Distributed sar interferometric baseline estimation method, apparatus, system, device and medium

Abstract

The embodiment of the application discloses a kind of distributed SAR interferometric baseline estimation method and, device, system, equipment and medium, the method includes: to the main star imaging azimuth time axis and auxiliary star imaging azimuth time axis are synchronized, obtain the synchronized main star imaging azimuth time axis and the synchronized auxiliary star imaging azimuth time axis;Determine the state vector of main star azimuth time and the state vector of auxiliary star azimuth time based on the synchronized main star imaging azimuth time axis and the synchronized auxiliary star imaging azimuth time axis;The state vector of main star azimuth time and the state vector of auxiliary star azimuth time are sampled, and obtain the sampled main star state vector and the sampled auxiliary star state vector;Determine the baseline vector of main star and auxiliary star based on the sampled main star state vector and the sampled auxiliary star state vector.

Description

Technical Field

[0001] This application relates to, but is not limited to, the field of radar technology, and in particular to a distributed SAR interferometric baseline estimation method, apparatus, system, device, and medium. Background Technology

[0002] Distributed Synthetic Aperture Radar (SAR) is a distributed microwave imaging system for Earth observation consisting of multiple transmitters and receivers located on a separate platform. Distributed SAR systems can utilize complex SAR images and Interferometric Synthetic Aperture Radar (InSAR) technology to acquire information such as surface elevation and surface deformation along the radar's line-of-sight. Interferometric baseline estimation methods are mainly divided into image-based estimation methods and external data-based estimation methods. Both methods require high-quality complex SAR images or additional reference data. Their estimation accuracy is easily affected by system noise, ambiguity, and other factors, and the processing is complex, computationally intensive, and inefficient. Summary of the Invention

[0003] In view of this, this application provides a distributed SAR interferometric baseline estimation method, apparatus, system, device, and medium to address the problems existing in the prior art.

[0004] The technical solution of this application embodiment is implemented as follows:

[0005] On one hand, embodiments of this application provide a distributed SAR interferometric baseline estimation method, comprising: synchronizing the imaging azimuth time axis of the primary star and the imaging azimuth time axis of the secondary star to obtain synchronized primary star imaging azimuth time axis and synchronized secondary star imaging azimuth time axis; determining the state vector of the primary star azimuth time and the state vector of the secondary star azimuth time based on the synchronized primary star imaging azimuth time axis and the synchronized secondary star imaging azimuth time axis; sampling the state vector of the primary star azimuth time and the state vector of the secondary star azimuth time to obtain sampled primary star state vector and sampled secondary star state vector; and determining the baseline vectors of the primary star and the secondary star based on the sampled primary star state vector and the sampled secondary star state vector.

[0006] In some embodiments, synchronizing the imaging azimuth time axis of the primary star and the imaging azimuth time axis of the secondary star to obtain synchronized primary star imaging azimuth time axis and synchronized secondary star imaging azimuth time axis includes: determining the imaging azimuth time axis of the primary star and the imaging azimuth time axis of any one of the multiple secondary stars; acquiring synchronization data of the primary star and the secondary star; determining the time deviation between the imaging azimuth time axis of the primary star and the imaging azimuth time axis of the secondary star based on the synchronization data; shifting the imaging azimuth time axis of the secondary star by the time deviation to obtain the synchronized secondary star imaging azimuth time axis; and determining the imaging azimuth time axis of the primary star as the synchronized primary star imaging azimuth time axis.

[0007] In some embodiments, determining the state vectors of the primary star's azimuth time and the secondary star's azimuth time based on the synchronized primary star imaging azimuth time axis and the synchronized secondary star imaging azimuth time axis includes: determining the second-interval sampling time, first position vector, and first velocity vector of the primary star, and the second-interval sampling time, second position vector, and second velocity vector of the secondary star; and inputting the synchronized primary star imaging azimuth time axis, the synchronized secondary star imaging azimuth time axis, the second-interval sampling time of the primary star, the first position vector, the first velocity vector, and the second-interval sampling time, the second position vector, and the second velocity vector of the secondary star into a high-precision satellite orbital propagator to obtain the state vectors of the primary star's azimuth time and the secondary star's azimuth time.

[0008] In some embodiments, sampling the state vector of the primary star's azimuth time and the state vector of the secondary star's azimuth time to obtain a sampled primary star state vector and a sampled secondary star state vector includes: sampling the state vector of the primary star's azimuth time based on the primary star's imaging azimuth time axis and a preset interval to obtain a sampled primary star imaging azimuth time axis and the sampled primary star state vector; determining the state vector of the secondary star's azimuth time that is at the same time as the sampled primary star imaging azimuth time axis to obtain the sampled secondary star state vector.

[0009] In some embodiments, sampling the state vector of the primary star's azimuth time based on the primary star's imaging azimuth time axis and a preset interval to obtain the sampled primary star's imaging azimuth time axis and the sampled primary star's state vector includes: sampling the state vector of the primary star's azimuth time on a first coordinate axis, a second coordinate axis, and a third coordinate axis based on the primary star's imaging azimuth time axis and the preset interval, respectively, to obtain the sampled primary star's imaging azimuth time axis, a first component of the primary star sampling, a second component of the primary star sampling, and a third component of the primary star sampling; and determining the sampled primary star's state vector based on the first component of the primary star sampling, the second component of the primary star sampling, and the third component of the primary star sampling.

[0010] In some embodiments, determining the state vector of the auxiliary star at the same time as the sampled primary star imaging azimuth time axis in the state vector of the auxiliary star azimuth time includes: determining the state vector at the same time in the state vector of the auxiliary star azimuth time on the first coordinate axis, the second coordinate axis, and the third coordinate axis, respectively, based on the sampled primary star imaging azimuth time axis, to obtain the first component, the second component, and the third component of the auxiliary star sampling; and determining the sampled auxiliary star state vector based on the first component, the second component, and the third component of the auxiliary star sampling.

[0011] In some embodiments, determining the baseline vectors of the primary and secondary stars based on the sampled primary star state vector and the sampled secondary star state vector includes: acquiring the scene center slant range, Doppler center frequency, and imaging parameters; obtaining the position vector of the observation target point through range-Doppler RD positioning based on the sampled primary star state vector, the scene center slant range, the Doppler center frequency, and the imaging parameters; determining the slant range length between the secondary star and the observation target point based on the position vector of the observation target point and the sampled secondary star state vector; and determining the baseline vector based on the slant range length.

[0012] In some embodiments, determining the slant distance between the auxiliary star and the observation target point based on the position vector of the observation target point and the sampled auxiliary star state vector includes: determining the first component, second component, and third component of the auxiliary star's position vector in the sampled auxiliary star state vector on the first coordinate axis, second coordinate axis, and third coordinate axis, respectively; determining the first component, second component, and third component of the observation target point's position vector on the first coordinate axis, second coordinate axis, and third coordinate axis, respectively; squaring the difference between the first component of the auxiliary star and the first component of the observation target point to obtain a first value; squaring the difference between the second component of the auxiliary star and the second component of the observation target point to obtain a second value; squaring the difference between the third component of the auxiliary star and the third component of the observation target point to obtain a third value; and summing the first value, the second value, and the third value and taking the square root to obtain the slant distance.

[0013] In some embodiments, determining the baseline vector based on the slant range length includes: determining the slant range difference between the primary star and the secondary star based on the slant range length and the slant range at the scene center; determining the reception time of the secondary star receiving the scattered echo from the observed target point based on the slant range difference; and determining the baseline vector based on the reception time.

[0014] In some embodiments, determining the slant range difference between the primary star and the secondary star based on the slant range length and the slant range at the scene center includes: subtracting the slant range length from the slant range range to obtain the slant range difference; determining the reception time of the secondary star receiving the scattered echo from the observation target point based on the slant range difference includes: dividing the slant range difference by the speed of light and summing it with the time in the sampled secondary star state vector to obtain the reception time.

[0015] In some embodiments, determining the baseline vector based on the reception time includes: obtaining the reception position vector of the auxiliary star receiving the scattered echo from the observation target point by interpolation based on the state vector of the reception time and the azimuth time of the auxiliary star; and determining the baseline vector based on the reception position vector and the sampled state vector of the primary star.

[0016] In some embodiments, obtaining the receiving position vector of the auxiliary star receiving the scattered echo from the observation target point by interpolation based on the receiving time and the state vector of the auxiliary star's azimuth time includes: interpolating the position vector in the state vector of the auxiliary star's azimuth time along the first coordinate axis, the second coordinate axis, and the third coordinate axis, respectively, based on the receiving time, to obtain a first position interpolation, a second position interpolation, and a third position interpolation; and obtaining the receiving position vector based on the first position interpolation, the second position interpolation, and the third position interpolation.

[0017] In some embodiments, determining the baseline vector based on the received position vector and the sampled primary star state vector includes: taking the difference between the received position vector and the position vector in the sampled primary star state vector to obtain the baseline vector.

[0018] In some embodiments, after determining the baseline vectors of the primary and secondary stars based on the sampled primary star state vector and the sampled secondary star state vector, the method further includes: determining a coordinate system with the primary star as a reference based on the sampled primary star state vector; determining three coordinate components of the baseline vector in the three coordinate axes of the coordinate system; fitting quadratic coefficients to the three coordinate components to obtain constant and linear coefficients; and outputting the constant and linear coefficients to a target file.

[0019] On the other hand, embodiments of this application provide a distributed SAR interferometric baseline estimation device, the device comprising: a synchronization module for synchronizing the imaging azimuth time axis of a primary star and the imaging azimuth time axis of a secondary star to obtain synchronized primary star imaging azimuth time axis and synchronized secondary star imaging azimuth time axis; a first determination module for determining the state vector of the primary star azimuth time and the state vector of the secondary star azimuth time based on the synchronized primary star imaging azimuth time axis and the synchronized secondary star imaging azimuth time axis; a sampling module for sampling the state vector of the primary star azimuth time and the state vector of the secondary star azimuth time to obtain sampled primary star state vector and sampled secondary star state vector; and a second determination module for determining the baseline vectors of the primary star and the secondary star based on the sampled primary star state vector and the sampled secondary star state vector.

[0020] In another aspect, embodiments of this application provide a distributed SAR system, the system including a primary satellite and a secondary satellite; the primary satellite transmits radar signals to illuminate a target area; the primary satellite and the secondary satellite receive echo signals scattered from the target area; the primary satellite and the secondary satellite alternately transmit synchronization signals during idle time outside the time window for receiving the echo signals; the primary satellite and the secondary satellite respectively send synchronization data corresponding to the synchronization signals to a computer at a ground station.

[0021] In another aspect, embodiments of this application provide a computer device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the program to implement some or all of the steps in the above-described method.

[0022] In another aspect, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements some or all of the steps in the above-described method.

