; wc c p,m The complex weight value is the complex weight value when DBF processing is carried out on the frequency band fc2 and the left-handed circularly polarized signal; SD m (t) M GNSS-S signals with frequency band fc2 and right-hand circular polarization; RD p (t) DBF processing of the right-hand circularly polarized signal of frequency band fc2P processed GNSS-S signals with high signal-to-noise ratio; wd p,m The complex weight value is the complex weight value when DBF processing is carried out on the frequency band fc2 and the right-hand circularly polarized signal; SE m (t) are frequency band fc3, left-handed circularly polarized M GNSS-S signals; RE p (t) P high signal-to-noise ratio GNSS-S signals after DBF processing is carried out on the frequency band fc3 and the left-handed circularly polarized signals; we p,m The complex weight value is the complex weight value when DBF processing is carried out on the frequency band fc3 and the left-handed circularly polarized signal; SF m (t) are frequency band fc3, right-hand circularly polarized M GNSS-S signals; RF (radio frequency) p (t) P high signal-to-noise ratio GNSS-S signals after DBF processing is carried out on the frequency band fc3 and the right-hand circularly polarized signals; wf p,m The complex weight is the complex weight when DBF processing is performed on the frequency band fc3 and the right-hand circularly polarized signal.
4. The method of claim 1, wherein dual-station SAR imaging comprises:
a1, constructing a double-station SAR imaging geometric scene according to navigation satellite position and speed information output by a satellite navigation positioning system and position and speed information of a GNSS-S radar system, wherein the size of the imaging scene is X multiplied by Y, and the sizes of imaging grids are delta X multiplied by delta Y;
a2, performing parallelization time domain BP double-station imaging processing on the GNSS-S signal by utilizing P parallelization BP imaging processing units (202) to obtain 6N double-station SAR images;
the detection area is irradiated by N navigation satellite signals to form N double-station radars, each double-station radar comprises three frequency bands and two kinds of polarization information, and the parallelization BP imaging processing unit (202) comprises 6N time domain BP double-station imaging processing sub-modules in total.
5. The method of claim 1, wherein the multi-dimensional SAR image fusion process comprises:
b1, compensating the path loss of the power of the double-station SAR image at different observation angles;
and b2, performing weight superposition on the 6N double-station SAR images by using a non-coherent processing method to obtain a high signal-to-noise ratio SAR image.
6. The method of claim 1, wherein the lengthwise extraction comprises:
c11, carrying out slice extraction on the detected ship target, and removing a sea clutter background;
c12, calculating the length-width ratio of the ship target;
c13, selecting the direction with the largest length-width ratio as the long axis direction of the ship target;
the extraction of the ship bow direction comprises the following steps:
c21, calculating the ship target position in each frame of the fused bistatic SAR image;
c22, correlating the P frame ship target position information to obtain the motion track and the motion direction of the ship target;
c23, obtaining the ship bow direction according to the long axis direction and the motion direction of the ship target, and calculating the azimuth included angle between the qth ship and the GNSS-S radar receiving antenna beam center
The multi-dimensional scattering property calculation includes:
c31, respectively carrying out azimuth multi-view processing on the 6N double-station SAR images before fusion, wherein the multi-view times K are 3-10;
c32, respectively calculating scattering coefficients of the ship target in the 6N multi-view processed double-station SAR images to obtain scattering coefficients sigma 1 of the ship target for N double-station angles, three frequency bands and two polarizations q,n 、σ2 q,n 、σ3 q,n 、σ4 q,n 、σ5 q,n 、σ6 q,n The method and the device respectively represent a frequency band fc1 and a left-hand circular polarization scattering coefficient, a frequency band fc1 and a right-hand circular polarization scattering coefficient, a frequency band fc2 and a left-hand circular polarization scattering coefficient, a frequency band fc2 and a right-hand circular polarization scattering coefficient, a frequency band fc3 and a left-hand circular polarization scattering coefficient, and a frequency band fc3 and a right-hand circular polarization scattering coefficient which are formed by irradiation of an nth navigation satellite on a qth ship target.
7. The method according to claim 1, wherein in step (d), the received triple-frequency dual-polarization information and corresponding scattering coefficients of each navigational transmitting satellite are normalized and then input into the target classification network together;
the object classification network includes:
the multilayer convolution (901) is used for sequentially performing convolution, activation and pooling on input, extracting the characteristics including three-frequency point dual polarization and corresponding scattering coefficients, and sharing the parameters of the multilayer convolution (901) corresponding to the plurality of navigation transmitting satellites;
the multilayer perceptron (902) is used for carrying out multilayer perception on the characteristic information obtained by the multilayer convolution (901) in a parameter sharing mode;
realizing the feature fusion of a plurality of launching satellites;
and the classifier (904) is used for judging the class of the ship according to the fusion characteristics.
8. The method according to claim 1, wherein the orbit height H of the satellite-borne GNSS-S radar (10) is 200-800km, and a ship multidimensional scattering signal of a plurality of navigation satellite signals is received simultaneously by using a triple-band, dual-polarized, azimuth-oriented multi-channel array antenna;
the antenna receives multi-dimensional GNSS-S signals of a ship target in a front side view mode, the multi-dimensional GNSS-S signals consist of M receiving channels in the azimuth direction, the wave beam in the azimuth direction of the antenna of each receiving channel is a wide wave beam, and the wave beam angle theta is larger than the wave beam angle theta a Greater than 5 DEG, the total synthetic aperture length being L S ；
Obtaining P sub-beams after digital beam forming processing is carried out on M receiving channels, wherein each sub-beam corresponds to multi-frame different position information of a ship target, and multi-frame SAR images are obtained after double-station SAR imaging;
angle of incidence thetar at the center of the antenna beam in 25-55 degrees, and the azimuth angle of the center of the antenna beam is 0;
the detection area is simultaneously irradiated by N navigation satellite signals, N double-station radar systems are formed by the detection area and the satellite-borne GNSS-S radar (10), and the incident angle of the nth navigation satellite signal is theta S n In an azimuth of
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