MIMO-SAR Deblurring Method and Device Based on OFDM-Chirp Signal and DBF Processing

Abstract

The present invention discloses a method and device for ambiguity resolution of MIMO-SAR based on OFDM-chirp signals and DBF processing. First, the original LFM signal is subjected to OFDM modulation, that is, zero-padding alternately in the frequency domain, to form two OFDM-chirp waveforms. The MIMO-SAR multi-channel is designed as a 4×1 array in the range-elevation direction; then a large mapping area is set up, that is, a setting of a scene with range ambiguity and echo simulation; the beam pointing angles of the target and its corresponding two ambiguous targets at each range index are calculated to form a steering vector matrix, the corresponding weighted vector matrix is calculated, and the DBF processing is performed on the echo data to resolve the ambiguity; OFDM demodulation is carried out, and then pulse compression is performed by multiplying with two pulse compression reference functions respectively; finally, imaging is completed using the range-Doppler algorithm. The present invention effectively utilizes the information resources in the time domain, frequency domain and spatial domain between the airborne radar and the target, making the ambiguity resolution algorithm more convenient, concise, accurate and effective, and also confirming the good application prospect of MIMO-SAR.

Description

Technical Field

[0001] The present invention belongs to the fields of radar imaging technology and radar ambiguity resolution, and relates to airborne multiple-input multiple-output synthetic aperture radar imaging technology and signal processing technology. Specifically, it relates to a method and device for MIMO-SAR ambiguity resolution based on OFDM-chirp signals and DBF processing. Background Art

[0002] Synthetic aperture radar (SAR) is a powerful active earth observation imaging radar, which has high imaging resolution and a large detection range, and is not affected by weather and time. However, for traditional SAR, due to the limitation of the minimum antenna area, it is impossible to achieve both high azimuth resolution and wide swath imaging capabilities. To solve this contradiction, the multiple-input multiple-output (MIMO) technology in the communication field is borrowed, and multiple-input multiple-output synthetic aperture radar (MIMO-SAR) emerges as the times require. As a new type of radar system, the unique performance of MIMO-SAR has attracted extensive attention. Compared with traditional SAR, MIMO-SAR can obtain far more degrees of freedom than the actual number of antennas through waveform and spatial diversity. By different combinations of transmitting antennas and receiving antennas, multiple equivalent phase centers are formed. The digital beamforming (DBF) technology at the receiving end also makes it possible to form multiple beams. Due to these system advantages, MIMO-SAR has become an important research direction for current and next-generation radar technologies. The current main research work on MIMO-SAR lies in the design and separation of orthogonal waveforms. Currently, several waveform diversity schemes with application prospects include short-time shifted orthogonal (STSO) waveforms, orthogonal frequency division multiplexing waveforms based on chirp (OFDM-chirp), stepped frequency waveforms, azimuth phase coding (APC) waveforms, etc.

[0003] Range ambiguity is a problem that often occurs in SAR systems and must be solved, otherwise there will be ghosting in the obtained SAR images. It is necessary to select an appropriate Pulse Repetition Frequency (PRF) to avoid range ambiguity within the main lobe of the antenna. In addition, the echoes from some strong scatterers entering through the antenna side lobes can also cause serious range ambiguity, such as the sub-satellite point problem. For these problems, researchers have made many meaningful attempts. For example, some people have analyzed the method of suppressing range ambiguity by alternately transmitting Up and Down Chirp signals, but it is found that this method only disperses and expands the energy of the ambiguous echoes and does not fundamentally eliminate them. Others have proposed an Azimuth Phase Coding (APC) method to suppress range ambiguity. This ingenious coding scheme makes the echoes in different range ambiguity regions have different degrees of frequency offset relative to the spectrum of the desired echo in the azimuth Doppler domain. The greater the frequency offset, the better the range ambiguity suppression effect, but this also depends on the selection of PRF. In addition, many researchers have considered suppressing range ambiguity through Digital Beam Forming (DBF) technology in the elevation direction to achieve High Resolution Wide Swath (HRWS) imaging. The basic idea is to divide each transmitted pulse into multiple sub-pulses, and then modulate the transmitted beam corresponding to each sub-pulse to different directions in the elevation direction, that is, irradiate different sub-swaths, so as to achieve wide-area coverage. From another perspective, DBF is essentially a spatial domain filtering technology and beam sharpening technology.

