CN112162281A - Multi-channel SAR-GMTI image domain two-step processing method - Google Patents
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Abstract
The invention discloses a two-step processing method of a multi-channel SAR-GMTI image domain, which comprises the steps of taking a reference channel as a benchmark, and carrying out time delay compensation and channel equalization processing on an echo signal after distance pulse pressure; sequentially carrying out azimuth spectrum compression and deskew processing on the registered signals, and transferring data to a two-dimensional image domain; performing combined clutter suppression processing on each channel image outside the reference channel and the reference channel image in the two-dimensional image domain to obtain a clutter suppression result; performing constant false alarm detection on each clutter suppression result to obtain a moving target detection result; returning an image domain before clutter suppression according to a moving target detection result, extracting joint pixel data of each moving target one by one to perform local joint optimal processing for spectrum estimation, and estimating the radial speed of the moving target; and marking each moving target in the reference channel image, and repositioning the moving target. The invention can realize high-probability detection and high-precision speed measurement positioning of the ground moving target while imaging a static scene.
Description
Technical Field
The invention belongs to the technical field of radar signal processing, and particularly relates to a two-step processing method for a multi-channel SAR-GMTI image domain.
Background
Synthetic Aperture Radar (SAR for short) transmits a broadband signal through a distance direction, an azimuth platform and a scene relatively move to form a long Synthetic Aperture, a two-dimensional high-resolution image of a Ground static scene can be obtained, and Ground Moving Target Indication (GMTI for short) can realize effective detection and display of a Ground Moving Target by inhibiting Ground clutter which is far stronger than the Target. In combination of the two technologies, the synthetic aperture radar ground moving target display technology (SAR-GMTI) can detect and measure the speed of a moving target while acquiring a high-resolution image of a ground static scene, and position the moving target at a corresponding position in the image. Therefore, the SAR-GMTI can simultaneously monitor a static scene and a moving target on the ground, has high practical application value, and is widely applied to many fields such as battlefield reconnaissance, traffic monitoring, ocean observation and the like.
In practical applications, SAR-GMTI is mainly focused on solving two problems: firstly, although the backscattering coefficient of a moving target is generally stronger than that of a static clutter, under the condition of low resolution, the moving target is generally represented as a point in a resolution unit due to small size, so that the moving target is submerged in numerous clutter scattering points, and a reliable clutter suppression algorithm is required to obtain a high enough signal-to-noise-and-noise ratio, so that the effective detection of the moving target is realized; secondly, the radial velocity of the moving target causes additional doppler shift, which causes the moving target to deviate from its actual position in the image azimuth direction, so that the radial velocity of the moving target needs to be estimated with high precision, and the azimuth shift is compensated, so that the moving target can be accurately repositioned to its actual position. Aiming at the problems of clutter suppression and speed measurement and positioning of SAR-GMTI, two types of methods are mainly used at present: the first method adopts optimal processing to carry out clutter suppression, and completes measurement of the speed of the moving target through searching, typical representatives of such methods mainly include a Doppler Post-processing Space-Time Adaptive algorithm (PD-STAP) that applies a Space-Time Adaptive technique (STAP) to a two-dimensional data domain of SAR, an imaging domain Space-Time Adaptive algorithm (isr) that processes in a range image domain and a two-dimensional image domain based on a Generalized Likelihood Ratio Test statistic (GLRT), and an Extended tap Phase Center offset algorithm (EDPCA), the method can obtain higher signal-to-noise-and-noise ratio after clutter suppression, thereby obtaining good detection and speed measurement performance; the second type of method uses non-optimal Antenna Phase Center offset (DPCA) and Along-Track interference (ATI) to perform clutter suppression and speed measurement, and typical examples of such methods include Adjacent Channel destructive interference (AC-ATI) that uses Adjacent channels to perform image subtraction and interference, Projection interference (SP-ATI) that uses orthogonal complementary space Projection and interference of clutter, and Projection Periodogram (SP-NP) that uses orthogonal complementary space Projection and Notch Periodogram of clutter, which have low computation.
However, in practical applications, the first method has two main problems: firstly, under the condition of unknown moving target position, the optimal method needs to calculate a global covariance matrix and search a peak point of a signal-to-noise-ratio pixel by pixel, which causes great operation burden on SAR-GMTI data with large two-dimensional data quantity; secondly, when channel errors, registration errors, moving target defocusing and other phenomena exist, moving target signals leak to neighborhood pixels, large deviation can be generated when a covariance matrix of clutter and noise data is estimated, and therefore the clutter suppression effect is reduced, and detection probability and speed measurement accuracy are affected. The second method is non-optimal, the gain of the clutter suppressed moving target energy is related to the radial velocity of the clutter suppressed moving target energy, and the clutter suppressed moving target energy has large loss under certain velocity, so that ideal detection probability and velocity measurement precision cannot be obtained.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a multi-channel SAR-GMTI image domain two-step processing method.