[0023] In this embodiment, the imaging azimuth time axis of the primary star and the imaging azimuth time axis of the secondary star are synchronized to obtain synchronized primary star imaging azimuth time axes and secondary star imaging azimuth time axes. Based on the synchronized primary star imaging azimuth time axes and secondary star imaging azimuth time axes, the state vectors of the primary star and secondary star azimuth time are determined. Then, the sampled primary star state vector and sampled secondary star state vector are obtained through sampling, thereby determining the baseline vectors of the primary and secondary stars. This achieves accurate estimation of the on-orbit bistatic SAR interferometric baseline and realizes the generation of a high-precision digital elevation model (DEM) for distributed SAR. The processing is simple, computationally inexpensive, and highly efficient, improving the efficiency and accuracy of distributed SAR interferometric baseline estimation. Attached Figure Description

[0024] In the accompanying drawings (which are not necessarily drawn to scale), similar reference numerals may describe similar parts in different views. Similar reference numerals with different letter suffixes may indicate different examples of similar parts. The drawings generally illustrate the various embodiments discussed in this application by way of example and not limitation.

[0025] Figure 1 A schematic diagram illustrating the implementation process of a distributed SAR interferometric baseline estimation method provided in this application embodiment;

[0026] Figure 2 This is a schematic diagram of a spaceborne distributed SAR imaging system with one transmitter and multiple receivers, provided as an embodiment of this application.

[0027] Figure 3 A schematic diagram of a spaceborne bistatic SAR interferometric stripe pattern imaging provided in this application embodiment;

[0028] Figure 4 A distributed SAR interferometric baseline estimation procedure is provided for embodiments of this application;

[0029] Figure 5 A geometric model for distance-Doppler RD localization is provided for embodiments of this application;

[0030] Figure 6 A distribution map of equidistant lines and equal Doppler lines provided for embodiments of this application;

[0031] Figure 7 A digital elevation model 1 for inversion provided in this application embodiment;

[0032] Figure 8 An SRTM digital elevation model 1 is provided as a reference for embodiments of this application;

[0033] Figure 9 An error 1 in a digital elevation model provided in this application embodiment;

[0034] Figure 10 A statistical distribution of error in a digital elevation model provided in this application embodiment 1;

[0035] Figure 11 A digital elevation model 2 for inversion provided in this application embodiment;

[0036] Figure 12 An SRTM digital elevation model 2 is provided as a reference for embodiments of this application;

[0037] Figure 13 An error 2 in a digital elevation model provided in this application embodiment;

[0038] Figure 14A statistical distribution of error in a digital elevation model provided in this application embodiment 2;

[0039] Figure 15 This is a schematic diagram of the composition structure of a distributed SAR interferometric baseline estimation device provided in an embodiment of this application;

[0040] Figure 16 This is a schematic diagram of the hardware entity of a computer device provided in an embodiment of this application. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application are further described in detail below with reference to the accompanying drawings and embodiments. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0042] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0043] The terms “first / second / third” are used merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that “first / second / third” may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0044] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this application is for descriptive purposes only and is not intended to be limiting.

[0045] Based on this, the embodiments of this application provide a distributed SAR interferometric baseline estimation method, which can accurately estimate the on-orbit bistatic SAR interferometric baseline and realize the generation of high-precision distributed SAR DEM. Figure 1 This is a schematic diagram illustrating the implementation process of a distributed SAR interferometric baseline estimation method provided in an embodiment of this application, as shown below. Figure 1 As shown, the method includes the following steps S101 to S104:

[0046] Step S101: Synchronize the imaging azimuth time axis of the primary star and the imaging azimuth time axis of the secondary star to obtain a synchronized imaging azimuth time axis of the primary star and a synchronized imaging azimuth time axis of the secondary star.

[0047] Here, there is one primary star and multiple secondary stars. The imaging azimuth time axis of the secondary stars is any one of the imaging azimuth time axes of the multiple secondary stars. By synchronizing the hypothetical imaging azimuth time axis of the primary star and the imaging azimuth time axis of the secondary stars in time, the synchronized imaging azimuth time axis of the primary star and the imaging azimuth time axis of the secondary stars are obtained.

[0048] For example, the time axis of the synchronized auxiliary star is represented by formula (1), as follows:

[0049]

[0050] The time axis of the synchronized primary star's position is represented by formula (2), as follows:

[0051]

[0052] Step S102: Based on the synchronized primary star imaging azimuth time axis and the synchronized secondary star imaging azimuth time axis, determine the state vector of the primary star azimuth time and the state vector of the secondary star azimuth time.

[0053] Here, the state vector of the primary star's azimuth time includes the time, position vector, and velocity vector sampled at second intervals; the state vector of the secondary star's azimuth time also includes the time vector, position vector, and velocity vector. By synchronizing the primary star's imaging azimuth time axis and the secondary star's imaging azimuth time axis, the corresponding state vectors of the primary star's azimuth time and the secondary star's azimuth time can be assumed.

[0054] For example, assume the sampling time of the primary star is T seconds. M The position vector is P M And the velocity vector is V M And the sampling time of the auxiliary star is T seconds. S The position vector is P S And the velocity vector is V S .

[0055] Step S103: Sample the state vector of the primary star's azimuth and time and the state vector of the secondary star's azimuth and time to obtain the sampled state vector of the primary star and the sampled state vector of the secondary star.

[0056] Here, the sampled primary star state includes the sampled primary star azimuth time axis, the sampled position vector, and the sampled velocity vector; the sampled secondary star state vector includes the sampled secondary star azimuth time axis, the sampled position vector, and the sampled velocity vector. By sampling the primary star azimuth time state vector and the secondary star azimuth time state vector, the sampled primary star state vector and the sampled secondary star state vector are obtained.

[0057] For example, the sampling of the primary star's azimuth time axis and state vector are represented by formulas (3) to (5), as follows:

[0058]

[0059]

[0060]

[0061] The sampled auxiliary star state vectors are represented by formulas (6) to (8), as follows:

[0062]

[0063]

[0064]

[0065] Step S104: Based on the sampled primary star state vector and the sampled secondary star state vector, determine the baseline vectors of the primary star and the secondary star.

[0066] Here, the baseline vector is a set of orthogonal basis vectors in a new coordinate system established using fundamental facts. The baseline vector allows for accurate estimation of the on-orbit bistatic SAR interferometric baseline. The baseline vectors of the primary and secondary satellites can be determined by calculating the sampled state vectors of the primary and secondary satellites.

[0067] For example, the baseline vectors of the primary and secondary stars are represented by formula (9), as follows:

[0068]

[0069] In this embodiment, the state vectors of the primary and secondary satellites are determined by synchronizing the imaging azimuth and time axes of the primary and secondary satellites. Then, the baseline vectors of the primary and secondary satellites are further determined through sampling and calculation. This enables accurate estimation of the on-orbit bistatic SAR interferometric baseline, generating a high-precision distributed SAR DEM, and improving the efficiency and accuracy of distributed SAR interferometric baseline estimation.

[0070] In some embodiments, step S101 described above can be implemented through the following steps:

[0071] The first step is to determine the imaging azimuth and time axis of the primary star and the imaging azimuth and time axis of any one of the auxiliary stars.

[0072] Here, the imaging azimuth and time axis of the primary star and the imaging azimuth and time axis of any one of the multiple auxiliary stars can be determined by assumption.

[0073] For example, suppose the imaging azimuth time axis of the primary star and the imaging azimuth time axis of any one of the auxiliary stars are represented by formula (10), as follows:

[0074] t M,i =t M,0 +(i-1)·PRT M (10)

[0075] t S,j =t S,0 +(j-1)·PRT S (11)

[0076] In this context, the subscripts M and S represent the primary star and the secondary star, respectively, and t M,0 The time t represents the start time of imaging the primary star. S The start time of the secondary star imaging, i∈{1,2,…,N} M} represents the pulse repetition time (PRT) of the primary star imaging, j∈{1,2,…,N} S} represents the j-th PRT of the secondary star imaging, PRT M and PRT S The PRT represents the primary and secondary stars, and the PRT M ≈PRT S =PRT.

[0077] The second step is to obtain synchronization data between the primary and secondary stars.

[0078] The third step is to determine the time deviation between the imaging azimuth and time axis of the primary star and the imaging azimuth and time axis of the secondary star, based on the synchronization data.

[0079] Here, the time deviation is the time difference between the imaging azimuth time axis of the primary star and the imaging azimuth time axis of the secondary star. Synchronization data of events of the primary and secondary stars can be obtained through existing inter-satellite radar synchronization links, and the time deviation between the imaging azimuth time axes of the primary and secondary stars can be determined based on this synchronization data.

[0080] In some possible implementations, the primary satellite transmits radar signals to illuminate the target area, and two satellites receive the scattered echo signals. Simultaneously, the two satellites exchange synchronization pulses during idle periods outside the radar echo reception window, without affecting the normal operation of the radar. Assume the primary satellite transmits a synchronization signal during the idle period of the first PRT, and the secondary satellite receives this synchronization signal; secondly, the secondary satellite transmits a synchronization signal to the primary satellite during the second PRT; then, the primary and secondary satellites alternately transmit synchronization signals. Finally, the synchronization data from the primary and secondary satellites is transmitted to the ground station, and matched filtering is performed on the synchronization data in the computer to obtain compressed pulse peaks. Then, the peak position time of the secondary satellite's synchronization signal is subtracted from the peak position time of the primary satellite and divided by two to obtain their time deviation Δt = t. S,0 -t M,0 .

[0081] The fourth step is to offset the time deviation of the auxiliary star imaging azimuth time axis to obtain a synchronized auxiliary star imaging azimuth time axis.

[0082] Here, by shifting the azimuth and time axis of the secondary star imaging to the right by a time deviation, the azimuth and time axis of the secondary star imaging is obtained and synchronized with the azimuth and time axis of the primary star imaging.

[0083] For example, the time axis of the synchronized auxiliary star is represented by formula (1), as follows:

[0084]

[0085] The fifth step is to determine that the imaging azimuth and time axis of the primary star is synchronized with the imaging azimuth and time axis of the primary star.

[0086] Here, the imaging azimuth time axis of the primary star is directly determined to be the imaging azimuth time axis of the primary star, which is synchronized with the imaging azimuth time axis of the secondary star.

[0087] For example, the time axis of the synchronized primary star's azimuth is represented by formula (2), as follows:

[0088]

[0089] In this embodiment, by assuming the imaging azimuth time axis of the primary star and the imaging azimuth time axis of any one of the auxiliary stars, the imaging azimuth time axis of the auxiliary stars is shifted based on the time deviation between the two to obtain a synchronized imaging azimuth time axis for the auxiliary stars, and the imaging azimuth time axis of the primary star is determined as the synchronized imaging azimuth time axis of the primary star. Thus, using the imaging azimuth time axis of the primary star as the primary factor to determine the synchronized imaging azimuth time axis of the primary star and the imaging azimuth time axis of the auxiliary stars is simple to process, has low computational load, and high computational efficiency, improving the efficiency and accuracy of distributed SAR interferometric baseline estimation.

[0090] In some embodiments, step S102 described above can be implemented through the following steps:

[0091] The first step is to determine the sampling time per second of the primary star, the first position vector, the first velocity vector, and the sampling time per second of the secondary star, the second position vector, and the second velocity vector.

[0092] Here, the first position vector and the first velocity vector are the position and velocity vectors of the primary star; the second position vector and the second velocity vector are the position and velocity vectors of the secondary star. The sampling time per second, the first position vector, and the first velocity vector of the primary star, as well as the sampling time per second, the second position vector, and the second velocity vector of the secondary star, are determined.