[0004] Regarding the orthogonal waveform design problem of MIMO-SAR, experts and scholars have also proposed many interesting solutions. Among them, Orthogonal Frequency Division Multiplexing (OFDM) technology, due to its good and strict frequency domain orthogonality, takes the lead in scientific research experiments. As one of the implementation methods of the multi-carrier transmission scheme, OFDM technology also has advantages such as a large time-bandwidth product, good anti-interference performance, high spectrum utilization rate, easy digitization, low implementation complexity, and easy application. Its modulation and demodulation can be realized by using IFFT / IDFT and FFT / DFT respectively. Summary of the Invention

[0005] Object of the Invention: The present invention provides a MIMO-SAR ambiguity resolution method and device based on OFDM-chirp signals and DBF processing, which separates multiple transmitted waveforms through the frequency domain orthogonality of the MIMO-SAR transmitted waveforms, and suppresses range ambiguity through the elevation multi-channel DBF technology. The algorithm is simple and efficient, and fundamentally eliminates ambiguity.

[0006] Technical Solution: A MIMO-SAR ambiguity resolution method based on OFDM-chirp signals and DBF processing according to the present invention includes the following steps:

[0007] (1) Waveform and channel design: Using a linear frequency modulation (LFM) signal with a certain duty cycle as the original signal, it is transformed into the frequency domain and zero-padded alternately to form the first OFDM-chirp signal. The spectrum of the first signal is shifted by one frequency domain sampling interval to form the second OFDM-chirp signal. The two signals respectively occupy the odd and even components of the spectrum, and the MIMO-SAR channel is designed as a range-elevation array;

[0008] (2) Construct a three-dimensional signal reception model with range ambiguity: Using an airborne side-looking synthetic aperture radar to obtain multi-channel echoes. The radar operates in the spotlight mode. Every other pulse interval, two OFDM-chirp waveforms are simultaneously transmitted to the same scatterer target. The echo is sampled to obtain range-azimuth-array three-dimensional echo data. Each target on a range gate corresponds to two ambiguous targets at a long distance respectively;

[0009] (3) Range-elevation DBF de-ambiguation: Calculate the beam steering angle corresponding to each MIMO-SAR channel to form a steering vector matrix; Use the steering vector matrix to calculate the weighted vector matrix, and multiply it with the three-dimensional echo data to complete the separation and suppression of range ambiguity;

[0010] (4) OFDM demodulation and pulse compression: Transform the echo data into the range frequency domain, separate the odd and even frequency point components, and then multiply them with two pulse compression reference functions respectively for pulse compression;

[0011] (5) According to the motion speed of the carrier platform and the antenna scanning parameters, perform range-Doppler algorithm processing on the processed echo data, and finally form a clear range-azimuth two-dimensional SAR image without ambiguity.

[0012] Furthermore, the implementation process of step (1) is as follows:

[0013] Using an LFM signal with a duty cycle of 25% as the original signal, adopting OFDM modulation, zero-padding the original LFM signal alternately in the frequency domain to double its spectrum width, forming the first OFDM-chirp signal, and then shifting the spectrum of this OFDM-chirp signal by one frequency domain sampling interval to obtain the second OFDM-chirp signal; The time domain expressions of the original LFM signal and the two OFDM-chirp signals are respectively:

[0014]

[0015]

[0016]

[0017] where s LFM is the original LFM signal, s OFDM1and s OFDM2 are two OFDM-chirp waveforms respectively, where t r is the range time, T p is the pulse width, k r is the frequency modulation slope, n r is the length of the original linear frequency modulation signal sequence, T s is the sampling interval, f s is the sampling frequency; the MIMO-SAR multi-channel design is a 4×1 array in the range-elevation direction. The channels point perpendicular to the track and perpendicular to the start of the main mapping strip scene. The elevation inclination angle is:

[0018]

[0019] where, θ in represents the elevation tilt angle of the channel, h0 is the radar platform height, and R0 is the slant range from the start of the main mapping strip to the radar platform.