One embodiment of the invention provides a multi-channel SAR-GMTI image domain two-step processing method, which comprises the following steps:
step 3, taking the reference channel as a benchmark, and aligning the phase history domain signal Bm(fr,ta) Carrying out time delay compensation and channel equalization processing to obtain a registered signal Cm(fr,ta)；
Step 7, sequentially carrying out clutter suppression on the two-dimensional image domain data F with M being 2-M channelsm(tr,fa) Performing constant false alarm rate detection, and performing first-step processing on a union set of detection results to obtain K detection results of moving targets, wherein K is an integer greater than 0;
step 9, for the local joint pixel data Gm,k(tr,fa) Performing local joint space-time optimization processing, and searching out the estimated value of the radial velocity of the kth moving target
In one embodiment of the present invention, the echo signal A in the step 1m(tr,ta) Expressed as:
wherein, wr(. is a function of the distance window, wa(. is a function of the azimuth window, trIs a distance, fast time, taFor azimuth slow time, t ═ tr+taIs full time, fcIs the carrier frequency, gamma is the frequency modulation rate, c is the speed of light, j is the imaginary unit, Rm(ta) Is the slope course of the target, the slope course R of the targetm(ta) Expressed as:
wherein, XnIs the target azimuth position, RBIs the closest slope distance of the target to the radar, vrTo move the target radial velocity, vaAs the speed of the carrier, dmIs the equivalent phase center spacing of the mth channel from the reference channel.
In one embodiment of the present invention, the phase history domain signal B in step 2m(fr,ta) Expressed as:
wherein, wr(. is a function of the distance window, wa(. is a function of the azimuth window, frIs the distance frequency, taFor azimuth slow time, fcIs the carrier frequency, c is the speed of light, j represents the unit of imaginary number, XnIs the target azimuth position, RBIs the closest slant distance of the target to the radar,vrto move the target radial velocity, vaAs the speed of the carrier, dmIs the equivalent phase center spacing of the mth channel from the reference channel.
In one embodiment of the invention, the registered signal C in step 3m(fr,ta) Expressed as:
wherein, wr(. is a function of the distance window, wa(. is a function of the azimuth window, frIs the distance frequency, taFor azimuth slow time, fcIs the carrier frequency, c is the speed of light, j represents the unit of imaginary number, XnIs the target azimuth position, RBIs the closest slope distance of the target to the radar, vrTo move the target radial velocity, vaAs the speed of the carrier, dmIs the equivalent phase center spacing of the mth channel from the reference channel.
In one embodiment of the present invention, the registered signal C is processed in step 4m(fr,ta) Sequentially carrying out azimuth spectrum compression and distance deskew processing to obtain a deskew-spectrum compressed phase history domain signal Dm(fr,τa) The method comprises the following steps:
step 4.1, registering the signal C after registrationm(fr,ta) And an azimuth spectrum compression function H2Multiplying for azimuth spectrum compression, wherein the azimuth spectrum compression function H2Expressed as:
wherein f isrIs the distance frequency, taFor azimuth slow time, fcIs the carrier frequency, c is the speed of light, j represents the unit of imaginary number, RBIs the closest slope distance of the target to the radar, vaIs the carrier speed;
step 4.2, performing distance deviation treatment after azimuth spectrum compressionNamely, carrying out variable substitution: (f)r+fc)ta＝fcτaObtaining the phase history domain signal D after the distance is deskewedm(fr,τa) Expressed as:
wherein, wr(. is a function of the distance window, wa(. is a function of the azimuth window, frIs the distance frequency, gamma is the distance modulation frequency, tauaFor slow time of orientation after variable substitution, fcIs the carrier frequency, c is the speed of light, j represents the unit of imaginary number, XnIs the target azimuth position, RBIs the closest slope distance of the target to the radar, vrTo move the target radial velocity, vaAs the speed of the carrier, dmIs the equivalent phase center spacing of the mth channel from the reference channel, and λ is the wavelength.
In one embodiment of the invention, the two-dimensional image domain data E in step 5m(tr,fa) Expressed as:
where sinc (·) is the sinc function, B is the transmission signal bandwidth, and T isaTo synthesize the aperture time, trFor a fast time of distance, faIs the azimuthal Doppler frequency, fcIs the carrier frequency, c is the speed of light, j is the unit of imaginary number, XnIs the target azimuth position, RBIs the closest slope distance of the target to the radar, vrTo move the target radial velocity, vaAs the speed of the carrier, dmIs the equivalent phase center spacing of the mth channel from the reference channel, and λ is the wavelength.
In one embodiment of the present invention, the clutter suppressed two-dimensional image domain data F in step 6m(tr,fa) Expressed as:
where sinc (·) is the sinc function, B is the transmission signal bandwidth, and T isaTo synthesize the aperture time, trFor a fast time of distance, faIs the azimuthal Doppler frequency, fcIs the carrier frequency, c is the speed of light, j is the unit of imaginary number, XnIs the target azimuth position, RBIs the closest slope distance of the target to the radar, vrTo move the target radial velocity, vaAs the speed of the carrier, dmIs the equivalent phase center spacing of the mth channel from the reference channel, and λ is the wavelength.
In an embodiment of the invention, in the step 7, the clutter suppressed two-dimensional image domain data F with M being 2 to M channels are sequentially processedm(tr,fa) Performing constant false alarm detection includes:
step 7.1, two-dimensional image domain data F after clutter suppressionm(tr,fa) Taking a model to obtain | Fm(tr,fa) | is expressed as:
where sinc (·) is the sinc function, B is the transmission signal bandwidth, and T isaTo synthesize the aperture time, trFor a fast time of distance, faIs the azimuthal Doppler frequency, fcIs the carrier frequency, c is the speed of light, j is the unit of imaginary number, XnIs the target azimuth position, RBIs the closest slope distance of the target to the radar, vrTo move the target radial velocity, vaAs the speed of the carrier, dmIs the equivalent phase center distance between the mth channel and the reference channel, λ is the wavelength, | · | is the modulus;
step 7.2, aligning the | F according to a preset constant false alarm rate detection methodm(tr,fa) And | performing constant false alarm rate detection to obtain K moving target detection results, wherein the preset constant false alarm rate detection method comprises a CA-CFAR algorithm, an OS-CFAR algorithm and a GO-CF algorithmAnd (5) an AR algorithm.