[0093] For example, assume the sampling time of the primary star is T seconds. M The position vector is P M And the velocity vector is V M And the sampling time of the auxiliary star is T seconds. S The position vector is P S And the velocity vector is V S The sampling intervals between the primary and secondary stars are expressed by formulas (12) and (13), respectively, as follows:

[0094] T M =[T M,1 ,…,T M,p ,…,T M,P (12)

[0095] T S =[T S,1 ,…,T S,a ,…,T S,A (13)

[0096] Among them, T M,p -T M,p-1 =1,T S,a -T S,a-1 =1, and The position and velocity vectors of the primary star are described by formulas (14) and (15), as follows:

[0097]

[0098]

[0099] Similarly, the position and velocity vectors of the auxiliary star are expressed by formulas (18) and (19), as follows:

[0100]

[0101]

[0102] The second step involves transmitting the synchronized primary satellite imaging azimuth time axis, the synchronized secondary satellite imaging azimuth time axis, the primary satellite's second-interval sampling time, the first position vector, the first velocity vector, and the secondary satellite's second-interval sampling time, the second position vector, and the second velocity vector into a high-precision satellite orbital transmitter to obtain the primary satellite's azimuth time state vector and the secondary satellite's azimuth time state vector.

[0103] Here, the azimuth time state vectors of the primary and secondary satellites include time, position, and velocity vectors. By inputting the time, position, and velocity vectors sampled at second intervals from the primary satellite and the secondary satellite into a high-precision satellite orbit transmitter, the time, position, and velocity vectors of the primary satellite's azimuth time, as well as those of the secondary satellite's azimuth time, can be obtained. The time, position, and velocity vectors of the primary satellite's azimuth time are then determined to be the state vectors of the primary satellite's azimuth time, and the time, position, and velocity vectors of the secondary satellite's azimuth time are determined to be the state vectors of the secondary satellite's azimuth time.

[0104] For example, the state vector of the primary star's azimuth time is represented by formulas (18) to (20), as follows:

[0105]

[0106]

[0107]

[0108] The state vector of the auxiliary star's azimuth time is expressed by formulas (21) to (23), as follows:

[0109]

[0110]

[0111]

[0112] In this embodiment, the time, position vector, and velocity vector of the primary satellite and the secondary satellite are determined at second-interval sampling intervals. These are then combined with the synchronized primary satellite imaging azimuth time axis and the synchronized secondary satellite imaging azimuth time axis input to a high-precision satellite orbit propagator to obtain the state vectors of the primary satellite's azimuth time and the secondary satellite's azimuth time. This simplifies the data processing, reduces computational complexity, and increases efficiency, thereby improving the efficiency and accuracy of distributed SAR interferometric baseline estimation.

[0113] In some embodiments, step S103 described above can be implemented through the following steps:

[0114] The first step is to sample the state vector of the primary star's azimuth time based on the primary star's imaging azimuth time axis and a preset interval, so as to obtain the sampled primary star imaging azimuth time axis and the sampled primary star state vector.

[0115] Here, the preset interval refers to sampling every N intervals along the primary star imaging azimuth time axis. A smaller preset interval results in a larger number of sampled state vectors, leading to higher estimation accuracy; a larger preset interval results in higher estimation efficiency and is more time-saving. Typically, N is chosen such that the interval between two sampled state vectors is 1 second (s), i.e., N*PRT = 1s. Sampling is performed every N intervals along the primary star imaging azimuth time axis for both the primary and secondary stars, yielding sampled primary star imaging azimuth time axes and sampled primary star state vectors.

[0116] For example, the sampling of the primary star's azimuth time axis and state vector are represented by formulas (3) to (5), as follows:

[0117]

[0118]

[0119]

[0120] The second step is to determine the state vector of the auxiliary star at the same time as the sampled primary star imaging azimuth time axis in the state vector of the auxiliary star's azimuth time, and obtain the sampled auxiliary star's state vector.

[0121] Here, the state vector of the auxiliary star's azimuth time is sampled based on the process of sampling the state vector of the primary star's azimuth time. The state vectors at the same time in the auxiliary star's azimuth time state vector are determined based on the obtained sampled primary star's azimuth time axis, and these state vectors at the same time are identified as the sampled auxiliary star state vectors.

[0122] For example, the sampled auxiliary star state vector is represented by formulas (6) to (8), as follows:

[0123]

[0124]

[0125]

[0126] In this embodiment, the state vector of the primary star's azimuth time is sampled according to the primary star's imaging azimuth time axis and a preset interval to obtain the sampled primary star state vector. Then, based on the sampled primary star's imaging azimuth time axis, the sampled auxiliary star state vector is obtained. In this way, the sampled primary and auxiliary star state vectors are obtained by sampling the auxiliary star using the primary star's sampling method. The sampling process is simple, the computational load is small, and the quantity of sampled primary and auxiliary star state vectors is guaranteed, thereby improving the accuracy of distributed SAR interferometric baseline estimation.

[0127] In some embodiments, the sampled primary star imaging azimuth time axis and the sampled primary star state vector can be obtained through the following steps:

[0128] The first step involves sampling the state vector of the primary star's azimuth time on the first, second, and third coordinate axes, based on the primary star's imaging azimuth time axis and a preset interval, to obtain the sampled primary star imaging azimuth time axis, the first component of the primary star sampling, the second component of the primary star sampling, and the third component of the primary star sampling.

[0129] Here, the first coordinate axis corresponds to the X-axis of the Earth-fixed coordinate system, the second coordinate axis corresponds to the Y-axis of the Earth-fixed coordinate system, and the third coordinate axis corresponds to the Z-axis of the Earth-fixed coordinate system. Based on the primary star imaging azimuth time axis and the preset interval, the state vector of the primary star's azimuth time is sampled on the three coordinate axes respectively, resulting in the sampled primary star imaging azimuth time axis, the first component on the X-axis, the second component on the Y-axis, and the third component on the Z-axis.

[0130] The second step is to determine the sampled primary star state vector based on the first component, the second component, and the third component of the primary star sampling.

[0131] Here, the sampled primary star state vector is determined based on the first component, the second component, and the third component of the primary star sampling.

[0132] In some embodiments, the sampled auxiliary star state vector can be obtained through the following steps:

[0133] The first step is to determine the state vectors at the same time in the state vectors of the secondary star's azimuth time on the first, second, and third coordinate axes, based on the sampling primary star's imaging azimuth time axis, and obtain the first component, second component, and third component of the secondary star sampling.

[0134] The second step involves defining the first coordinate axis as the X-axis of the Earth-fixed coordinate system, the second coordinate axis as the Y-axis, and the third coordinate axis as the Z-axis. Based on the sampled primary star imaging azimuth time axis, the three components of the state vector at the same moment in the secondary star's azimuth time state vector are determined on the three coordinate axes.

[0135] The third step is to determine the sampled auxiliary star state vector based on the first component, the second component, and the third component of the auxiliary star sampling.

[0136] Here, the sampled auxiliary star state vector is determined based on the first component, the second component, and the third component of the auxiliary star sampling.

[0137] In some embodiments, step S104 above can be implemented by the following steps:

[0138] The first step is to obtain the scene center slant distance, Doppler center frequency, and imaging parameters.

[0139] Here, existing data such as scene center slant distance, Doppler center frequency, and imaging parameters are directly obtained.

[0140] The second step involves obtaining the position vector of the observed target point through range-Doppler RD positioning based on the sampled primary star state vector, scene center slant distance, Doppler center frequency, and imaging parameters.

[0141] Here, based on the sampled primary star state vector, scene center slant distance, Doppler center frequency, and imaging parameters, the position vector of the observed target point is estimated using the range-Doppler RD positioning method.

[0142] In some possible implementations, obtaining the position vector of the observed target point using the range-Doppler RD localization method includes:

[0143] First, a Cartesian coordinate system is established with the SAR nadir point as the origin of the coordinate system, the x-axis along the track direction (azimuth direction) as the x-axis, and the y-axis perpendicular to the track direction (range direction) as the y-axis.

[0144] Assuming the coordinates of the ground target point are (x, y, 0), the slant range between the SAR satellite and the target point is R, the angle between the satellite's motion direction and the SAR line-of-sight direction is γ, the satellite's orbital altitude is h, and the satellite's velocity is V, then we can obtain equation (24) as follows:

[0145] x 2 +y 2 =R 2 -h 2 (twenty four)

[0146] A SAR (Special Radar Array) at a certain flight path position observes ground targets. Multiple target points equidistant from the SAR antenna's phase center can form concentric circles centered on a nadir point. Target points on the same circle are equidistant from the nadir point. In other words, the SAR is equidistant from target points on the same circle. Therefore, target points on different concentric circles can be distinguished based on the echo signal delay.

[0147] Assume the SAR signal carrier frequency is f c Given the speed of light as c and the wavelength as λ, the frequency of the radar echo signal is given by formula (25), as follows:

[0148]

[0149] in, This represents the Doppler center frequency. Furthermore, the radial velocity of the SAR relative to the target point can be expressed by formula (26), as follows:

[0150] V r =Vcosγ (26)

[0151] in, Furthermore, it can be transformed into equation (27), as follows:

[0152] (x 2 +y 2 +h 2 cos 2 γ=x 2 (27)

[0153] Furthermore, by transforming the above equation, we obtain the hyperbola equation formula (28), as follows:

[0154]

[0155] The above equations can describe the equal Doppler frequency distribution in the observation area. Within the SAR beam range, targets containing the same Doppler frequency have different time delays, and targets with the same time delay contain different Doppler frequencies. Based on this, precise target localization can be achieved.

[0156] In target positioning, the ellipsoid equation is usually used to describe the Earth model, which can be expressed as formula (29), as follows:

[0157]

[0158] Among them, R e R is the average equatorial radius. p =(1-1 / f)R e The polar radius and f = 298.255 represent the flatness factor.

[0159] In target localization, the slant range from the phase center of the SAR antenna to the observed target point can be expressed by formula (30), as follows:

[0160]

[0161] in, For satellite position vectors, Let be the target position vector, c be the speed of light, and τ be the echo delay of the target.

[0162] In target localization, the Doppler center frequency f of the target's scattered echo is... dc This can be expressed as formula (31), as follows:

[0163]

[0164] Where λ is the radar wavelength, and This represents the velocity vector of the SAR satellite relative to the target point. Furthermore, solving the above three equations yields the position vector of the observed target.

[0165] Assume that the position vectors of the SAR satellite and the observed target point in the geocentric coordinate system are respectively and Then we can obtain equations (32) and (33) as follows:

[0166] R 2 =(R sx -x) 2 +(R sy -y) 2 +(R sz -z) 2 (32)

[0167] R 2 =R sx 2 +x 2 +R sy 2 +y 2 +R sz 2 +z 2 -2R sx x-2R sy y-2R sz z (33)

[0168] Based on the equation of the ellipsoid, we can obtain equation (34) as follows:

[0169]

[0170] Furthermore, based on the Doppler equation, we can obtain equation formula (35) as follows:

[0171]

[0172] Then, the above three equations can be combined into a system of three equations, formula (36), as follows:

[0173]

[0174] This system of equations consists of three nonlinear equations. Here, Newton's iterative method is used to estimate the approximate solution of this system of equations, and its main iterative process is described in equation (37) as follows:

[0175] X k+1 =X k -B -1 f(X k (37)

[0176] X = [xyz] T , f(X)=[f1(x,y,z) f2(x,y,z) f3(x,y,z)] T B -1 Represents the Jacobi matrix

[0177] The inverse matrix of the k-th iteration is expressed as formula (38), as follows:

[0178]

[0179] Finally, the iterative process for determining the target location is summarized as follows:

[0180] 1) Based on the satellite state vector, calculate the satellite's position vector and velocity vector in the orbital coordinate system;

[0181] 2) Transform the position and velocity vectors in the orbital coordinate system to their corresponding vectors in the geocentric coordinate system;

[0182] 3) Calculate the Jacobi matrix;

[0183] 4) Set the initial value and threshold for the iteration, and calculate X. k+1 -X k Check if it is less than a threshold; if it is, stop the calculation; otherwise, use the X obtained in the previous step. k+1 Used as initial values ​​for iterative calculations;

[0184] 5) Output the position vector of the observed target in the Earth-centered Earth-fixed coordinate system.