[0020] Furthermore, the implementation process of the step (2) is as follows:

[0021] First, perform scene setting: The size of the main mapping strip within which the original LFM signal and the OFDM-chirp signal can be clearly imaged is where n r is the length of the original linear frequency modulation signal sequence, c is the speed of light, f s is the sampling frequency. For targets outside the main mapping strip, due to the incomplete waveform of the received echo signals, information is lost, and finally, it is manifested as a blurred SAR image. Therefore, when setting the scene, a sub-mapping strip is set before and after the main mapping strip, with the same size as the main mapping strip, so that the size of the simulation scene is Then, the signal received by each azimuth sampling point is composed of the addition of two blurred echoes and one target echo;

[0022] Next, perform echo simulation: Every other pulse repetition interval, two OFDM-chirp waveforms are simultaneously transmitted by two transmitting channels. After irradiating all scene targets, the echoes return. Four receiving channels simultaneously receive all the echoes to form four "range-azimuth" two-dimensional echo data. The echo data of these four channels are stacked together in sequence to form a three-dimensional data matrix of "range-azimuth-array".

[0023] Furthermore, the implementation process of the step (3) is as follows:

[0024] The range index of the main mapping strip is:

[0025] D r = R0+(0:nr-1)×l rg

[0026] Among them, R0 is the slant range from the start of the main survey strip to the radar platform, and n r is the length of the original chirp signal sequence, is the range gate length, c is the speed of light, and f s is the sampling frequency; then the time-domain expression of the echo received by a single channel is as follows:

[0027]

[0028]

[0029] Among them, echo i represents the single-channel echo, i = 1, 2, 3, 4, and t r is the range time, and pt n is the point target echo in the main survey strip, and pt a1n is the echo of the point target in the near-field main survey strip, and pt a2n is the echo of the point target in the far-field main survey strip, λ is the wavelength, and are the range indices of the near-field and far-field main survey strips respectively, and d i =(i - 1)W rs where W rs is the channel interval, and θ 1,2,3 is the beam pointing angle; the calculation method of the beam pointing angle is as follows:

[0030]

[0031]

[0032]

[0033] Among them, h0 is the height of the radar platform, m = 0, 1, 2,..., 2n r -1, R0 is the slant range from the start of the main survey strip to the radar platform, and n r is the length of the original chirp signal sequence, is the range gate length, c is the speed of light, and f s is the sampling frequency, and θ in represents the pitch tilt angle of the channel; the received data of the four channels are combined into a matrix as follows:

[0034]

[0035] V is the steering vector matrix, and its expression is as follows:

[0036]

[0037] To separate and suppress ambiguity, the weighted vector matrix is calculated using the steering vector matrix as follows:

[0038] W=(V H V) -1 V H

[0039] Finally, multiplying the echo matrix by the weighted vector matrix can separate the ambiguity, and the calculation is as follows:

[0040]

[0041] The first row data obtained after matrix multiplication is the echo of the target within the non-ambiguous imaging range.

[0042] Furthermore, the implementation process of step (4) is as follows:

[0043] Perform range FFT on the data matrix obtained after ambiguity resolution. To separate the two OFDM-chirp waveforms, separate the odd and even spectra to form two sets of echo data, and then multiply them by the corresponding two pulse compression reference functions in the frequency domain respectively for pulse compression; the calculation method of the frequency domain pulse compression reference function is as follows:

[0044] S[p]=DFT{s LFM}=[S[0],S[1],…,S[N - 1]]

[0045] S1[p]=[S[0],0,S[1],0,…,S[N - 1],0]

[0046] S2[p]=[0,S[0],0,S[1],…,0,S[N - 1]]

[0047] Among them, S[p] is the DFT transform of the original linear frequency modulation signal time domain sequence, and S1[p] and S2[p] two frequency domain pulse compression reference functions are obtained respectively after alternately padding with zeros.

[0048] Based on the same inventive concept, the present invention also provides a MIMO-SAR ambiguity resolution device based on OFDM-chirp signals and DBF processing, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is loaded into the processor, it implements the above-mentioned MIMO-SAR ambiguity resolution method based on OFDM-chirp signals and DBF processing.