In one embodiment of the present invention, said step 9 is to said local joint pixel data Gm,k(tr,fa) Performing local combined space-time optimization processing, and searching out the kth moving target radial velocity vkThe method comprises the following steps:
step 9.1, establishing a local joint space-time optimization model, wherein the local joint space-time optimization model is expressed as:
wherein s.t denotes the constraint of the optimization problem, WoptIn order to optimize the weight vector,
for locally combining pixel data Gm,k(tr,fa) C is the array flow pattern vector, Q ═ 1,0]HFor constraining the vector, (.)HPerforming matrix conjugate transposition;
step 9.2, solving the local joint space-time optimization model by utilizing a Lagrange multiplier algorithm to obtain an optimization weight vector WoptSaid optimization weight vector WoptExpressed as:
and 9.3, estimating K moving target radial velocities according to a Capon spectral peak search method, wherein the K moving target radial velocity estimation value is as follows:
in an embodiment of the present invention, the position offset of the kth moving target in the step 10 represents:
wherein,
is the k-th moving target radial velocity estimated value, RBIs the closest slope distance of the target to the radar, vaIs the carrier speed.
Compared with the prior art, the invention has the beneficial effects that:
according to the multi-channel SAR-GMTI image domain two-step processing method provided by the invention, the image domain combined clutter cancellation processing is carried out by utilizing different channels and the reference channel, and the intersection is taken for the detection result, so that the moving targets with different speeds can be ensured to have enough gain after clutter suppression, the problem that the energy loss of the moving targets with different speeds is larger by a non-optimal method is solved, and the high-probability detection and high-precision speed measurement positioning of the ground moving targets are realized while a static scene is imaged.
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Drawings
Fig. 1(a) to fig. 1(b) are schematic flow diagrams of a two-step processing method for a multi-channel SAR-GMTI image domain according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a conventional multi-channel SAR-GMTI two-dimensional geometric observation model;
FIG. 3 is a schematic diagram of a cell average CA-CFAR algorithm according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a local combined pixel data model extracted in the second step of the multi-channel SAR-GMTI image domain two-step processing method according to the embodiment of the present invention;
fig. 5(a) -5 (b) are schematic diagrams of an original complex image and a moving target position used in a simulation experiment provided by an embodiment of the present invention;
6(a) -6 (b) are schematic diagrams comparing the gain results of the moving target after clutter suppression according to the method and other two non-optimal methods;
FIG. 7 is a schematic diagram showing the comparison of the probability results of moving target detection by the method of the present invention with two other non-optimal methods;
FIG. 8 is a schematic diagram showing the comparison of the root mean square error results of the velocity measurement of the moving target according to the method of the present invention with two other non-optimal methods;
fig. 9(a) -9 (b) are schematic diagrams illustrating comparison of root mean square error results of velocity measurement of a moving target in the presence of channel phase errors and registration errors in the method of the present invention and the optimal method.
Detailed Description
The present invention will be described in further detail with reference to specific examples, but the embodiments of the present invention are not limited thereto.
Example one
Referring to fig. 1(a) to fig. 1(b), fig. 1(a) to fig. 1(b) are schematic flow diagrams of a two-step processing method for a multi-channel SAR-GMTI image domain according to an embodiment of the present invention, where fig. (b) is a schematic flow diagram of a two-step processing method for an image domain corresponding to a specific channel number of 4. The embodiment of the invention provides a two-step processing method of a multi-channel SAR-GMTI image domain, which comprises the following steps:
Specifically, referring to fig. 2, fig. 2 is a schematic diagram of a conventional multi-channel SAR-GMTI two-dimensional geometric observation model, in this embodiment, the multi-channel SAR-GMTI two-dimensional geometric observation model includes a two-dimensional observation coordinate system constructed by using an X-axis to represent an azimuth direction and a Y-axis to represent a distance direction, and a radar platform is positioned at an origin with a velocity vaMoving in azimuth, 1, M and M denote the 1 st channel (reference channel), the M-th channel and the Mth channel, respectively, dmIs the equivalent phase center spacing between the mth channel and the reference channel. At the initial moment, the moving object is located at the coordinate (X)n,RB) At point T, the skew distances to the 1 st, M th and M th channels are respectively represented as R1、Rm、RM，vxAnd vrRespectively, the heading speed and the radial speed of the moving target. According to the multi-channel equivalent phase center model, approximation is carried out by using second-order Taylor expansion, and t can be obtainedaThe slope distance between the instant moving target and the mth channel is as follows:
in this embodiment, for a multi-channel SAR having M channels, a first channel is used as a reference channel, but the first channel is not limited to be used as the reference channel, and other channels can also be selected as the reference channel, a chirp signal is transmitted by using the reference channel, and echo signals corresponding to a two-dimensional time domain are simultaneously received by using the M channels, where an echo signal received by the M-th channel in the two-dimensional time domain is am(tr,ta) Expressed as:
wherein, wr(. is a function of the distance window, wa(. is a function of the azimuth window, trIs a distance, fast time, taFor azimuth slow time, t ═ tr+taIs full time, fcIs the carrier frequency, gamma is the frequency modulation rate, c is the speed of light, j is the imaginary unit, Rm(ta) Is the slope course of the target, the slope course R of the targetm(ta) Expressed as:
wherein, XnIs the target azimuth position, RBIs the closest slope distance of the target to the radar, vrTo move the target radial velocity, vaAs the speed of the carrier, dmIs the m < th > oneEquivalent phase center spacing of the channel from the reference channel.