[0185] The third step is to determine the slant distance between the auxiliary star and the observation target point based on the position vector of the observation target point and the sampled auxiliary star state vector.

[0186] Here, the slant distance between the auxiliary star and the observed target point is calculated based on the position vector of the observed target point and the sampled regeneration state vector.

[0187] The fourth step is to determine the baseline vector based on the slant range length.

[0188] Here, the baseline vector between the primary star and the secondary star is further determined based on the slant distance between the secondary star and the observation target point.

[0189] In this embodiment, by acquiring the scene center slant range, Doppler center frequency, and imaging parameters, and combining them with the sampled primary star state vector, the position vector of the observed target point is obtained through range-Doppler RD positioning. Then, based on the position vector of the observed target point and the sampled secondary star state vector, the slant range between the two is determined, thereby determining the baseline vector between the primary and secondary stars. In this way, the on-orbit bistatic SAR interferometric baseline can be accurately estimated. The calculation process is simple and the computational load is small, which improves the efficiency and accuracy of distributed SAR interferometric baseline estimation.

[0190] In some embodiments, the slant distance between the auxiliary star and the target observation point can be determined by the following steps:

[0191] Determine the first, second, and third components of the auxiliary star's position vector in the sampled auxiliary star's state vector, respectively, on the first, second, and third coordinate axes. Determine the first, second, and third components of the target point's position vector, respectively, on the first, second, and third coordinate axes. Square the difference between the first component of the auxiliary star and the first component of the target point to obtain the first value. Square the difference between the second component of the auxiliary star and the second component of the target point to obtain the second value. Square the difference between the third component of the auxiliary star and the third component of the target point to obtain the third value. Summate the first, second, and third values ​​and take the square root to obtain the slant range length.

[0192] Here, based on the position vector P of the target point T and sampled auxiliary star position vector The slant distance between the auxiliary star and the target point can be expressed by formula (39), as follows:

[0193]

[0194] Among them, P T,X P T,Y and P T,Z P T The X, Y, and Z axis components, and They represent The X, Y, and Z axis components.

[0195] In some embodiments, determining the baseline vector based on the slant range length can be achieved through the following steps:

[0196] The first step is to determine the slant range difference between the primary and secondary stars based on the slant range length and the slant range at the scene center.

[0197] Here, the slant range difference between the primary and secondary stars is the difference between the slant range length between the secondary star and the observed target point and the slant range at the scene center. The slant range difference between the primary and secondary stars is obtained by calculating the slant range length and the slant range at the scene center.

[0198] The second step is to determine the time when the auxiliary satellite receives the scattered echo from the observation target point based on the slant range difference.

[0199] Here, the slant range difference between the primary and secondary stars is combined with the time in the sampled state vector of the secondary star to calculate the time when the secondary star receives the scattered echo from the observation target point.

[0200] The third step is to determine the baseline vector based on the reception time.

[0201] Here, the baseline vector between the primary and secondary stars can be calculated based on the time it takes for the secondary star to receive the scattered echo from the observation target point.

[0202] In this embodiment, the slant range difference between the primary and secondary stars is determined based on the slant range between the secondary star and the observed target point, and the slant range at the scene center. The time at which the secondary star receives the scattered echo from the observed target point is then determined, thereby establishing the baseline vectors of the primary and secondary stars. This method achieves the determination of the baseline vectors of the primary and secondary stars based on the slant range. The calculation process is simple and ensures more accurate baseline vectors for the determined primary and secondary stars, improving the efficiency and accuracy of distributed SAR interferometric baseline estimation.

[0203] In some embodiments, the slant distance difference between the primary star and the secondary star can be determined by the following steps:

[0204] The first step is to calculate the difference between the slant distance length and the slant distance at the scene center to obtain the slant distance difference.

[0205] Here, the slant range difference between the primary star and the secondary star can be expressed as: ΔR=R2-R1, where R2 is the slant range length between the secondary star and the observation target point, and R1 is the slant range at the center of the scene.

[0206] The second step is to determine the reception time of the scattered echo from the observation target point by the auxiliary star based on the slant range difference. This includes dividing the slant range difference by the speed of light and summing it with the time in the sampled auxiliary star state vector to obtain the reception time.

[0207] Here, the difference in slant distance between the primary star and the secondary star is divided by the speed of light C, and then added to the time in the sampled state vector of the secondary star to obtain the time when the secondary star receives the scattered echo from the observation target point, which can be expressed as formula (40), as follows:

[0208]

[0209] In some embodiments, determining the baseline vector based on the time it takes for the secondary satellite to receive the scattered echo from the observed target point can be achieved through the following steps:

[0210] The first step is to obtain the receiving position vector of the auxiliary satellite's echo receiving time by interpolation, based on the state vectors of the receiving time and the auxiliary satellite's azimuth time.

[0211] Here, based on the time when the auxiliary star receives the scattered echo from the observation target point and the sampled azimuth time axis of the auxiliary star imaging, the azimuth time state vector of the auxiliary star is interpolated, and the interpolated auxiliary star state vector is used as the receiving position vector of the auxiliary star receiving the echo time.

[0212] The second step is to determine the baseline vector based on the state vector of the auxiliary satellite's echo reception time and the sampled state vector of the primary satellite.

[0213] Here, the baseline vector between the primary and secondary stars is obtained by using the position vector in the state vector of the secondary star's received echo time and the sampled position vector of the primary star.

[0214] In this embodiment, based on the time of the secondary satellite receiving the scattered echo from the observation target point and the secondary satellite's azimuth time state vector, the state vector of the secondary satellite's received echo time is obtained by interpolation. This state vector, combined with the sampled primary satellite state vector, determines the baseline vector between the primary and secondary satellites. This approach reduces computational complexity while enabling faster and more accurate determination of the baseline vectors between the primary and secondary satellites, thus improving the efficiency and accuracy of distributed SAR interferometric baseline estimation.

[0215] In some embodiments, the received position vector can be obtained through the following steps:

[0216] The first step is to interpolate the position vector in the state vector of the auxiliary star's azimuth time based on the reception time on the first, second, and third coordinate axes, respectively, to obtain the first position interpolation, the second position interpolation, and the third position interpolation.

[0217] The second step is to obtain the receiving position vector based on the first position interpolation, the second position interpolation, and the third position interpolation.

[0218] Here, by echo time And auxiliary star azimuth time state vector P S and V S The position vector and velocity vector of the auxiliary satellite receiving the echo are obtained by interpolation, and are expressed by formulas (41) and (42) as follows:

[0219]

[0220]

[0221] In some embodiments, the baseline vector can be determined by the following methods:

[0222] The baseline vector is obtained by taking the difference between the received position vector and the position vector in the sampled primary star state vector.

[0223] Here, based on the sampled primary star position vector and interpolated auxiliary star position vector The baseline vectors of the primary and secondary stars can be calculated using formula (9), as follows:

[0224]

[0225] In some embodiments, after step S104 described above, the following step is further included:

[0226] The first step is to determine a coordinate system with the primary star as the reference, based on the sampled primary star state vector.

[0227] Here, the coordinate system is the TCN coordinate system, where the T-axis represents the satellite's trajectory direction, the N-axis represents the vector direction from the satellite to the Earth's center, and the C-axis is the product of the T and N-axis direction vectors. The TCN coordinate system, with the primary star as the reference, is determined based on the position and velocity vectors from the sampled primary star state vectors.

[0228] For example, in the TCN coordinate system, the T-axis represents the direction of the satellite track. The N-axis represents the vector direction from the satellite to the center of the Earth. and the product Vec, where the C-axis is the direction vector of the T / N axis. N =Vec N XVec T .

[0229] The second step is to determine the three components of the baseline vector based on the baseline vector and the coordinate system.

[0230] Here, based on the baseline vectors of the primary and secondary stars and the TCN coordinate system, the three components of the baseline vectors on the T, N, and C axes of the TCN coordinate system are determined.

[0231] For example, based on baseline vector B and TCN coordinate system [Vec T ,Vec C ,Vec N The three components of the baseline vector can be expressed by formulas (43) to (45), as follows:

[0232] T B =Vec T Τ ·B (43)

[0233] C B=Vec C Τ ·B (44)

[0234] N B =Vec N Τ ·B (45)

[0235] The third step is to fit the quadratic coefficients of the three components to obtain the constant term and the linear coefficients.

[0236] Here, by fitting the quadratic coefficients of the three components of the baseline vector, the data of the constant term and the linear coefficients are obtained.

[0237] The fourth step is to output the constant term and the coefficients of the linear term to the target file.

[0238] Here, the target file can be an interferometric baseline (.base) file. By outputting the constant and linear coefficients to the interferometric baseline (.base) file, the data of the interferometric baseline can be managed and queried through the file.

[0239] In this embodiment, the coordinate system with the primary star as the reference can be determined based on the sampled primary star state vector, thereby determining the three components of the baseline vector; the constant term and the linear term coefficients are obtained through quadratic term coefficient fitting and output to the target file. This method enhances the management and querying of interferometric baselines, enabling rapid and intuitive determination of distributed SAR interferometric baseline estimation results, thus achieving high-precision DEM generation for distributed SAR.

[0240] The following describes the application of the distributed SAR interferometric baseline estimation method provided in this application in a real-world scenario, using a distributed SAR system consisting of one transmitter (primary satellite) and multiple receivers (secondary satellites) as an example.

[0241] This application proposes a SAR interferometric baseline estimation method. Distributed SAR is a distributed Earth microwave imaging system consisting of multiple transmitters and receivers distributed across a platform. Here, we mainly discuss a distributed SAR system consisting of one transmitter (primary satellite) and multiple receivers (secondary satellites). Figure 2 As shown in the illustration, this application provides a schematic diagram of a one-to-many spaceborne distributed SAR imaging system, including a primary satellite and multiple auxiliary satellites. Synchronization links exist between the primary and auxiliary satellites. The primary satellite transmits radar signals to the observed target, and the multiple auxiliary satellites receive the echo signals scattered from the observed target. Currently, in-orbit spaceborne distributed SAR systems include: the TanDEM-X bistatic SAR system developed by the German Aerospace Center (DLR), the my country Land Survey-1 (LT-1) bistatic SAR system, and macro... Figure 1 The LT-1 system is a distributed SAR system. It consists of two L-band fully polarized multi-channel SAR satellites with flexible formation flight capabilities. The primary LT-1 satellite acts as the transmitter, illuminating the target area with radar signals, while both satellites simultaneously receive the scattered echo signals, achieving interferometric stripe imaging. Figure 3 As shown in the figure, an embodiment of this application provides a schematic diagram of a spaceborne dual-base SAR interferometric strip mode imaging, including a primary satellite, a secondary satellite, and a target region. There is a synchronization signal between the primary satellite and the secondary satellite. The primary satellite transmits radar signals to the target region, and the primary satellite and the secondary satellite simultaneously receive the echo signals scattered from the target region, thereby realizing the interferometric strip mode imaging.

