CN113221062B - High-frequency motion error compensation method of small unmanned aerial vehicle-mounted BiSAR system - Google Patents
High-frequency motion error compensation method of small unmanned aerial vehicle-mounted BiSAR system Download PDFInfo
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Abstract
According to the high-frequency motion error compensation algorithm of the small-sized unmanned airborne BiSAR system, the azimuth error phases of N strong point information are obtained by performing coarse imaging on echo data of the small-sized unmanned airborne BiSAR system, wherein N is a positive integer; carrying out Fourier transform on the azimuth error phase of the strong point information to obtain time-frequency information of the strong point information, and filtering low-frequency signal components of the time-frequency information of the strong point information according to time-frequency ridges of the time-frequency information of the strong point information to obtain high-frequency signal components of the time-frequency information; carrying out inverse radon transformation on the high-frequency signal component to obtain the maximum frequency deviation of the high-frequency signal component, and error estimation values of the frequency and the initial phase; obtaining estimated values of frequency and an initial phase by using a weighted average algorithm, and obtaining an estimated value of maximum frequency deviation by using a least square method; the high-frequency signal component is compensated in the distance direction on the basis of the estimated value of the high-frequency signal component. The space-variant motion compensation and the low-frequency motion compensation of the high-frequency error of the BiSAR system are realized, and the good focusing of the SAR image is realized.
Description
Technical Field
The invention belongs to the technical field of bistatic synthetic aperture radars, and particularly relates to a high-frequency motion error compensation method of a small unmanned aerial vehicle-mounted BiSAR (bistatic synthetic aperture radar) system.
Background
The small unmanned aerial vehicle platform has the characteristics of light weight, small size, flexibility, low price and the like, and is used as a carrier platform by more and more SAR systems. In the working process of the SAR system, the small unmanned aerial vehicle platform is easy to be interfered by the external environment due to light weight, and the flight track and the flight attitude of the small unmanned aerial vehicle platform can be greatly changed in the movement process.
Most carrier platforms of the traditional SAR system are large-scale transport planes or satellites, the size and the weight of the platforms are large, and the stability is good, so that motion errors mainly come from the precision of an inertial navigation system and are reflected as low-frequency errors. However, unlike conventional SAR system carriers, for small unmanned airborne systems, the platform introduces not only low-frequency motion errors but also high-frequency motion errors due to the flutter of the aircraft attitude during the motion process.
Because the small unmanned aerial vehicle has great limitation on load and power consumption, a high-precision inertial navigation system cannot be installed, and therefore the high-frequency motion cannot be recorded, and high-frequency errors of tracks are caused. In addition, the BiSAR system receiving and transmitting platform is separately arranged, and high-frequency motion errors can be introduced, so that the error influence is more complicated. When the working frequency band of the system is high, such as Ku band, the high frequency jitter of millimeter-scale amplitude will have a great influence on the imaging result. Most of the existing motion compensation algorithms only consider low-frequency errors under common conditions, however, the high-frequency error motion compensation algorithm based on high-precision inertial navigation information is difficult to be provided by a light and small SAR platform. Meanwhile, an algorithm for compensating the multi-component motion error usually adopts a direct search mode to estimate error parameters, but a large amount of time and operation resources are needed, and real-time processing of inertial navigation data is difficult to realize. Therefore, a more effective high-frequency motion error compensation algorithm is urgently needed for the small unmanned aerial vehicle-based BiSAR system.
Disclosure of Invention
The invention overcomes one of the defects of the prior art, provides the high-frequency motion error compensation method of the small-sized unmanned airborne BiSAR system, can perform space-variant motion compensation and low-frequency motion compensation of high-frequency errors on echo data of the small-sized unmanned airborne BiSAR system, and realizes good focusing of SAR images.