Specifically, in this embodiment, the echo signal received in step 1 is am(tr,ta) Distance pulse pressure compression processing is performed, the distance pulse pressure compression method includes a time domain matching method, a frequency domain multiplication method and a dechirp method, the embodiment adopts but is not limited to the frequency domain multiplication method, and the specific frequency domain multiplication method includes:
for the echo signal Am(tr,ta) Distance FFT is carried out, and the distance frequency domain is transferred to the distance frequency domain to obtain the distance frequency domain echo signal Am(fr,ta) Echo signal A in the range frequency domainm(fr,ta) Expressed as:
wherein f isrIs the distance frequency
Then, for the range frequency domain echo signal Am(fr,ta) Distance-direction matched filtering is carried out to obtain a phase history domain signal Bm(fr,ta) In particular range frequency domain echo signals Am(fr,ta) And matching function
Multiplying to obtain phase history domain signal Bm(fr,ta) Phase history domain signal Bm(fr,ta) Expressed as:
wherein, wr(. is a function of the distance window, wa(. is a function of the azimuth window, frIs the distance frequency, taFor azimuth slow time, fcIs the carrier frequency, c is the speed of light, j represents the unit of imaginary number, XnIs the target azimuth position, RBIs the closest slope distance of the target to the radar, vrTo move the target radial velocity, vaAs the speed of the carrier, dmIs the equivalent phase center spacing of the mth channel from the reference channel.
Step 3, taking the reference channel as a benchmark to the phase process domain signal Bm(fr,ta) Carrying out time delay compensation and channel equalization processing to obtain a registered signal Cm(fr,ta)。
Specifically, in step 3 of this embodiment, the phase history domain signal B obtained in step 2 is compared with the reference channel as a referencem(fr,ta) Carrying out time delay compensation and channel equalization processing to obtain a registered signal Cm(fr,ta) Registered signal Cm(fr,ta) Expressed as:
wherein, wr(. is a function of the distance window, wa(. is a function of the azimuth window, frIs the distance frequency, taFor azimuth slow time, fcIs the carrier frequency, c is the speed of light, j represents the unit of imaginary number, XnIs the target azimuth position, RBIs the closest slope distance of the target to the radar, vrTo move the target radial velocity, vaAs the speed of the carrier, dmIs the equivalent phase center spacing of the mth channel from the reference channel.
Specifically, in this embodiment, the registered signal C obtained in step 3 is first processedm(fr,ta) Sequentially compressing azimuth spectrum and deskewing distance to avoid false target generated by moving target speed, and obtaining phase history domain signal D after deskew-spectrum compressionm(fr,τa) The specific step 4 comprises:
step 4.1, registering the signal C after registrationm(fr,ta) And an azimuth spectrum compression function H2Multiplying for azimuth spectrum compression, wherein the azimuth spectrum compression function H2Expressed as:
wherein f isrIs the distance frequency, taFor azimuth slow time, fcIs the carrier frequency, c is the speed of light, j represents the unit of imaginary number, RBIs the closest slope distance of the target to the radar, vaIs the carrier speed.
Step 4.2, performing distance deviation treatment after azimuth spectrum compression, namely performing variable substitution: (f)r+fc)ta＝fcτaObtaining the phase history domain signal D after the distance is deskewedm(fr,τa) Expressed as:
wherein, wr(. is a function of the distance window, wa(. is a function of the azimuth window, frIs distance frequency, gamma is distance modulation frequency, tauaFor slow time of orientation after variable substitution, fcIs the carrier frequency, c is the speed of light, j represents the unit of imaginary number, XnIs the target azimuth position, RBIs the closest slope distance of the target to the radar, vrIn order to move the radial velocity of the target,vaas the speed of the carrier, dmIs the equivalent phase center spacing of the mth channel from the reference channel, and λ is the wavelength.
Specifically, in this embodiment, the phase history domain signal D after the deskew-spectrum compression obtained in step 4 is processedm(fr,τa) Performing range IFFT and azimuth FFT to obtain two-dimensional image domain data Em(tr,fa) Two-dimensional image domain data Em(tr,fa) Expressed as:
where sinc (·) is the sinc function, B is the transmission signal bandwidth, and T isaTo synthesize the aperture time, trIs a distance, faIs the azimuthal Doppler frequency, fcIs the carrier frequency, c is the speed of light, j is the unit of imaginary number, XnIs the target azimuth position, RBIs the closest slope distance of the target to the radar, vrTo move the target radial velocity, vaAs the speed of the carrier, dmIs the equivalent phase center spacing of the mth channel from the reference channel, and λ is the wavelength.