[0242] Distributed SAR systems possess all-weather, high-resolution, and wide-coverage capabilities, enabling them to acquire information such as surface elevation and surface deformation along the radar line of sight using complex SAR images and InSAR technology. They have wide applications in topographic mapping, land subsidence monitoring, and natural disaster monitoring. Therefore, interferometry is an important research direction for distributed SAR. Interferometric Synthetic Aperture Radar (InSAR) includes two implementation methods: monostatic SAR heavy-orbit interferometry and dual-multistatic SAR single-pass interferometry. This discussion focuses on the dual-multistatic SAR single-pass interferometry method. Distributed SAR images the same target area using multiple spatially separated platforms, acquiring scattered echoes from the observed target from multiple angles. These echoes are then imaged separately to obtain focused complex radar images. Further, the finely registered complex radar images are correlated to obtain their interferometric phase. Finally, through steps such as coarse estimation of the interferometric baseline, de-flattening, interferometric phase filtering, phase unwrapping, fine baseline estimation, and topographic phase-to-elevation conversion, the DEM of the target area is obtained. Interferometric baselines are spatial vectors pointing from the phase center of the primary satellite antenna to the phase center of the secondary satellite antenna when SAR illuminates the same target. Baseline estimation errors can cause errors in flat-ground phase calculations, leading to a "phase tilt" problem, and can also cause errors in the terrain phase-to-elevation conversion coefficients, reducing the accuracy of DEM inversion. Therefore, millimeter-level precision interferometric baseline estimation methods are an important research direction.

[0243] Interferometric baseline estimation methods are mainly divided into image-based methods and external data-based methods. Image-based baseline estimation methods typically estimate the baseline based on the relationship between the baseline and the registration offset, and the relationship between the baseline and the frequency fringes of the interferogram. These methods require high-quality image data. Furthermore, while they do not require other data besides the image data, they do require the selection of some additional parameters, leading to baseline estimation errors. External data-based baseline estimation methods typically require ground control points (GCPs) and external DEM data. GCP-based methods estimate the interferometric baseline by establishing an interferometric observation equation and solving the observation equation parameters using multiple GCPs. GCP methods theoretically have high accuracy, but GCPs in the target area are often difficult to obtain, and the accuracy of the GCPs affects the accuracy of the baseline estimation. DEM-based methods first set an initial baseline value, then continuously adjust the baseline so that the characteristics of the interferometric data simulated based on the external DEM approximate the original interferometric data, or obtain the residual phase using the external DEM, and then construct an observation equation based on the relationship between the baseline and the residual phase to achieve accurate interferometric baseline estimation. The two methods mentioned above require high-quality complex SAR images or additional reference data. Their estimation accuracy is easily affected by factors such as system noise and ambiguity interference. The processing is complex, computationally intensive, and inefficient. To address this, this application proposes a spaceborne distributed SAR interferometric baseline estimation method based on satellite orbit data, and verifies the method using LT-1 bistatic SAR data.

[0244] This application mainly proposes a method for estimating the interferometric baseline of spaceborne distributed SAR, achieving accurate interferometric baseline estimation and high-precision digital elevation model generation. The technical solutions adopted in this application mainly include:

[0245] To accurately estimate the interferometric baseline of spaceborne distributed SAR and ensure the inversion accuracy of the digital elevation model of the target area, this application proposes an interferometric baseline estimation method, such as... Figure 3 As shown, the process includes the following:

[0246] First, based on the imaging azimuth and time axes of the primary and secondary stars, and using the primary star as a reference, the imaging azimuth and time axes of the primary and secondary stars are synchronized. This synchronization is achieved by adding a constant time deviation to the secondary star's imaging azimuth and time axis. This constant time deviation is a known quantity obtained through existing inter-satellite radar synchronization links. Further, the synchronized imaging azimuth and time axes of the primary and secondary stars are obtained.

[0247] Then, the synchronous imaging azimuth time axis of the primary and secondary satellites, and the orbital state vectors (time, position vector and velocity vector) sampled at second intervals of the primary and secondary satellites are input into the high-precision satellite orbital transmitter to obtain the azimuth time state vectors of the primary and secondary satellites.

[0248] Furthermore, the state vector of the primary star's azimuth time is sampled every N intervals along the primary star's imaging azimuth time axis to obtain the sampled primary star imaging azimuth time axis and the sampled primary star state vector. Based on the sampled primary star imaging azimuth time axis, the state vectors at the same time in the secondary star's azimuth time state vector are extracted to obtain the sampled secondary star state vector.

[0249] Furthermore, range-Doppler RD positioning is performed based on the sampled primary star state vector, scene center slant distance R1, Doppler center frequency, and imaging parameters to obtain the position vector of the observed target point T. Based on the position vector of the observed target point T and the sampled secondary star state vector, the slant distance R2 between the secondary star and the observed target point T is obtained.

[0250] Furthermore, based on the slant range lengths of the primary and secondary stars, their slant range difference ΔR is obtained. The time when the secondary star receives the echo T is calculated based on the slant range difference ΔR, and the state vector of the secondary star's echo reception time is interpolated based on this echo time and the secondary star's azimuth time state vector.

[0251] Finally, based on the sampled primary star state vector and the interpolated secondary star state vector, the baseline vectors of the primary and secondary stars are calculated. Simultaneously, based on the sampled primary star state vector, a TCN coordinate system with the primary star as the reference is obtained. In this coordinate system, the T-axis represents the satellite's trajectory direction, the N-axis represents the vector direction from the satellite to the Earth's center, and the C-axis, along with the T-axis and N-axis, forms a Cartesian coordinate system. Based on the baseline vector and the TCN coordinate system, the three components of the baseline vector in the TCN are obtained, and quadratic coefficients are fitted to each of the three components to obtain their constant and linear coefficients. Finally, their constant and linear coefficients are output to the interferometric baseline.base file.

[0252] Distributed SAR (such as the LT-1 bistatic SAR system) acquires multi-angle complex radar images of the target area through single-pass navigation. Then, using InSAR technology for terrain phase-to-elevation conversion, the DEM of the target area can be retrieved from the complex images. However, the accuracy of the interferometric baseline estimation is related to the accuracy of the phase-to-elevation conversion coefficient, directly affecting the final DEM retrieval accuracy. Therefore, this application proposes an interferometric baseline estimation method, which mainly includes three parts: baseline estimation procedure, range-Doppler RD positioning, and on-orbit experimental verification.

[0253] Part 1: Baseline Estimation Process

[0254] To achieve accurate estimation of distributed SAR interferometric baselines and ensure the inversion accuracy of the DEM in the observation area, this application proposes an accurate interferometric baseline estimation method, such as... Figure 4 The diagram illustrates a distributed SAR interferometric baseline estimation process provided in this application embodiment, which specifically includes the following steps:

[0255] First, assume that the imaging azimuth and time axis of the primary star and the imaging azimuth and time axis of any one of the auxiliary stars are respectively formulas (10) and (11), as follows:

[0256] t M,i =t M,0 +(i-1)·PRT M (10)

[0257] t S,j =t S,0 +(j-1)·PRT S (11)

[0258] In this context, the subscripts M and S represent the primary star and the secondary star, respectively, and t M,0 The time t represents the start time of imaging the primary star. S,0 The start time of secondary star imaging, i∈{1,2,…,N} M} represents the pulse repetition time (PRT) of the primary star imaging, j∈{1,2,…,N} S} represents the j-th PRT of the secondary star imaging, PRT M and PRT S The PRT represents the primary and secondary stars, and the PRT M ≈PRT S =PRT.

[0259] Based on existing inter-satellite radar synchronization links, the time difference between the primary and secondary satellites can be obtained. For example... Figure 3 As shown, the primary satellite transmits radar signals to illuminate the target area, and the two satellites receive the scattered echo signals. Simultaneously, the two satellites exchange synchronization pulses during idle time outside the radar echo reception window, without affecting the normal operation of the radar. Assume the primary satellite transmits a synchronization signal during the idle time of the first PRT, and the secondary satellite receives this synchronization signal; secondly, the secondary satellite transmits a synchronization signal to the primary satellite during the second PRT; then, the primary and secondary satellites alternately transmit synchronization signals. Finally, the synchronization data from the primary and secondary satellites is transmitted to the ground station, and matched filtering is performed on the synchronization data in the computer to obtain compressed pulse peaks. Then, the peak position time of the secondary satellite's synchronization signal is subtracted from the peak position time of the primary satellite and divided by 2 to obtain their time deviation Δt = t. S,0 -t M,0Finally, the azimuth time axis of the auxiliary star is shifted to the right by Δt time deviation to obtain the synchronized azimuth time axis of the auxiliary star as shown in formula (1), as follows:

[0260]

[0261] Similarly, the time axis of the azimuth of the primary star during synchronization is given by formula (2), as follows:

[0262]

[0263] Secondly, assume that the sampling time of the primary star at second intervals is T. M The position vector is P M And the velocity vector is V M And the sampling time of the auxiliary star is T seconds. S The position vector is P S And the velocity vector is V S The sampling time interval between the primary and secondary stars is expressed by formulas (12) and (13), as follows:

[0264] T M =[T M,1 ,…,T M,p ,…,T M,P (12)

[0265] T S =[T S,1 ,…,T S,a ,…,T S,A (13)

[0266] Among them, T M,p -T M,p-1 =1,T S,a -T S,a-1 =1, and The position and velocity vectors of the primary star are described by formulas (14) and (15), as follows:

[0267]

[0268]

[0269] Similarly, the position and velocity vectors of the auxiliary star are expressed by formulas (16) and (17), as follows:

[0270]

[0271]

[0272] Then, synchronize the primary star's timeline. Synchronized auxiliary star timeline The time T of the main star M Position vector P M and velocity vector V M and the time T of the auxiliary star S Position vector P S and velocity vector V S Input the high-precision satellite orbit transmitter shown in Table 1 below:

[0273] Table 1 High-precision satellite orbital transmitters

[0274] gravitational field 21X 21EGM96 Atmospheric drag Dynamic atmospheric density models (e.g., Jacchia–Roberts) Solar radiation pressure spherical model and biconical shadow model Celestial acceleration Sun and Moon Gravitational Model Solid tides and ocean tides Solid tides and other perturbations (e.g., general relativity)

[0275] The state vectors (time, position vector, and velocity vector) of the primary and secondary stars' positions and times are obtained as described in formulas (18) to (23):

[0276]

[0277]

[0278]

[0279]

[0280]

[0281]

[0282] Then, the state vector of the primary star's azimuth time is sampled every N intervals along the primary star's imaging azimuth time axis to obtain the sampled primary star's azimuth time axis and state vector, as described in formulas (3) to (5):

[0283]

[0284]

[0285]

[0286] Based on the sampled primary star azimuth time axis, extract the state vectors (sampled auxiliary star state vectors) at the same time from the auxiliary star azimuth time state vectors, as described in formulas (6) to (8):

[0287]

[0288]

[0289]

[0290] Furthermore, based on the sampled primary star position vector and velocity vector Based on the scene center slant distance R1, Doppler center frequency, and imaging parameters, the position vector of the observed target point is estimated according to the distance-Doppler RD positioning method described in Part 2, as shown in formula (46):

[0291]

[0292] Based on the position vector P of the target point T and sampled auxiliary star position vector Calculate the slant range between the auxiliary star and the target point, as shown in formula (39):

[0293]

[0294] Among them, P T,X P T,Y and P T,Z P T The X, Y, and Z axis components, and They represent The X, Y, and Z axis components.