According to one aspect of the disclosure, the invention provides a high-frequency motion error compensation method of a small unmanned aerial vehicle-mounted BiSAR system, which comprises the following steps:
acquiring echo data of the small unmanned aerial vehicle-mounted BiSAR system, performing rough imaging on the echo data to obtain N strong point information, and extracting azimuth error phases of the N strong point information, wherein N is a positive integer;
carrying out Fourier transform on azimuth error phases of the N strong point information to obtain time-frequency information of the N strong point information, and filtering low-frequency signal components of the time-frequency information of the N strong point information according to time-frequency ridges of the time-frequency information of the N strong point information to obtain high-frequency signal components of the time-frequency information of the N strong point information;
carrying out inverse radon transformation on the high-frequency signal components of the time-frequency information of the N strong point information to obtain the maximum frequency deviation, the error estimation values of the frequency and the initial phase of the high-frequency signal components of the N strong point information;
obtaining estimated values of the frequency and the initial phase of the high-frequency signal components of the N strong point information by using a weighted average algorithm, and obtaining an estimated value of the maximum frequency offset of the high-frequency signal components of the N strong point information by using a least square method;
and compensating the high-frequency signal components of the N strong point information in the distance direction according to the maximum frequency deviation, the frequency and the estimated value of the initial phase of the high-frequency signal components of the N strong point information to obtain the echo signal of the small unmanned airborne BiSAR system after high-frequency error compensation.
In one possible implementation, the azimuth error phase is a sum of sinusoidal frequency modulated signals.
In a possible implementation manner, the filtering, according to the time-frequency ridge of the time-frequency information of the N strong point information, the low-frequency signal component of the time-frequency information of the N strong point information to obtain the high-frequency signal component of the time-frequency information of the N strong point information includes:
obtaining the bandwidth of the time-frequency information of the N strong point information according to the estimated value of the time-frequency ridge of the time-frequency information of the N strong point information;
and filtering the low-frequency signal component of the time-frequency information by using a band elimination filter based on the bandwidth of the time-frequency information to obtain the high-frequency signal component of the time-frequency information of the N strong point information.
In a possible implementation manner, the filtering, by using a band elimination filter, a low-frequency signal component of the time-frequency information based on a bandwidth of the time-frequency information to obtain a high-frequency signal component of the time-frequency information of the N strong point information includes:
and setting a stopband of the band elimination filter according to the bandwidth of the time-frequency information of each strong point information to separate the time-frequency information of each strong point information, and filtering the low-frequency signal component of the time-frequency information of each strong point information to obtain the high-frequency signal component of the time-frequency information.
In one possible implementation, the band-stop of the band-stop filter is twice the bandwidth of the time-frequency information.
According to the high-frequency motion error compensation method of the small-sized unmanned aerial vehicle-mounted BiSAR system, echo data of the small-sized unmanned aerial vehicle-mounted BiSAR system are obtained, rough imaging is carried out on the echo data to obtain N strong point information, and the azimuth error phase of the N strong point information is extracted, wherein N is a positive integer; carrying out Fourier transform on azimuth error phases of the N strong point information to obtain time-frequency information of the N strong point information, and filtering low-frequency signal components of the time-frequency information of the N strong point information according to time-frequency ridges of the time-frequency information of the N strong point information to obtain high-frequency signal components of the time-frequency information of the N strong point information; carrying out inverse radon transformation on the high-frequency signal components of the time-frequency information of the N strong point information to obtain the error estimation values of the maximum frequency deviation, the frequency and the initial phase of the high-frequency signal components of the N strong point information; obtaining estimated values of the frequency and the initial phase of the high-frequency signal components of the N strong point information by using a weighted average algorithm, and obtaining an estimated value of the maximum frequency deviation of the high-frequency signal components of the N strong point information by using a least square method; and compensating the high-frequency signal components of the N strong point information in the distance direction according to the maximum frequency deviation, the frequency and the estimated value of the initial phase of the high-frequency signal components of the N strong point information to obtain the echo signal of the small unmanned airborne BiSAR system after high-frequency error compensation. The space-variant motion compensation and the low-frequency motion compensation of high-frequency errors of echo data of the small unmanned aerial vehicle-mounted BiSAR system are realized, and the good focusing of an SAR image is realized.
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The accompanying drawings are included to provide a further understanding of the technology or prior art of the present application and are incorporated in and constitute a part of this specification. The drawings expressing the embodiments of the present application are used for explaining the technical solutions of the present application, and should not be construed as limiting the technical solutions of the present application.