Specifically, in this embodiment, the two-dimensional image domain data E of M channels is obtained through the steps 1 to 5m(tr,fa) Using two-dimensional image domain data E of channels with M of 2-Mm(tr,fa) Two-dimensional image domain data E of each of the reference channels (m 1)1(tr,fa) Clutter cancellation processing is carried out to obtain clutter suppressed two-dimensional image domain data F with M being 2-M channelsm(tr,fa) Two-dimensional image domain data F after clutter suppression of mth channelm(tr,fa) Expressed as:
where sinc (·) is the sinc function, B is the transmission signal bandwidth, and T isaTo synthesize the aperture time, trIs a distance, faIs the azimuthal Doppler frequency, fcIs the carrier frequency, c is the speed of light, j is the unit of imaginary number, XnIs the target azimuth position, RBIs the closest slope distance of the target to the radar, vrTo move the target radial velocity, vaAs the speed of the carrier, dmIs the equivalent phase center spacing of the mth channel from the reference channel, and λ is the wavelength.
Step 7, sequentially carrying out clutter suppression on the two-dimensional image domain data F with M of 2-M channelsm(tr,fa) And performing constant false alarm detection, and performing first-step processing on a union set of all detection results to obtain K detection results of the moving targets, wherein K is an integer greater than 0.
Specifically, the embodiment sequentially performs clutter suppression on the two-dimensional image domain data F after M is 2 to M channelsm(tr,fa) And (3) carrying out constant false alarm detection to obtain K detection results of the moving targets, wherein the specific step 7 comprises the following steps:
step 7.1, two-dimensional image domain data F after clutter suppressionm(tr,fa) Taking a model to obtain | Fm(tr,fa) | is expressed as:
where sinc (·) is the sinc function, B is the transmission signal bandwidth, and T isaTo synthesize the aperture time, trIs a distance, faIs the azimuthal Doppler frequency, fcIs the carrier frequency, c is the speed of light, j is the unit of imaginary number, XnIs the target azimuth position, RBIs the closest slope distance of the target to the radar, vrTo move the target radial velocity, vaAs the speed of the carrier, dmIs the equivalent phase center distance between the mth channel and the reference channel, λ is the wavelength, | · | is the modulus;
step 7.2, aligning the | F according to a preset constant false alarm rate detection methodm(tr,fa) And | performing constant false alarm detection to obtain K moving target detection results. The preset Constant False Alarm detection method comprises a Cell Average Constant False Alarm algorithm (CA-CFAR for short), an Order statistical Constant False Alarm algorithm (OS-CFAR for short) and a maximum selection Constant False Alarm algorithm (GO-CFAR for short). In this embodiment, the CA-CFAR algorithm is adopted but not limited to be adopted, please refer to fig. 3, fig. 3 is a schematic diagram of the cell average CA-CFAR algorithm provided in the embodiment of the present invention, and the implementation process of the CA-CFAR algorithm is as follows: if the unit D to be detected is processed, xiSampling all reference units in a two-dimensional reference plane, wherein N is the number of the reference units, T is a threshold adjustment coefficient, and calculating the average value of all the sampled reference units
Comparing the mean value Z with a threshold value IcIf the mean value is greater than the threshold value IcIf not, the target is judged to be a motionless target.
Specifically, in this embodiment, after the constant false alarm detection is performed in step 7, detection results of K moving objects are obtained, and the kth moving object is in the two-dimensional image domain data | Fm(tr,fa) The position of | is (X)k,Yk) Returning to the two-dimensional image domain data E before clutter suppressionm(tr,fa) To (X)k,Yk) As a center, please refer to fig. 4, fig. 4 is a schematic diagram of a local joint pixel data model extracted in the second step of the multi-channel SAR-GMTI image domain two-step processing method according to the embodiment of the present invention, and the embodiment of the present invention extracts a local joint pixel data model from two-dimensional image domain data E according to the local joint pixel data model shown in fig. 4m(tr,fa) Extracting local joint pixel data G related to kth moving objectm,k(tr,fa)，XkThe position of the kth moving target in the distance direction, YkThe position of the kth moving target in the azimuth direction. Wherein, the solid black pixel in FIG. 4 is (X)k,Yk) Corresponding to pixels, the single-slant stripe pixels represent the condition that moving target signals are leaked to adjacent pixels due to channel errors, registration errors, defocusing and other factors, the selected moving target combined pixels serve as protection samples, and the multiple stripe pixels represent pixels which only contain clutter and noise and do not contain moving targets.
Step 9, for the local joint pixel data Gm,k(tr,fa) Performing local joint space-time optimization processing, and searching out the estimated value of the radial velocity of the kth moving target
Specifically, the present embodiment deals with the extracted local joint pixel data Gm,k(tr,fa) A local joint space-time optimal position is made, and a radial velocity estimated value of the kth moving target is obtained through search processing
The specific step 9 comprises:
step 9.1, establishing a local joint space-time optimization model, wherein the local joint space-time optimization model is expressed as:
wherein s.t denotes the constraint of the optimization problem, WoptIn order to optimize the weight vector,
for locally combining pixel data Gm,k(tr,fa) C is the array flow pattern vector, Q ═ 1,0]HFor constraining the vector, (.)HAnd is a matrix conjugate transpose.