[0295] Therefore, the slant range difference between the primary star and the secondary star is ΔR = R2 - R1. Furthermore, by dividing the slant range difference by the speed of light C, the time it takes for the secondary star to receive the scattered echo from the target point can be obtained using formula (40), as follows:

[0296]

[0297] Based on echo time And auxiliary star azimuth time state vector P S and V S Interpolation yields the position and velocity vector of the auxiliary satellite at the time of receiving the echo, as shown in formulas (41) and (42):

[0298]

[0299]

[0300] Based on the sampled primary star position vector and interpolated auxiliary star position vector Calculate the baseline vectors of the primary and secondary stars, as shown in formula (9):

[0301]

[0302] At the same time, based on the sampled primary star state vector and The TCN coordinate system with the primary star as a reference is obtained. The T-axis represents the direction of the satellite's trajectory. The N-axis represents the vector direction from the satellite to the center of the Earth. and the product Vec, where the C-axis is the direction vector of the T / N axis. N =Vec N XVec T .

[0303] Based on baseline vector B and TCN coordinate system [Vec T ,Vec C ,Vec N The three components of the baseline vector are obtained as shown in formulas (43) to (45):

[0304] T B =Vec T Τ ·B (43)

[0305] C B =Vec C Τ ·B (44)

[0306] N B =Vec N Τ ·B (45)

[0307] Finally, regarding T B C B and N B The three components are fitted with quadratic coefficients to obtain their constant and linear coefficients. Finally, their constant and linear coefficients are output to the interferometric baseline .base file.

[0308] Part Two: Distance-Doppler RD Localization Method

[0309] Based on SAR slant range, Doppler center frequency, and imaging parameters, the range-Doppler RD positioning method can be used to obtain the position vector of the observed target point. For example... Figure 5 As shown in the embodiment of this application, a geometric model for range-Doppler RD positioning is provided. A Cartesian coordinate system is established with the SAR nadir point as the origin, the x-axis along the track direction (azimuth direction) as the x-axis, and the y-axis perpendicular to the track direction (range direction). Assuming the coordinates of the ground target point are (x, y, 0), the slant range between the SAR satellite and the target point is R, the angle between the satellite's motion direction and the SAR line-of-sight direction is γ, the satellite's orbital altitude is h, and the satellite's velocity is V, then equation (24) can be obtained as follows:

[0310] x 2 +y 2 =R 2 -h 2 (twenty four)

[0311] A SAR (Special Radar System) at a certain flight path position observes ground targets. Multiple target points equidistant from the SAR antenna's phase center can form a concentric circle centered on the nadir point. For example... Figure 6 As shown, including equal Doppler lines and equidistant lines, the distance from a target point on the same circle to the nadir point is equal. In other words, the distance from the SAR to a target point on the same circle is equal. Therefore, target points on different concentric circles can be distinguished based on the echo signal delay.

[0312] Assume the SAR signal carrier frequency is f c Given the speed of light as c and the wavelength as λ, the frequency of the radar echo signal is given by formula (25), as follows:

[0313]

[0314] in, This represents the Doppler center frequency. Furthermore, the radial velocity of the SAR relative to the target point can be expressed by formula (26), as follows:

[0315] V r =Vcosγ (26)

[0316] in, And it can be transformed into the following equation (27):

[0317] (x 2 +y 2 +h 2 cos 2 γ=x 2 (27)

[0318] Furthermore, by transforming the above equation, we obtain the hyperbola equation formula (28), as follows:

[0319]

[0320] The above equations can describe the equal Doppler frequency distribution in the observation area. For example... Figure 6 As shown, including range and azimuth values, within the SAR beam range, targets containing the same Doppler frequency have different time delays, and targets with the same time delay contain different Doppler frequencies. Based on this, precise target positioning can be achieved.

[0321] In target positioning, the ellipsoid equation is usually used to describe the Earth model, which can be expressed as formula (29), as follows:

[0322]

[0323] Among them, R e R is the average equatorial radius. p=(1-1 / f)R e The polar radius and f = 298.255 represent the flatness factor.

[0324] In target localization, the slant range from the phase center of the SAR antenna to the observed target point can be expressed by formula (30), as follows:

[0325]

[0326] in, For satellite position vectors, Let be the target position vector, c be the speed of light, and τ be the echo delay of the target.

[0327] In target localization, the Doppler center frequency f of the target's scattered echo is... dc This can be expressed as formula (31), as follows:

[0328]

[0329] Where λ is the radar wavelength, and This represents the velocity vector of the SAR satellite relative to the target point. Furthermore, solving the above three equations yields the position vector of the observed target.

[0330] Assume that the position vectors of the SAR satellite and the observed target point in the geocentric coordinate system are respectively and Then we can obtain the following equations and formulas (32) and (33), as follows:

[0331] R 2 =(R sx -x) 2 +(R sy -y) 2 +(R sz -z) 2 (31)

[0332] R 2 =R sx 2 +x 2 +R sy 2 +y 2 +R sz 2 +z 2 -2R sx x-2R sy y-2R sz z (32)

[0333] Based on the equation of the ellipsoid, we can obtain the following equation formula (34), as follows:

[0334]

[0335] Furthermore, based on the Doppler equation, we can obtain the following equation formula (35), as follows:

[0336]

[0337] Then, the above three equations can be combined into a system of three equations, formula (36), as follows:

[0338]

[0339] The system of equations consists of three nonlinear equations. Here, the Newton-Raphson iterative method is used to estimate the approximate solution of the system of equations, and its main iterative process is described by formula (37):

[0340] X k+1 =X k -B -1 f(X k (37)

[0341] X = [xyz] T , f(X)=[f1(x,y,z) f2(x,y,z) f3(x,y,z)] T B -1 Represents the Jacobi matrix

[0342] The inverse matrix of the k-th iteration is expressed as formula (38), as follows:

[0343]

[0344] The above summarizes the main points of the range-Doppler RD localization method. Finally, the iterative process for solving the target location is summarized as follows:

[0345] 1) Based on the satellite state vector, calculate the satellite's position vector and velocity vector in the orbital coordinate system;

[0346] 2) Transform the position and velocity vectors in the orbital coordinate system to their corresponding vectors in the geocentric coordinate system;

[0347] 3) Calculate the Jacobi matrix;

[0348] 4) Set the initial value and threshold for the iteration, and calculate X. k+1 -X k Check if it is less than a threshold; if it is, stop the calculation; otherwise, use the X obtained in the previous step. k+1 Used as initial values ​​for iterative calculations;

[0349] 5) Output the position vector of the observed target in the Earth-centered Earth-fixed coordinate system.

[0350] Part Three: On-orbit Experimental Verification

[0351] Based on satellite state vectors, phase synchronization data, and radar echo data acquired by the on-orbit LT-1 bistatic SAR system, the distributed SAR interferometric baseline estimation method proposed in this application is validated. The main parameters of the LT-1 system are shown in Table 2. Because the estimation accuracy of the interferometric baseline directly affects the accuracy of the retrieved DEM, this experiment evaluates the performance of the interferometric baseline estimation method by analyzing the DEM accuracy. As shown in Table 2, the interferometric baseline estimation method extracted in this application is validated based on two sets of data acquired by the LT-1 system on July 7, 2022, and October 19, 2022.

[0352] Table 2 Main parameters of the LT-1 bistatic SAR system

[0353] orbital height 607km carrier frequency 1.26GH radar signal bandwidth 80MHz Radar signal pulse width 100us Radar signal sampling rate 90MHz Synchronization signal bandwidth 80MHz Synchronization signal pulse width 10us Synchronization signal sampling rate 90MHz Antenna length 9.8m Experimental data number 1 20220707 Experimental data number 2 20221019

[0354] First, the echo data from the primary and secondary satellites of satellite 20220707 were imaged separately to obtain focused complex radar images. Second, based on interferometric processing software, the complex radar images were precisely registered (<0.05 pixel accuracy) to obtain their interferometric phase.

[0355] Then, the interferometric baseline parameters are estimated using the baseline estimation method proposed in the embodiments of this application, as shown in Table 3:

[0356] Table 3. Baseline estimation results for bistatic SAR data from July 7, 2022.

[0357]

[0358]

[0359] Finally, based on the interferometric baselines in Table 3, the following steps were performed sequentially: terrain clearing, interferometric phase filtering, phase unwrapping, and terrain phase-to-elevation conversion, to obtain the DEM of the target area, as shown below. Figure 7 As shown, this includes range and azimuth values. Figure 8 As shown, this includes range and azimuth values. Furthermore, it will... Figure 7 DEM and Figure 8 The elevation error between the two SRTM reference elevation data is obtained by subtracting them, as shown below. Figure 9 As shown, this includes distance and azimuth values, and the statistical results of elevation errors are as follows: Figure 10 As shown, this includes height error and error distribution. In Figure 10 In the figure, the STD of the elevation error is approximately 3.2m, which meets the design requirements of the interferometric SAR system.

[0360] Similarly, the radar data numbered 20221019 was processed, and the interferometric baseline was estimated using the baseline estimation method proposed in the embodiments of this application, as shown in Table 4:

[0361] Table 4. Baseline estimation results for bistatic SAR data from October 19, 2022.

[0362] Baseline -2132.9589m -2019.2183m 1000.7532m Baseline change rate -1.9754m / s -2.0021m / s -1.1478m / s

[0363] The DEM of the target area was obtained based on data from 20221019, such as Figure 11 As shown, this includes range and azimuth values. Figure 12 The above includes range and azimuth values, which will Figure 11 DEM and Figure 12 Subtracting the SRTM data shown, we obtain the elevation error between the two, as follows: Figure 13 As shown, this includes distance and azimuth values, and the statistical results of elevation errors are as follows: Figure 14 As shown, this includes height error and error distribution. In Figure 14 In the figure, the STD of the elevation error is approximately 2.8m, which meets the design requirements of the interferometric SAR system.

[0364] This application proposes a method for estimating the interferometric baseline of spaceborne distributed SAR, achieving accurate interferometric baseline estimation and high-precision digital elevation model (DEM) generation. Experimental results demonstrate that the obtained DEM accuracy meets the design requirements of the distributed SAR system; the proposed method can accurately estimate the distributed SAR interferometric baseline; the distributed SAR interferometric baseline estimation method and process are effective; and experimental results prove the feasibility of the method and the accuracy of the estimation results, achieving accurate estimation of the distributed SAR interferometric baseline while meeting the requirements of high-precision DEM inversion. Thus, accurate estimation of the on-orbit bistatic SAR interferometric baseline and generation of a high-precision distributed SAR DEM are achieved. Furthermore, the method features simple data processing, low computational load, and high computational efficiency, improving the efficiency and accuracy of distributed SAR interferometric baseline estimation.