[0049] Beneficial effects: Compared with the prior art, the present invention has the following beneficial effects: the present invention designs an OFDM modulation waveform with a wide imaging swath, establishes an airborne MIMO-SAR large-scene target model and a fuzzy echo model based on waveform design and the principle of distance ambiguity generation, and utilizes the frequency domain orthogonality of multi-transmission waveforms and the pitch-direction DBF spatial domain filtering technology to solve the distance fuzzy imaging ghosting problem during actual large swath imaging. The imaging results are clear and accurate. While adopting multi-channel technology, the information resources in the three dimensions of time domain, frequency domain and spatial domain between the airborne radar and the target are effectively utilized, making the defuzzification algorithm faster, more convenient, simpler, more accurate and effective, and can fundamentally eliminate distance ambiguity. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a flow chart of the present invention;

[0051] Figure 2 is the time domain waveform and spectrum of the original LFM signal;

[0052] Figure 3 The time domain waveform and spectrum of the OFDM modulation waveform;

[0053] Figure 4 The geometric model of the multi-channel antenna array for MIMO-SAR;

[0054] Figure 5 Set up schematics for large swath scenarios;

[0055] Figure 6 Generate schematics for range blur;

[0056] Figure 7 Simulation results of point target deblurring imaging;

[0057] Figure 8 This is the imaging result of the surface target scene affected by distance blur;

[0058] Figure 9 This is the imaging result after deblurring the surface target scene. DETAILED DESCRIPTION

[0059] The present invention will be further described in detail below with reference to the accompanying drawings.

[0060] The present invention provides a MIMO-SAR deambiguation method based on OFDM-chirp signal and DBF processing, such as Figure 1 As shown, the following steps are included:

[0061] Step 1: Taking the linear frequency modulation signal with a duty cycle of 25% as the original signal, adopting OFDM modulation, padding zeros alternately in the frequency domain for the original linear frequency modulation signal to double its spectral width, forming the first OFDM-chirp signal, and then shifting the spectrum of this OFDM-chirp signal by one frequency domain sampling interval to obtain the second OFDM-chirp signal; the time-domain expressions of the original LFM signal and the two OFDM-chirp signals are respectively:

[0062]

[0063]

[0064]

[0065] where, s LFM is the original linear frequency modulation signal, s OFDM1 and s OFDM2 are the two OFDM-chirp waveforms respectively, t r is the range time, T p is the pulse width, k r is the frequency modulation slope, n r is the length of the original linear frequency modulation signal sequence, T s is the sampling interval, f s is the sampling frequency. Figure 2 is the time-domain waveform and its spectrum of the original LFM signal, Figure 3 is the time-domain waveform and spectrum of the OFDM modulation waveform. It can be seen from Figure 2 and Figure 3 that the time-domain waveform length of the two OFDM waveforms is twice that of the original LFM waveform, and its spectrum is the same as the spectral envelope of the original LFM signal, but zeros are padded alternately between each spectral sampling point.

[0066] The MIMO-SAR multi-channel design is a 4×1 array in the range-elevation direction. The channels are directed perpendicular to the track and perpendicular to the start of the main mapping scene. The elevation inclination angle is

[0067]

[0068] where, θ in represents the elevation tilt angle of the channel, h0 is the radar platform height, and R0 is the slant range from the start of the main mapping scene to the radar platform. Figure 4 is the geometric model of the multi-channel antenna array of MIMO-SAR.

[0069] Step 2: Construct a three-dimensional signal reception model with range ambiguity: Use an airborne side-looking synthetic aperture radar to obtain multi-channel echoes. The radar operates in the spotlight mode. At each pulse interval, two OFDM-chirp waveforms are simultaneously transmitted to the same scatterer target. The range-azimuth-array three-dimensional echo data is obtained by sampling the echoes. Since the scene range is set to be very large, three times the detection range of the main mapping strip, there are two ambiguous targets at long distances corresponding to each target on each range gate.