Step 9.2, solving the local joint space-time optimization model by utilizing a Lagrange multiplier algorithm to obtain an optimization weight vector WoptSaid optimization weight vector WoptExpressed as:
step 9.3, estimating K moving target radial velocities according to a Peng (Capon) spectrum peak search method, wherein the K moving target radial velocity estimation value is as follows:
Specifically, the present embodiment obtains the radial velocity estimation value of the moving object according to step 9
Calculating the position offset of each moving object, and then using the two-dimensional image domain data E of the reference channel1(tr,fa) And (4) according to the position offset of each moving target, repositioning and marking the kth moving target to finish the second step of processing, and repeating the steps 8-10 until the K moving targets are repositioned and marked. And marking after repositioning to obtain an SAR image simultaneously containing a static scene and a moving target. Wherein, the relocation of each moving target to its true position offset is represented by:
wherein,
is the k-th moving target radial velocity estimated value, RBIs the closest slope distance of the target to the radar, vaIs the carrier speed.
In order to verify the multi-channel SAR-GMTI image domain two-step processing method provided by the application, the following simulation experiment is used for further explanation:
first, simulation experiment
1. Simulation conditions
Please refer to fig. 5a to 5b, fig. 5a to 5b are schematic diagrams of an original complex image and a radial road where a moving target is located for a simulation experiment provided by an embodiment of the present invention, and fig. 5(a) is a schematic diagram of an original complex image for a simulation experiment provided by an embodiment of the present invention, in this embodiment, a complex image obtained by processing SAR measured data shown in fig. 5(a) is used to perform an inverse imaging operation to obtain clutter original data, and a speed is selected at intervals of 0.5 m/s in a speed interval of 0 m/s to 16 m/s to perform simulation, so that 32 moving targets are simulated together, fig. 5(b) is a schematic diagram of a radial road where a moving target location is located for a simulation experiment provided by an embodiment of the present invention, and simulation parameters of the experiment are shown in table 1.
TABLE 1 SAR simulation parameters
2. Simulation content and result analysis
Simulation 1:
referring to fig. 6(a) to 6(b), fig. 6a to 6b are schematic diagrams illustrating comparison of clutter suppression post-moving target gain results between the method and other two non-optimal methods, in this embodiment, an Adjacent phase elimination method (AC for short), a Subspace Projection method (SP for short) and the method of the present invention are respectively adopted to perform clutter suppression processing on radar echo data; and extracting the residual energy of each moving target for analysis. As can be seen from fig. 6(a) -6 (b), the method of the present invention utilizes channels with different intervals to perform the joint clutter suppression processing, so that the residual energy of moving targets with different speeds after clutter suppression is always higher than that of the AC and SP methods, and therefore, the method has a higher signal-to-noise ratio.
Simulation 2:
referring to fig. 7, fig. 7 is a schematic diagram illustrating a comparison between the detection probability results of moving objects by the method of the present invention and other two non-optimal methods, in this embodiment, an AC method, an SP method, and the method of the present invention are respectively used to perform 500 monte carlo experiments on simulation data, different random noise is added in each experiment, and 33 moving objects are detected by the same threshold. As can be seen from FIG. 7, the method of the present invention can obtain higher signal-to-noise-and-noise ratios for moving targets with different radial velocities, and therefore, the method can always obtain higher detection probability.
Simulation 3:
referring to fig. 8, fig. 8 is a schematic diagram illustrating a comparison of root mean square error results of speed measurement of moving targets with the method of the present invention and two other non-optimal methods, according to a result of simulation 2, in this embodiment, 23 moving targets with a detection probability of 1 and a radial speed of 3 m/s to 14 m/s are selected and then subjected to 500 monte carlo experiments, and a proximity destructive interference method (AC-ATI for short), a Projection interference method (SP-ATI for short), a Projection period search method (SP-NP for short) and the method of the present invention are respectively adopted to estimate the radial speed of the moving targets. As can be seen from fig. 8, the method of the present invention performs local combined optimal processing on the image domain before clutter suppression, and constrains the energy of the moving object to be unchanged, so that the method has a higher signal-to-noise-and-noise ratio during velocity measurement, and therefore, the root mean square error of velocity measurement is the lowest.
And (4) simulation:
referring to fig. 9(a) to 9(b), fig. 9(a) to 9(b) are schematic diagrams comparing root mean square error results of velocity measurement of a moving target when a channel Phase error and a registration error exist between the method of the present invention and an optimal Extended Displaced Phase Center offset method (EDPCA for short), in this embodiment, the channel Phase error of 1 to 10 degrees and the image registration error of 0 to 1 pixel are respectively added to simulation data, and the EDPCA method and the method used by the present invention are used to measure the velocity of the moving target respectively. As can be seen from fig. 9(a) to 9(b), the method of the present invention uses local joint pixel data to protect the moving target information, so that the velocity measurement accuracy can be ensured not to be affected under the condition of channel phase error and registration error.