[0365] Based on the foregoing embodiments, this application provides a distributed SAR interferometric baseline estimation device. The device includes various units and modules included in each unit, which can be implemented by a processor in a computer device; of course, it can also be implemented by specific logic circuits. In the implementation process, the processor can be a central processing unit (CPU), a microprocessor unit (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA), etc.

[0366] Figure 15 This is a schematic diagram of the composition structure of a distributed SAR interferometric baseline estimation device provided in an embodiment of this application, as shown below. Figure 15 As shown, the distributed SAR interferometric baseline estimation device 1500 includes: a synchronization module 1501, a first determination module 1502, a sampling module 1503, and a second determination module 1504, wherein:

[0367] Synchronization module 1501 is used to synchronize the imaging azimuth time axis of the primary star and the imaging azimuth time axis of the secondary star, so as to obtain synchronized imaging azimuth time axis of the primary star and synchronized imaging azimuth time axis of the secondary star.

[0368] The first determining module 1502 is used to determine the state vector of the primary star's azimuth time and the state vector of the secondary star's azimuth time based on the synchronized primary star imaging azimuth time axis and the synchronized secondary star imaging azimuth time axis.

[0369] The sampling module 1503 samples the state vector of the primary star's azimuth time and the state vector of the secondary star's azimuth time to obtain the sampled state vector of the primary star and the sampled state vector of the secondary star.

[0370] The second determining module 1504 is used to determine the baseline vectors of the primary star and the secondary star based on the sampled primary star state vector and the sampled secondary star state vector.

[0371] In some embodiments, the synchronization module further includes:

[0372] The first determining submodule is used to determine the imaging azimuth time axis of the primary star and the imaging azimuth time axis of any one of the multiple auxiliary stars.

[0373] The first acquisition submodule is used to acquire the synchronization data of the primary star and the secondary star.

[0374] The second determining submodule is used to determine the time deviation between the imaging azimuth time axis of the primary star and the imaging azimuth time axis of the secondary star based on the synchronization data.

[0375] The offset submodule is used to offset the azimuth time axis of the auxiliary star imaging by the time deviation to obtain the synchronized azimuth time axis of the auxiliary star imaging.

[0376] The third determining submodule is used to determine that the imaging azimuth time axis of the primary star is the same as the imaging azimuth time axis of the synchronized primary star.

[0377] In some embodiments, the first determining module further includes:

[0378] The fourth determining submodule is used to determine the second-interval sampling time, the first position vector, the first velocity vector of the primary star, and the second-interval sampling time, the second position vector, and the second velocity vector of the auxiliary star.

[0379] The input submodule is used to input the synchronized primary star imaging azimuth time axis, the synchronized secondary star imaging azimuth time axis, the second-interval sampling time of the primary star, the first position vector, the first velocity vector, and the second-interval sampling time of the secondary star, the second position vector, and the second velocity vector into the high-precision satellite orbit propagator to obtain the state vector of the primary star azimuth time and the state vector of the secondary star azimuth time.

[0380] In some embodiments, the sampling module further includes:

[0381] The first sampling module is used to sample the state vector of the primary star's azimuth time based on the primary star's imaging azimuth time axis and a preset interval, so as to obtain the sampled primary star imaging azimuth time axis and the sampled primary star state vector.

[0382] The fifth determining submodule is used to determine the state vector at the same time as the sampled primary star imaging azimuth time axis in the state vector of the auxiliary star azimuth time, so as to obtain the sampled auxiliary star state vector.

[0383] In some embodiments, the sampling module further includes:

[0384] The first sampling unit is used to sample the state vector of the primary star's azimuth time on the first coordinate axis, the second coordinate axis, and the third coordinate axis based on the primary star's imaging azimuth time axis and the preset interval, respectively, to obtain the sampled primary star imaging azimuth time axis, the first component of the primary star sampling, the second component of the primary star sampling, and the third component of the primary star sampling.

[0385] The first determining unit is used to determine the sampled primary star state vector based on the first component, the second component, and the third component of the primary star sampling.

[0386] In some embodiments, the fourth determining submodule further includes:

[0387] The second determining unit is used to determine the state vectors at the same time in the state vectors of the secondary star's azimuth time on the first coordinate axis, the second coordinate axis, and the third coordinate axis, based on the sampling primary star imaging azimuth time axis, to obtain the first component, the second component, and the third component of the secondary star sampling.

[0388] The third determining unit is used to determine the sampled auxiliary star state vector based on the first component, the second component, and the third component of the auxiliary star sampling.

[0389] In some embodiments, the second determining module further includes:

[0390] The second acquisition submodule is used to acquire the scene center slant range, Doppler center frequency, and imaging parameters;

[0391] The positioning submodule is used to obtain the position vector of the observed target point by using range-Doppler RD positioning based on the sampled primary star state vector, the scene center slant distance, the Doppler center frequency, and the imaging parameters.

[0392] The sixth determination submodule is used to determine the slant distance between the auxiliary star and the observation target point based on the position vector of the observation target point and the sampled auxiliary star state vector.

[0393] The seventh determination submodule is used to determine the baseline vector based on the slant distance length.

[0394] In some embodiments, the fifth determining submodule further includes:

[0395] The fourth determining unit is used to determine the first component, the second component, and the third component of the auxiliary star in the sampled auxiliary star state vector, respectively, on the first coordinate axis, the second coordinate axis, and the third coordinate axis.

[0396] The fifth determining unit is used to determine the first component, the second component, and the third component of the position vector of the observation target point on the first coordinate axis, the second coordinate axis, and the third coordinate axis, respectively.

[0397] The first calculation unit is used to square the difference between the first component of the auxiliary star and the first component of the observation target point to obtain the first value.

[0398] The second calculation unit is used to square the difference between the second component of the auxiliary star and the second component of the observation target point to obtain the second value.

[0399] The third calculation unit is used to square the difference between the third component of the auxiliary star and the third component of the observation target point to obtain the third value.

[0400] The fourth calculation unit is used to sum the first value, the second value, and the third value and then take the square root to obtain the slope distance length.

[0401] In some embodiments, the sixth determining submodule further includes:

[0402] The sixth determining unit is used to determine the slant range difference between the primary star and the secondary star based on the slant range length and the slant range of the scene center.

[0403] The seventh determining unit is used to determine the time when the auxiliary satellite receives the scattered echo from the observation target point based on the slant range difference.

[0404] The eighth determining unit is used to determine the baseline vector based on the receiving time.

[0405] In some embodiments, the sixth determining unit further includes:

[0406] The first calculation subunit is used to obtain the slope difference by subtracting the slope distance length from the slope distance of the scene center;

[0407] The first calculation subunit is used to divide the slant range difference by the speed of light and sum the time in the sampled auxiliary star state vector to obtain the receiving time.

[0408] In some embodiments, the eighth determining unit further includes:

[0409] An interpolation subunit is used to obtain the receiving position vector of the auxiliary satellite receiving the scattered echo from the observation target point by interpolation based on the state vector of the receiving time and the azimuth time of the auxiliary satellite;

[0410] A determination subunit is used to determine the baseline vector based on the received position vector and the sampled primary satellite state vector.

[0411] In some embodiments, the interpolation subunit is further configured to interpolate the position vector in the state vector of the auxiliary satellite azimuth time on the first coordinate axis, the second coordinate axis, and the third coordinate axis, respectively, based on the receiving time, to obtain a first position interpolation, a second position interpolation, and a third position interpolation; and to obtain the receiving position vector based on the first position interpolation, the second position interpolation, and the third position interpolation.

[0412] In some embodiments, the determining subunit is further configured to calculate the difference between the received position vector and the position vector in the sampled primary star state vector to obtain the baseline vector.

[0413] In some embodiments, the apparatus further includes:

[0414] The third determining module is used to determine a coordinate system with the primary star as a reference based on the sampled primary star state vector.

[0415] The fourth determining module is used to determine the three components of the baseline vector based on the baseline vector and the coordinate system.

[0416] The fitting module is used to fit the quadratic coefficients of the three components respectively to obtain the constant term and the linear coefficients.

[0417] The output module is used to output the constant term and the coefficients of the linear term to the target file.

[0418] This application provides a distributed SAR interferometric baseline estimation device, which enables accurate estimation of distributed SAR interferometric baselines while meeting the requirements of high-precision DEM inversion. Furthermore, the data processing is simple, computationally intensive, and highly efficient, thereby improving the efficiency and accuracy of distributed SAR interferometric baseline estimation.

[0419] The descriptions of the apparatus embodiments above are similar to those of the method embodiments above, and have similar beneficial effects. In some embodiments, the functions or modules included in the apparatus provided in this disclosure can be used to perform the methods described in the method embodiments above. For technical details not disclosed in the apparatus embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0420] It should be noted that, in the embodiments of this application, if the above-mentioned problem discovery method is implemented as a software functional module and sold or used as an independent product, it can also be stored in a terminal-readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to the prior art, can be embodied in the form of a software product. This terminal software product is stored in a storage medium and includes several instructions to cause a terminal (which may be a personal computer or a server, etc.) to execute all or part of the methods described in the various embodiments of this application.

[0421] This application provides a distributed SAR system, which includes a primary satellite and a secondary satellite. The primary satellite emits radar signals to illuminate a target area. The primary satellite and the secondary satellite receive echo signals scattered from the target area. During idle time outside the time window for receiving the echo signals, the primary satellite and the secondary satellite alternately transmit synchronization signals. The primary satellite and the secondary satellite respectively send synchronization data corresponding to the synchronization signals to a computer at a ground station.

[0422] Here, the primary satellite transmits radar signals to illuminate the target area, and the two satellites receive the scattered echo signals. Simultaneously, the two satellites exchange synchronization pulses during idle periods outside the radar echo reception window, without affecting the normal operation of the radar. Assume the primary satellite transmits a synchronization signal during the idle period of the first PRT, and the secondary satellite receives this synchronization signal; then, the secondary satellite transmits a synchronization signal to the primary satellite during the second PRT; then, the primary and secondary satellites alternately transmit synchronization signals. Finally, the synchronization data from the primary and secondary satellites is transmitted to the ground station, and matched filtering is performed on the synchronization data in the computer to obtain compressed pulse peaks. Then, the peak position time of the secondary satellite's synchronization signal is subtracted from the peak position time of the primary satellite and divided by 2 to obtain their time deviation.

[0423] This application provides a computer device including a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the program, it implements some or all of the steps in the above-described method.

[0424] This application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements some or all of the steps in the above-described method. The computer-readable storage medium can be transient or non-transient.

[0425] This application provides a computer program including computer-readable code, wherein when the computer-readable code is executed in a computer device, a processor in the computer device performs some or all of the steps in the above-described method.

[0426] This application provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program. When the computer program is read and executed by a computer, it implements some or all of the steps in the above-described method. This computer program product can be implemented specifically through hardware, software, or a combination thereof. In some embodiments, the computer program product is specifically embodied as a computer storage medium; in other embodiments, the computer program product is specifically embodied as a software product, such as a software development kit (SDK), etc.