[0070] First, perform scene setting: The size of the main mapping strip where the original LFM signal and OFDM-chirp signal can be clearly imaged is where n r is the length of the original linear frequency modulation signal sequence, c is the speed of light, f s is the sampling frequency. For targets outside the main mapping strip, the information is lost because the received echo signal waveform is incomplete, and finally it is manifested as a blurred SAR image. Therefore, when setting the scene, a sub-mapping strip is set in front of and behind the main mapping strip, with the same size as the main mapping strip, so that the size of the simulation scene is Figure 5 is the schematic diagram of the large mapping strip scene setting. The signal received by each azimuth sampling point is composed of the addition of two ambiguous echoes and one target echo. Figure 6 is the schematic diagram of the generation principle of range ambiguity. As can be seen from Figure 6 , half of the waveform and energy of the echoes of the sub-mapping strips in front of and behind the main mapping strip enter the echo interval of the main mapping strip. The range ambiguity generated by this part of the echo cannot use the orthogonality of the waveform to eliminate the ambiguity because its waveform is incomplete and the spectrum is not alternately filled with zeros.

[0071] Then, perform echo simulation: At each pulse repetition interval, two OFDM-chirp waveforms are simultaneously transmitted by two transmitting channels. After irradiating all scene targets, the echoes return. Four receiving channels simultaneously receive all echoes to form four "range-azimuth" two-dimensional echo data. The echo data of these four channels are stacked together at one time to form a three-dimensional data matrix of "range-azimuth-array".

[0072] Step 3: Range-elevation DBF de-ambiguation: Calculate the beam pointing angle corresponding to each MIMO-SAR channel to form a steering vector matrix; use the steering vector matrix to calculate the weighted vector matrix, and multiply it with the three-dimensional echo data to complete the separation and suppression of range ambiguity.

[0073] The range index of the main mapping strip can be written as D r = R0+(0:nr-1)×l rg , where R0 is the slant range from the start of the main mapping strip to the radar platform, n ris the length of the original chirp signal sequence, is the range gate length, c is the speed of light, f s is the sampling frequency. Then the time-domain expression of the echo received by a single channel is as follows:

[0074]

[0075]

[0076] where, echo i represents the single-channel echo, i = 1, 2, 3, 4, t r is the range time, pt n is the echo of the point target in the main survey strip, pt a1n is the echo of the point target in the near-field sub-survey strip, pt a2n is the echo of the point target in the far-field sub-survey strip, λ is the wavelength, and are the range indices of the near-field and far-field sub-survey strips respectively, d i =(i - 1)W rs ,W rs is the channel interval, θ 1,2,3 is the beam pointing angle. The calculation method of the beam pointing angle is as follows:

[0077]

[0078]

[0079]

[0080] where, h0 is the radar platform height, R0 is the slant range from the start of the main survey strip to the radar platform, n r is the length of the original chirp signal sequence, is the range gate length, c is the speed of light, f s is the sampling frequency, θ in represents the pitch tilt angle of the channel. The received data of the four channels are combined into a matrix M echo as follows:

[0081]

[0082] V is the steering vector matrix, and its expression is as follows:

[0083]

[0084] In order to separate and suppress ambiguity, the weighted vector matrix is calculated using the steering vector matrix as follows:

[0085] W=(V HV) -1 V H

[0086] Finally, multiply the echo matrix by the weighted vector matrix to separate the ambiguity, and the calculation is as follows:

[0087]

[0088] The first row data obtained after matrix multiplication is the echo of the target within the non-ambiguous imaging range.

[0089] Step 4: OFDM demodulation and pulse compression: Convert the echo data to the range frequency domain, separate the even and odd frequency point components, and then multiply them by the two-channel pulse compression reference functions respectively for pulse compression.

[0090] Perform a range FFT on the data matrix obtained after ambiguity resolution. To separate the two-channel OFDM-chirp waveforms, separate the even and odd spectra to form two sets of echo data, and then multiply them by the corresponding two-channel pulse compression reference functions respectively in the frequency domain for pulse compression. The calculation method of the frequency domain pulse compression reference function is as follows:

[0091] S[p] = DFT{s LFM} = [S[0], S[1], …, S[N - 1]]

[0092] S1[p] = [S[0], 0, S[1], 0, …, S[N - 1], 0]

[0093] S2[p] = [0, S[0], 0, S[1], …, 0, S[N - 1]]

[0094] Among them, S[p] is the DFT transform of the original linear frequency modulation signal time domain sequence, and S1[p] and S2[p], the two-channel frequency domain pulse compression reference functions, are obtained after alternately padding with zeros.