Fig. 1 shows a flow chart of a high frequency motion error compensation method of a small-scale unmanned airborne BiSAR system according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram illustrating a time-frequency analysis result of an azimuth error phase of echo data of a small-sized unmanned airborne BiSAR system according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram illustrating a time-frequency ridge extraction result of an azimuth error phase of echo data of a small-sized unmanned airborne BiSAR system according to an embodiment of the present disclosure;
figure 4 shows a spectral diagram of the time-frequency ridges of the azimuthal error phase of echo data for a small unmanned airborne BiSAR system according to an embodiment of the disclosure;
figure 5 shows a band-stop filter schematic diagram of spectral filtering of the time-frequency ridges of the azimuth error phase of the echo data of the small-scale unmanned airborne BiSAR system to low-frequency signal components, according to an embodiment of the present disclosure;
fig. 6 is a schematic diagram showing an estimated value of a frequency-spectrum filtered low-frequency signal component of a time-frequency ridge of an azimuth error phase of echo data of a small-sized unmanned airborne bistar system according to an embodiment of the present disclosure;
fig. 7 is a schematic diagram illustrating the results of high-frequency sinusoidal signal error amplitude space-variant analysis of azimuth error phase of echo data of a small-scale unmanned airborne BiSAR system according to an embodiment of the present disclosure;
fig. 8 shows an experimental scenario of a high-frequency motion error compensation method of a small-scale unmanned airborne BiSAR system according to an embodiment of the present disclosure;
figure 9 shows a schematic diagram of the results of a prior art imaging of the echo signals of a small unmanned airborne BiSAR system using the NCS algorithm;
fig. 10 is a diagram illustrating the imaging result of the high-frequency motion error compensation method of the small-sized unmanned airborne BiSAR system according to an embodiment of the present disclosure.
Detailed Description
The following detailed description of the embodiments of the present invention will be provided with reference to the accompanying drawings and examples, so that how to apply the technical means to solve the technical problems and achieve the corresponding technical effects can be fully understood and implemented. The embodiments and the features of the embodiments can be combined without conflict, and the technical solutions formed are all within the scope of the present invention.
Additionally, the steps illustrated in the flow charts of the figures may be performed in a computer such as a set of computer-executable instructions. Also, while a logical order is shown in the flow diagrams, in some cases, the steps shown or described may be performed in an order different than here.
Fig. 1 shows a flow chart of a high-frequency motion error compensation method of a small-sized unmanned aerial vehicle-mounted BiSAR system according to an embodiment of the disclosure. As shown in fig. 1, the method may include:
step S1: obtaining echo data of the small unmanned aerial vehicle-mounted BiSAR system, carrying out coarse imaging on the echo data to obtain N strong point information, and extracting azimuth error phases of the N strong point information, wherein N is a positive integer.
Firstly, inertial navigation data of the small-sized unmanned airborne BiSAR system are obtained through an inertial navigation sensor of the small-sized unmanned airborne BiSAR system, and then distance pulse compression and distance walk correction are carried out on original echo data of the inertial navigation data of the small-sized unmanned airborne BiSAR system. When the small unmanned airborne bistar system has high-frequency sinusoidal motion errors, the azimuth error phase can be expressed in the form of a sum of sinusoidal frequency modulated Signals (SFM).
The echo signal containing the sine error of the small unmanned airborne BiSAR system can be expressed as follows:
where u is the azimuth time, λ is the wavelength, a em Is the maximum frequency offset, f, of the mth error component em Is the modulation frequency of the mth error component,
is the initial phase of the mth error component.
The sinusoidal error form of the azimuth error phase may be:
under the condition of the small unmanned aerial vehicle-mounted BiSAR system, high-frequency errors are introduced into the receiving and transmitting platforms (namely a transmitter platform and a receiver platform) of the small unmanned aerial vehicle-mounted BiSAR system, and the high-frequency errors can comprise two groups of independent SFM components. According to equations (1) and (2), the phase of the high frequency sinusoidal error (azimuth error phase) may be:
according to the formula (1) -formula (3), the echo data of the small unmanned aerial vehicle-mounted BiSAR system is subjected to coarse imaging to obtain the azimuth error phase of N strong point information
Step S2: and carrying out Fourier transform on the azimuth error phase of the N strong point information to obtain the time frequency information of the N strong point information, and filtering the low-frequency signal component of the time frequency information of the N strong point information according to the time frequency ridge of the time frequency information of the N strong point information to obtain the high-frequency signal component of the time frequency information of the N strong point information.