[0427] It should be noted that the descriptions of the various embodiments above tend to emphasize the differences between them, while their similarities or commonalities can be referred to interchangeably. The descriptions of the above embodiments of the device, storage medium, computer program, and computer program product are similar to the descriptions of the above method embodiments and have similar beneficial effects. For technical details not disclosed in the embodiments of the device, storage medium, computer program, and computer program product of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0428] It should be noted that, Figure 16 This is a schematic diagram of a hardware entity of a computer device in an embodiment of this application, such as... Figure 16 As shown, the hardware entity of the computer device 1600 includes: a processor 1601, a communication interface 1602, and a memory 1603, wherein:

[0429] Processor 1601 typically controls the overall operation of computer device 1600.

[0430] Communication interface 1602 enables computer devices to communicate with other terminals or servers over a network.

[0431] The memory 1603 is configured to store instructions and applications executable by the processor 1601, and can also cache data to be processed or already processed (e.g., image data, audio data, voice communication data, and video communication data) in the processor 1601 and various modules in the computer device 1600. It can be implemented using flash memory or random access memory (RAM). Data transfer between the processor 1601, the communication interface 1602, and the memory 1603 can be performed via bus 1604.

[0432] The descriptions of the storage medium and device embodiments above are similar to those of the method embodiments above, and have similar beneficial effects. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0433] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms. The above-mentioned separated components may or may not be physically separated, and the components shown may or may not be physical units; they can be located in one place or distributed across multiple network units; some or all of the units can be selected to achieve the purpose of the solution in this embodiment according to actual needs.

[0434] Furthermore, the functional units in the various embodiments of this application can all be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units. Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable storage medium, and when executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks. Alternatively, if the integrated unit of this application is implemented as a software functional module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to the prior art, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause the terminal to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage media include various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks. The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A distributed SAR interferometric baseline estimation method, characterized in that, The method includes: Synchronize the imaging azimuth and time axis of the primary star and the imaging azimuth and time axis of the secondary star to obtain a synchronized imaging azimuth and time axis of the primary star and a synchronized imaging azimuth and time axis of the secondary star. Based on the synchronized primary star imaging azimuth time axis and the synchronized secondary star imaging azimuth time axis, determine the state vector of the primary star azimuth time and the state vector of the secondary star azimuth time. The state vectors of the primary star's azimuth time and the secondary star's azimuth time are sampled to obtain the sampled state vectors of the primary star and the sampled state vectors of the secondary star. Based on the sampled primary star state vector and the sampled secondary star state vector, the baseline vectors of the primary star and the secondary star are determined. The determination of baseline vectors for the primary and secondary stars based on the sampled primary star state vector and the sampled secondary star state vector includes: Obtain the scene center slant distance, Doppler center frequency, and imaging parameters; Based on the sampled primary star state vector, the scene center slant distance, the Doppler center frequency, and the imaging parameters, the position vector of the observed target point is obtained through range-Doppler RD positioning. Based on the position vector of the observation target point and the sampled auxiliary star state vector, the slant distance between the auxiliary star and the observation target point is determined; The baseline vector is determined based on the slant distance length.

2. The method according to claim 1, characterized in that, The synchronization of the primary star imaging azimuth time axis and the secondary star imaging azimuth time axis to obtain synchronized primary star imaging azimuth time axis and synchronized secondary star imaging azimuth time axis includes: Determine the imaging azimuth and time axis of the primary star and the imaging azimuth and time axis of any one of the auxiliary stars; Acquire synchronization data between the primary star and the secondary star; Based on the synchronization data, the time deviation between the imaging azimuth time axis of the primary star and the imaging azimuth time axis of the secondary star is determined. The time deviation is offset from the azimuth time axis of the auxiliary star imaging to obtain the synchronized azimuth time axis of the auxiliary star imaging. The imaging azimuth time axis of the primary star is determined to be the imaging azimuth time axis of the synchronized primary star.

3. The method according to claim 1, characterized in that, The determination of the state vectors for the primary star's azimuth time and the secondary star's azimuth time based on the synchronized primary star imaging azimuth time axis and the synchronized secondary star imaging azimuth time axis includes: Determine the sampling time, first position vector, and first velocity vector of the primary star, and the sampling time, second position vector, and second velocity vector of the secondary star; The synchronized primary star imaging azimuth time axis, the synchronized secondary star imaging azimuth time axis, the second-interval sampling time of the primary star, the first position vector, the first velocity vector, and the second-interval sampling time of the secondary star, the second position vector, and the second velocity vector are input into a high-precision satellite orbit propagator to obtain the state vector of the primary star azimuth time and the state vector of the secondary star azimuth time.

4. The method according to any one of claims 1 to 3, characterized in that, The sampling of the state vectors of the primary star's azimuth time and the secondary star's azimuth time to obtain sampled primary star state vectors and sampled secondary star state vectors includes: Based on the primary star imaging azimuth time axis and a preset interval, the state vector of the primary star azimuth time is sampled to obtain the sampled primary star imaging azimuth time axis and the sampled primary star state vector. The sampled auxiliary star state vector is obtained by determining the state vector at the same time as the sampled primary star imaging azimuth time axis in the auxiliary star's azimuth time state vector.

5. The method according to claim 4, characterized in that, The step of sampling the state vector of the primary star's azimuth time based on the primary star's imaging azimuth time axis and a preset interval to obtain the sampled primary star's imaging azimuth time axis and the sampled primary star's state vector includes: Based on the primary star imaging azimuth time axis and the preset interval, the state vector of the primary star azimuth time is sampled on the first coordinate axis, the second coordinate axis and the third coordinate axis respectively to obtain the sampled primary star imaging azimuth time axis, the first component of the primary star sampling, the second component of the primary star sampling and the third component of the primary star sampling. Based on the first component, the second component, and the third component of the primary star sampling, the sampled primary star state vector is determined.

6. The method according to claim 4, characterized in that, The state vector for determining the azimuth time of the auxiliary star, which is the same as the state vector at the same time as the sampled primary star imaging azimuth time axis, is used to obtain the sampled auxiliary star state vector, including: Based on the sampling of the primary star imaging azimuth time axis, the state vectors at the same time in the state vectors of the secondary star azimuth time are determined on the first coordinate axis, the second coordinate axis, and the third coordinate axis, respectively, to obtain the first component, the second component, and the third component of the secondary star sampling. Based on the first component, the second component, and the third component of the auxiliary star sampling, the sampled auxiliary star state vector is determined.

7. The method according to claim 1, characterized in that, The determination of the slant range between the auxiliary star and the observation target point based on the position vector of the observed target point and the sampled auxiliary star state vector includes: The position vectors in the sampled auxiliary star state vectors are determined to be the first component, the second component, and the third component of the auxiliary star on the first coordinate axis, the second coordinate axis, and the third coordinate axis, respectively. The position vector of the observation target point is determined to have a first component, a second component, and a third component on the first coordinate axis, the second coordinate axis, and the third coordinate axis, respectively. The first value is obtained by squaring the difference between the first component of the auxiliary star and the first component of the observation target point. The second value is obtained by squaring the difference between the second component of the auxiliary star and the second component of the observation target point. The third value is obtained by squaring the difference between the third component of the auxiliary star and the third component of the observation target point. The slope distance length is obtained by summing the first value, the second value, and the third value and then taking the square root.

8. The method according to claim 1, characterized in that, Determining the baseline vector based on the slant range length includes: Based on the slant range length and the slant range at the scene center, determine the slant range difference between the primary star and the secondary star; Based on the slant range difference, the reception time of the auxiliary satellite receiving the scattered echo from the observation target point is determined; The baseline vector is determined based on the reception time.

9. The method according to claim 8, characterized in that, The determination of the slant range difference between the primary star and the secondary star based on the slant range length and the scene center slant range includes: The slant distance difference is obtained by subtracting the slant distance length from the slant distance at the scene center. The determination of the reception time of the auxiliary satellite receiving the scattered echo from the observed target point based on the slant range difference includes: The receiving time is obtained by summing the slant range difference (divided by the speed of light) with the time in the sampled auxiliary star state vector.

10. The method according to claim 8, characterized in that, Determining the baseline vector based on the reception time includes: Based on the state vectors of the receiving time and the azimuth time of the auxiliary star, the receiving position vector of the auxiliary star receiving the scattered echo from the observation target point is obtained by interpolation; The baseline vector is determined based on the received position vector and the sampled primary star state vector.

11. The method according to claim 10, characterized in that, The state vector based on the reception time and the azimuth time of the auxiliary star is interpolated to obtain the reception position vector of the auxiliary star receiving the scattered echo from the observation target point, including: Based on the receiving time, the position vector in the state vector of the auxiliary star's azimuth time is interpolated on the first coordinate axis, the second coordinate axis, and the third coordinate axis respectively to obtain the first position interpolation, the second position interpolation, and the third position interpolation; The received position vector is obtained based on the first position interpolation, the second position interpolation, and the third position interpolation.

12. The method according to claim 10, characterized in that, Determining the baseline vector based on the received position vector and the sampled primary satellite state vector includes: The baseline vector is obtained by subtracting the received position vector from the position vector in the sampled primary star state vector.

13. The method according to any one of claims 1 to 3, characterized in that, After determining the baseline vectors of the primary and secondary stars based on the sampled primary star state vector and the sampled secondary star state vector, the method further includes: Based on the sampled primary star state vector, a coordinate system with the primary star as a reference is determined; Determine the three coordinate components of the baseline vector in the three coordinate axes of the coordinate system; The coefficients of the quadratic term are fitted to the three coordinate components respectively to obtain the constant term and the coefficients of the linear term; Output the constant term and the coefficients of the linear term to the target file.

14. A distributed SAR interferometric baseline estimation device, characterized in that, The device includes: The synchronization module is used to synchronize the imaging azimuth and time axis of the primary star and the imaging azimuth and time axis of the secondary star, so as to obtain the synchronized imaging azimuth and time axis of the primary star and the synchronized imaging azimuth and time axis of the secondary star. The first determining module is used to determine the state vector of the primary star's azimuth time and the state vector of the secondary star's azimuth time based on the synchronized primary star imaging azimuth time axis and the synchronized secondary star imaging azimuth time axis. The sampling module is used to sample the state vector of the primary star's azimuth time and the state vector of the secondary star's azimuth time to obtain the sampled primary star state vector and the sampled secondary star state vector. The second determining module is used to determine the baseline vectors of the primary star and the secondary star based on the sampled primary star state vector and the sampled secondary star state vector. The second determining module is further configured to acquire the scene center slant range, Doppler center frequency, and imaging parameters; based on the sampled primary star state vector, the scene center slant range, the Doppler center frequency, and the imaging parameters, obtain the position vector of the observation target point through range-Doppler RD positioning; based on the position vector of the observation target point and the sampled secondary star state vector, determine the slant range length between the secondary star and the observation target point; and based on the slant range length, determine the baseline vector.

15. A distributed SAR system, employing the steps of the method according to any one of claims 1 to 13, characterized in that, The system includes a primary star and a secondary star; The primary satellite emits radar signals that illuminate the target area. The primary star and the secondary star receive echo signals scattered from the target region; The primary satellite and the secondary satellite alternately transmit synchronization signals during idle periods outside the time window for receiving the echo signals; The primary satellite and the secondary satellite respectively send the synchronization data corresponding to the synchronization signal to the computer at the ground station.

16. A computer device comprising a memory and a processor, the memory storing a computer program executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method according to any one of claims 1 to 13.

17. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program performs the steps of the method according to any one of claims 1 to 13.

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