In summary, in the multi-channel SAR-GMTI image domain two-step processing method provided by this embodiment, by performing image domain combined clutter cancellation processing by using different channels and reference channels and taking intersection for the detection result, it can be ensured that the moving targets with different speeds have sufficient gain after clutter suppression, thereby avoiding the problem that the moving target energy loss is large in some speeds by non-optimal methods, and realizing high-probability detection and high-precision speed measurement positioning of the ground moving target while imaging a static scene; the method comprises the steps of detecting a moving target, returning to an image domain before clutter suppression, performing local combined optimal processing, and constraining the energy of the moving target to be kept unchanged, so that the speed measurement precision is improved compared with a non-optimal method, meanwhile, local data can be extracted according to the position of the moving target detected in the first step to perform optimal processing for spectrum estimation, the operation amount of the algorithm is greatly reduced compared with the global optimal processing of an optimal algorithm, and local neighborhood pixels are extracted by taking the specific position of the moving target as the center when a covariance matrix is estimated for performing combined processing, so that the influence of channel and registration errors on the covariance matrix estimation precision can be solved.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (10)
1. A multi-channel SAR-GMTI image domain two-step processing method is characterized by comprising the following steps:
step 1, taking a first channel of a multi-channel SAR as a reference channel and transmitting signals by using the reference channel, wherein M channels of the multi-channel SAR simultaneously receive echo signals in a two-dimensional time domain, M is an integer larger than 0, and the echo signal received by the mth channel is Am(tr,ta)，0<M is less than or equal to M, wherein trFor a fast time of distance, taThe azimuth slow time;
step 2, for the echo signal Am(tr,ta) Distance FFT is carried out to obtain distance frequency domain echo signal Am(fr,ta) And using the transmitting signal as a matching function to the range frequency domain echo signal Am(fr,ta) Distance-direction matched filtering is carried out to obtain a phase history domain signal Bm(fr,ta) Wherein f isrIs the range frequency;
step 3, taking the reference channel as a benchmark, and aligning the phase history domain signal Bm(fr,ta) Carrying out time delay compensation and channel equalization processing to obtain a registered signal Cm(fr,ta)；
Step 4, registering the signal Cm(fr,ta) Sequentially carrying out azimuth spectrum compression and distance deskew processing to obtain a deskew-spectrum compressed phase history domain signal Dm(fr,τa)；
Step 5, the phase history domain signal D after the declivity-spectrum compression is carried outm(fr,τa) Performing distance IFFT and orientation FFT to obtain the m-th channel two-dimensional image domain data Em(tr,fa) Wherein f isaIs the Doppler frequency;
step 6, utilizing the two-dimensional image domain data E of each channel with M of 2-Mm(tr,fa) Two-dimensional image domain data E of the reference channels respectively1(tr,fa) Clutter cancellation processing is carried out to obtain clutter suppressed two-dimensional image domain data F with M being 2-M channelsm(tr,fa)；
Step 7, sequentially carrying out clutter suppression on the two-dimensional image domain data F with M being 2-M channelsm(tr,fa) Performing constant false alarm rate detection, and performing first-step processing on a union set of detection results to obtain K detection results of moving targets, wherein K is an integer greater than 0;
step 8, two-dimensional image domain data F of the kth moving target detected by the first step after clutter suppressionm(tr,fa) Position (X)k,Yk)，0<K is less than or equal to K, in the formula (X)k,Yk) As a center, from the two-dimensional image domain data Em(tr,fa) Extracting local joint pixel data G related to the kth moving targetm,k(tr,fa) Wherein X iskThe position of the kth moving target in the distance direction, YkFor the k-th moving target in azimuthA location;
step 9, for the local joint pixel data Gm,k tr,faPerforming local joint space-time optimization processing, and searching out the estimated value of the radial velocity of the kth moving target
Step 10, according to the estimated value of the radial velocity of the kth moving target
Calculating the position offset of the kth moving target, and using the two-dimensional image domain data E1(tr,fa) And repositioning the kth moving target according to the position offset of the kth moving target and labeling to complete the second-step processing, and repeating the steps of 8-10 until the K moving targets are repositioned and labeled to obtain the final multi-channel SAR image.
2. The multi-channel SAR-GMTI image domain two-step processing method according to claim 1, characterized in that the echo signal A in step 1m(tr,ta) Expressed as:
wherein, wr(. is a function of the distance window, wa(. is a function of the azimuth window, trIs a distance, fast time, taFor azimuth slow time, t ═ tr+taIs full time, fcIs the carrier frequency, gamma is the frequency modulation rate, c is the speed of light, j is the imaginary unit, Rm(ta) Is the slope course of the target, the slope course R of the targetm(ta) Expressed as:
wherein, XnIs the target azimuth position, RBIs the closest slope distance of the target to the radar, vrTo move the target radial velocity, vaAs the speed of the carrier, dmIs the equivalent phase center spacing of the mth channel from the reference channel.
3. The multi-channel SAR-GMTI image domain two-step processing method according to claim 1, characterized in that the phase history domain signal B in the step 2m(fr,ta) Expressed as:
wherein, wr(. is a function of the distance window, wa(. is a function of the azimuth window, frIs the distance frequency, taFor azimuth slow time, fcIs the carrier frequency, c is the speed of light, j represents the unit of imaginary number, XnIs the target azimuth position, RBIs the closest slope distance of the target to the radar, vrTo move the target radial velocity, vaAs the speed of the carrier, dmIs the equivalent phase center spacing of the mth channel from the reference channel.
4. The multi-channel SAR-GMTI image domain two-step processing method according to claim 1, characterized in that the registered signal C in step 3m(fr,ta) Expressed as:
wherein, wr(. is a function of the distance window, wa(. is a function of the azimuth window, frIs the distance frequency, taFor azimuth slow time, fcIs the carrier frequency, c is the speed of light, j represents the unit of imaginary number, XnIs the target azimuth position, RBIs the closest slope distance of the target to the radar, vrTo move the target radial velocity, vaAs the speed of the carrier, dmIs the equivalent phase center spacing of the mth channel from the reference channel.