According to equation (3) in step S1, the instantaneous doppler frequency under the high-frequency sinusoidal error of the time-frequency information of the strong point information can be represented as:
to obtain the instantaneous Doppler frequency f u Is estimated by
The time-frequency information of the N strong point information needs to be analyzed. For example, short-time fourier transform may be performed on the azimuth error phases of the N strong point information extracted in step S1 to obtain N time-frequency relationship curves, and interpolation processing may be performed on the N time-frequency relationship curves, so that frequency changes of the N strong points can be observed more significantly. As shown in fig. 2, the time-frequency analysis result of the azimuth error phase of the echo data of the small unmanned airborne BiSAR system shows that each time on the time-frequency analysis result curve corresponds to a certain frequency width. For the convenience of analysis, the frequency value with the maximum amplitude corresponding to each moment can be reserved by extracting the time-frequency ridge of the time-frequency information of the strong point information, and the rest frequencies are set to be zero. Wherein, the time-frequency ridge extraction can be expressed as:
wherein u is the number of slow time points, f is the frequency, and the time-frequency ridge extraction result is shown in figure (3).
After the time-frequency ridge is extracted, the estimated value of the time-frequency ridge can be obtained through the formula (6)
Evaluating the time-frequency ridge>
The sequence subtracts its mean value and shifts the doppler center frequency to zero.
To pair
Fourier transform is carried out to obtain
In an example, the bandwidth of the time-frequency information of the N strong point information may be obtained according to an estimated value of a time-frequency ridge of the time-frequency information of the N strong point information;
and filtering the low-frequency signal component of the time-frequency information by using a band elimination filter based on the bandwidth of the time-frequency information to obtain the high-frequency signal component of the time-frequency information of the N strong point information.
The stopband of the band elimination filter can be set according to the bandwidth of the time-frequency information of each strong point information to separate the time-frequency information of each strong point information, and the low-frequency signal component of the time-frequency information of each strong point information is filtered to obtain the high-frequency signal component of the time-frequency information.
Fig. 5 shows a band elimination filter schematic diagram of the frequency spectrum of the time-frequency ridge of the azimuth error phase of the echo data of the small-sized unmanned airborne BiSAR system for filtering out low-frequency signal components according to an embodiment of the disclosure.
For the parameter estimation of each SFM signal (sinusoidal fm signal), the frequency spectrum F of the time-frequency information of the strong point information can be estimated using a band-stop filter u Separation is carried out. When separating each SFM signal, the passband center frequency of the band-stop filter may be set to the local maximum according to the bandwidth of the time-frequency information of each strong point information, and the band-stop (passband bandwidth) of the band-stop filter is set to be twice as large as the bandwidth of the SFM signal, that is, 2/T, so as to ensure that the interference of the low-frequency signal component is filtered while each SFM signal information is retained, and obtain the high-frequency signal component of each SFM signal (the high-frequency signal component of the time-frequency information of the strong point information), where the bandwidth of the band-stop filter is set as shown in fig. 5. By adjusting the passband position and the bandstop of the bandstop filter, the inverse Fourier transform of the signal of each SFM signal after the low-frequency signal component is filtered can be realized, and the time-frequency relation curve of each SFM signal is obtained.
And step S3: and carrying out inverse radon transformation on the high-frequency signal components of the time-frequency information of the N strong point information to obtain the error estimation values of the maximum frequency deviation, the frequency and the initial phase of the high-frequency signal components of the N strong point information.
The inverse radon transformation can be performed on the high-frequency signal component of the SFM signal, and the time-frequency relation curve of the SFM signal has good convergence. The time-frequency relation curve of each SFM signal high-frequency signal component can be converted into a point in the domain through inverse radon, and the position of the point determines the parameter information of the SFM signal, so that the error estimation values of the maximum frequency deviation, the frequency and the initial phase of the SFM signal high-frequency signal component are obtained. Therefore, the frequency of the SFM signal can be estimated by the degree of aggregation of the inverse radon transform result of the high-frequency signal components of the SFM signal.