[0095] Step 5: According to the carrier platform motion speed and antenna scanning parameters, perform range-Doppler algorithm processing on the processed echo data, and finally form a clear and non-ambiguous range-azimuth two-dimensional SAR image.

[0096] First, perform range migration correction on the echo data processed in Step 4, and then perform azimuth compression to obtain a clear real target SAR image. Perform point target simulation and distributed surface target scene simulation respectively. During the distributed surface target scene simulation, select a high-resolution SAR image as the ground simulation scene and simulate the distributed scene surface scattering body target. Figure 7 is the simulation result of point target ambiguity resolution imaging; Figure 8 is the imaging result of the surface target scene affected by range ambiguity; Figure 9It is the imaging result after deblurring the scene of the planar target. It can be seen from the imaging result that the range ambiguity can be effectively suppressed and filtered, which proves the effectiveness of the algorithm. It also proves that while adopting the multi-channel technology, effectively utilizing the information resources in the time domain, frequency domain and spatial domain can deblur more effectively and perform high-resolution wide-swath imaging more precisely. It solves the problem of range ambiguity imaging ghosting in actual wide-swath imaging. The imaging result is clear and accurate, making the deblurring algorithm faster, more convenient, more concise and more accurate and effective, and can fundamentally eliminate range ambiguity. It shows that MIMO-SAR will become an important direction for the development and application of future radars.

[0097] Based on the same inventive concept, the present invention also provides a MIMO-SAR deblurring device based on OFDM-chirp signals and DBF processing, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is loaded into the processor, it implements the above-mentioned MIMO-SAR deblurring method based on OFDM-chirp signals and DBF processing.

Claims

1. A MIMO-SAR deambiguation method based on OFDM-chirp signal and DBF processing, characterized in that: The following steps are involved: (1) Waveform and channel design: The linear frequency modulation signal is used as the original signal, which is converted to the frequency domain and then alternately replaced with zeros to form the first OFDM-chirp signal. The spectrum of the first signal is shifted by one frequency domain sampling interval to form the second OFDM-chirp signal. The two signals occupy the odd and even components of the spectrum respectively. The MIMO-SAR channel is designed as a range-elevation array. (2) Construct a three-dimensional signal reception model with range ambiguity: an airborne side-looking synthetic aperture radar is used to obtain multi-channel echoes. The radar operates in a spotlight mode and simultaneously transmits two OFDM-chirp waveforms to the same scatterer target every other pulse interval. The echoes are sampled to obtain three-dimensional echo data of range, azimuth, and array. Each target in the range gate corresponds to two long-range ambiguous targets. (3) Range-elevation DBF deambiguation: Calculate the beam pointing angle corresponding to each MIMO-SAR channel to form a steering vector matrix; The weighted vector matrix is calculated using the guidance vector matrix and multiplied with the three-dimensional echo data to separate and suppress range ambiguity. (4) OFDM demodulation and pulse compression: convert the echo data into the range frequency domain, separate the odd and even frequency components, and then multiply them by two pulse compression reference functions for pulse compression; (5) According to the movement speed of the carrier platform and the antenna scanning parameters, the processed echo data is processed by the range Doppler algorithm to finally form a clear and unambiguous range and azimuth two-dimensional SAR image.

2. The MIMO-SAR deambiguation method based on OFDM-chirp signal and DBF processing according to claim 1, characterized in that: The implementation process of step (1) is as follows: Taking a linear frequency modulation signal with a duty cycle of 25% as the original signal, OFDM modulation is used to alternately replace zeros in the frequency domain with the original linear frequency modulation signal, so that its spectrum width is doubled to form the first OFDM-chirp signal. Then, the spectrum of the OFDM-chirp signal is shifted by one frequency domain sampling interval to obtain the second OFDM-chirp signal. The time domain expressions of the original LFM signal and the two OFDM-chirp signals are: Among them, s LFM is the original linear frequency modulation signal, s OFDM1 and s OFDM2 They are two-way OFDM-chirp waveforms, t r is the distance time, T p is the pulse width, k r is the frequency modulation slope, n r is the length of the original linear frequency modulation signal sequence, T s is the sampling interval, f s is the sampling frequency; the MIMO-SAR multi-channel is designed as a 4×1 array in the range-elevation direction, with the channel pointing perpendicular to the track and perpendicular to the start of the main mapping swath scene, and the pitch angle is: Among them, θ in It represents the pitch angle of the channel, h0 is the height of the radar platform, and R0 is the slant distance from the start of the main surveying band to the radar platform.