5. The multi-channel SAR-GMTI image domain two-step processing method according to claim 1, characterized in that the registered signal C is processed in the step 4m(fr,ta) Sequentially carrying out azimuth spectrum compression and distance deskew processing to obtain a deskew-spectrum compressed phase history domain signal Dm(fr,τa) The method comprises the following steps:
step 4.1, registering the signal C after registrationm(fr,ta) And an azimuth spectrum compression function H2Multiplying for azimuth spectrum compression, wherein the azimuth spectrum compression function H2Expressed as:
wherein f isrIs the distance frequency, taFor azimuth slow time, fcIs the carrier frequency, c is the speed of light, j represents the unit of imaginary number, RBIs the closest slope distance of the target to the radar, vaIs the carrier speed;
step 4.2, performing distance deviation treatment after azimuth spectrum compression, namely performing variable substitution: (f)r+fc)ta＝fcτaObtaining the phase history domain signal D after the distance is deskewedm(fr,τa) Expressed as:
wherein, wr(. is a function of the distance window, wa(. is a function of the azimuth window, frIs the distance frequency, gamma is the distance modulation frequency, tauaFor slow time of orientation after variable substitution, fcIs the carrier frequency, c is the speed of light, j represents the unit of imaginary number, XnTo the eyesNominal azimuth position, RBIs the closest slope distance of the target to the radar, vrTo move the target radial velocity, vaAs the speed of the carrier, dmIs the equivalent phase center spacing of the mth channel from the reference channel, and λ is the wavelength.
6. The multi-channel SAR-GMTI image domain two-step processing method according to claim 1, characterized in that the two-dimensional image domain data E in the step 5m(tr,fa) Expressed as:
where sinc (·) is the sinc function, B is the transmission signal bandwidth, and T isaTo synthesize the aperture time, trFor a fast time of distance, faIs the azimuthal Doppler frequency, fcIs the carrier frequency, c is the speed of light, j is the unit of imaginary number, XnIs the target azimuth position, RBIs the closest slope distance of the target to the radar, vrTo move the target radial velocity, vaAs the speed of the carrier, dmIs the equivalent phase center spacing of the mth channel from the reference channel, and λ is the wavelength.
7. The multi-channel SAR-GMTI image domain two-step processing method according to claim 1, wherein the clutter suppressed two-dimensional image domain data F in the step 6m(tr,fa) Expressed as:
where sinc (·) is the sinc function, B is the transmission signal bandwidth, and T isaTo synthesize the aperture time, trFor a fast time of distance, faIs the azimuthal Doppler frequency, fcIs the carrier frequency, c is the speed of light, j is the unit of imaginary number, XnIs the target azimuth position, RBFor targets and radarsNearest slope, vrTo move the target radial velocity, vaAs the speed of the carrier, dmIs the equivalent phase center spacing of the mth channel from the reference channel, and λ is the wavelength.
8. The multi-channel SAR-GMTI image domain two-step processing method according to claim 1, wherein in the step 7, the clutter suppressed two-dimensional image domain data F with M being 2-M channels are sequentially processedm(tr,fa) Performing constant false alarm detection includes:
step 7.1, two-dimensional image domain data F after clutter suppressionm(tr,fa) Taking a model to obtain | Fm(tr,fa) | is expressed as:
where sinc (·) is the sinc function, B is the transmission signal bandwidth, and T isaTo synthesize the aperture time, trFor a fast time of distance, faIs the azimuthal Doppler frequency, fcIs the carrier frequency, c is the speed of light, j is the unit of imaginary number, XnIs the target azimuth position, RBIs the closest slope distance of the target to the radar, vrTo move the target radial velocity, vaAs the speed of the carrier, dmIs the equivalent phase center distance between the mth channel and the reference channel, λ is the wavelength, | · | is the modulus;
step 7.2, aligning the | F according to a preset constant false alarm rate detection methodm(tr,fa) And | performing constant false alarm detection to obtain K moving target detection results, wherein the preset constant false alarm detection method comprises a CA-CFAR algorithm, an OS-CFAR algorithm and a GO-CFAR algorithm.
9. The multi-channel SAR-GMTI image domain two-step processing method according to claim 1, wherein the step 9 is to the local combined pixel data Gm,k(tr,fa) Performing local joint space-time optimization processing, and searching out the firstk moving target radial velocities vkThe method comprises the following steps:
step 9.1, establishing a local joint space-time optimization model, wherein the local joint space-time optimization model is expressed as:
wherein s.t denotes a constraint in the optimization problem, WoptIn order to optimize the weight vector,
for locally combining pixel data Gm,k(tr,fa) C is the array flow pattern vector, Q ═ 1,0]HFor constraining the vector, (.)HPerforming matrix conjugate transposition;
step 9.2, solving the local joint space-time optimization model by utilizing a Lagrange multiplier algorithm to obtain an optimization weight vector WoptSaid optimization weight vector WoptExpressed as:
and 9.3, estimating K moving target radial velocities according to a Capon spectral peak search method, wherein the K moving target radial velocity estimation value is as follows:
10. the multi-channel SAR-GMTI image domain two-step processing method according to claim 1, wherein the position offset of the kth moving target in the step 10 represents:
wherein,
is the k-th moving target radial velocity estimated value, RBIs the closest slope distance of the target to the radar, vaIs the carrier speed.
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