3. The MIMO-SAR deambiguation method based on OFDM-chirp signal and DBF processing according to claim 1, characterized in that: The implementation process of step (2) is as follows: First, the scene is set: the main mapping band range where the original LFM signal and OFDM-chirp signal can be clearly imaged is where n r is the length of the original linear frequency modulation signal sequence, c is the speed of light, f s The sampling frequency is , and the target outside the main mapping range loses information due to the incomplete waveform of the echo signal it receives, which eventually appears as a blurred SAR image. Therefore, when setting the scene, a sub-mapping band is set before and after the main mapping band, and the range size is the same as the main mapping band, so that the range size of the simulation scene is Then the signal received at each azimuth sampling point is composed of the sum of two fuzzy echoes and one target echo; Then, an echo simulation is performed: every other pulse repetition interval, two transmitting channels simultaneously transmit two OFDM-chirp waveforms, which illuminate all scene targets and return echoes. The four receiving channels simultaneously receive all echoes to form four "range-azimuth" two-dimensional echo data. The echo data of these four channels are stacked together in sequence to form a "range-azimuth-array" three-dimensional data matrix.

4. The MIMO-SAR deambiguation method based on OFDM-chirp signal and DBF processing according to claim 1, characterized in that: The implementation process of step (3) is as follows: The distance index of the main survey swath is: D r =R0+(0:n r -1)×l rg Among them, R0 is the slant distance from the start of the main surveying band to the radar platform, n r is the length of the original linear frequency modulation signal sequence, is the distance gate length, c is the speed of light, f s is the sampling frequency; the time domain expression of the echo received by a single channel is as follows: Among them, echo i Indicates single channel echo, i=1,2,3,4, t r is the distance time, pt n Point target echo within the main surveying band, pt a1n For near-field sub-mapping with point target echo, pt a2n is the echo of the far-field sub-mapping point target, λ is the wavelength, and are the distance indices of the near-field and far-field sub-swaths, respectively, d i =(i-1)W rs , W rs is the channel spacing, θ1, θ2, and θ3 are the beam pointing angles. The beam pointing angles are calculated as follows: Where h0 is the radar platform height, m=0,1,2,…,2n r -1, R0 is the slant distance from the start of the main survey swath to the radar platform, n r is the length of the original linear frequency modulation signal sequence, is the distance gate length, c is the speed of light, f s is the sampling frequency, θ in Indicates the pitch tilt angle of the channel; the received data of the four channels are formed into a matrix as follows: V is the steering vector matrix, which is expressed as follows: In order to separate and suppress blur, the weighted vector matrix is calculated using the steering vector matrix as follows: W=(V H V) -1 V H Finally, the echo matrix is multiplied by the weighted vector matrix to separate the ambiguity. The calculation is as follows: The first row of data obtained after matrix multiplication is the echo of the target within the unambiguous imaging range.

5. The MIMO-SAR deambiguation method based on OFDM-chirp signal and DBF processing according to claim 1, characterized in that: The implementation process of step (4) is as follows: The data matrix obtained after defuzzification is subjected to range-direction FFT. To separate the two OFDM-chirp waveforms, the even and odd spectra are separated to form two sets of echo data. These are then multiplied by the corresponding two pulse compression reference functions in the frequency domain for pulse compression. The frequency domain pulse compression reference function is calculated as follows: S[p]=DFT{s LFM }=[S[0],S[1],…,S[N-1]] S1[p]=[S[0],0,S[1],0,…,S[N-1],0] S2[p]=[0,S[0],0,S[1],…,0,S[N-1]] Among them, S[p] is the DFT transform of the original linear frequency modulation signal time domain sequence, and after alternating with zero, we get two frequency domain pulse pressure reference functions S1[p] and S2[p], respectively. LFM is the time domain of the original LFM signal.

6. A MIMO-SAR deambiguation device based on OFDM-chirp signals and DBF processing, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is loaded into a processor, the MIMO-SAR deambiguation method based on OFDM-chirp signal and DBF processing is implemented according to any one of claims 1 to 5.

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