For each SFM signal component, based on it in the frequency spectrum F u Determines the frequency range [ f ] of the SFM signal min f max ]Let the frequency f of each SFM signal component e Is at min f max ]Are uniformly distributed. Let phi =2 pi f e ' u, inverse ra for different values of phidon transforms TFR (. Phi.,. F). Examining all the radon transform results by using image entropy, the smaller the image entropy, the more concentrated the convergence points are, and f 'which will minimize the image entropy' e As f e Is estimated by
The maximum value position of the inverse radon transform result of the SFM signal is recorded as (x, y), and then the maximum frequency offset and the estimated value of the initial phase of the SFM signal are obtained
And &>
Can be as follows:
and step S4: and obtaining estimated values of the frequency and the initial phase of the high-frequency signal components of the N strong point information by using a weighted average algorithm, and obtaining an estimated value of the maximum frequency deviation of the high-frequency signal components of the N strong point information by using a least square method.
Fig. 6 is a schematic diagram illustrating an estimated value of a frequency-spectrum filtered low-frequency signal component of a time-frequency ridge of an azimuth error phase of echo data of a small-sized unmanned airborne BiSAR system according to an embodiment of the present disclosure.
The N strong points obtained in step S1 are subjected to the parameter estimation in steps S2 to S4, so that N sets of estimated values can be obtained for each of the M high-frequency sinusoidal error components, as shown in fig. 6. Wherein, the first and the second end of the pipe are connected with each other,
and &>
Respectively representing the maximum frequency deviation, the frequency and the initial phase estimation value of the Mth high-frequency sinusoidal error component obtained by the echo information of the strong point N.
The 1 st high frequency sinusoidal error component is taken as an example to explain how to estimate the estimated results of the N sets of frequency point parameter estimated values from the N strong points. Because the error frequency and the initial phase of the high-frequency sinusoidal error component have no space-variant property, a weighted average method can be adopted for estimation, and the precision of frequency point parameter estimation is related to the signal-to-noise ratio of N strong point information, namely the higher the signal-to-noise ratio of the strong point information is, the higher the precision of the frequency point estimation value of the high-frequency sinusoidal error component is. Therefore, the weighting factor of the frequency point estimation value precision of the high-frequency sinusoidal error component can adopt the signal-to-noise ratio at the strong point target position, namely
w＝[SNR(P 1 ),SNR(P 2 )L SNR(P N )] T
The error frequency and the error estimated value of the initial phase of the high-frequency signal component of the N strong point information can be obtained by equation (9).
The vector H of 1 multiplied by 3 is selected to represent the amplitude and the spatial orientation of a high-frequency sinusoidal signal jitter of the small unmanned aerial vehicle platform, namely H = [ ] x ,H y ,H z ] T And (5) formula (10).
If the error amplitude a at a certain target point e Is a linear function of the platform dither amplitude H that causes the high frequency motion error. Taking a transmitter motion platform of a small unmanned aerial vehicle-mounted BiSAR system with certain motion error parameters as an example, the space-variant relation of the error amplitude can be expressed as follows:
ν＝H T T ×Β-a r in the formula (11),
wherein the content of the first and second substances,
in the formula, H T Subscript of (a) indicates the high frequencyThe motion error comes from the transmitter,. Phi TP Represents the unit direction vector of the transmitter pointing at the target point P, and ν is the residual error.
Sinusoidal jitter amplitude conversion of transmitter motion platform to find a set of H T Minimizing the residual error v, i.e.
The high-frequency motion error H of the transmitter is estimated by adopting a weighted least square method according to the signal-to-noise ratio intensity consideration of different target points T The weighted least squares estimation (WLS) result can be obtained as:
wherein, W is a diagonal weighting coefficient matrix, and the weighting coefficient is the signal-to-noise ratio of each target point.
That is, the diagonal weighting coefficient matrix W is:
W＝diag([SNR(P 1 ),SNR(P 2 )L SNR(P N )]) Formula (15).
According to the estimation process, whether each high-frequency sinusoidal error comes from a transmitter platform or a receiver platform can be known, then a group of motion errors are calculated by combining the group of errors and the position parameters of the transmitter platform, and further the motion track of the transmitter platform is compensated, and the imaging result is improved. And processing each group of errors to obtain an estimated value of a high-frequency sinusoidal error component of the small-sized unmanned aerial vehicle-mounted BiSAR system.
Suppose that the transmitter platform introduces M 1 A high-frequency sinusoidal error component, the receiver platform introducing M 2 High frequency sinusoidal error components, then the estimation process can obtain the high frequency sinusoidal jitter errors of the transmitter platform and the receiver platform as:
wherein H Tm1 、f m1 、
Respectively representing the amplitude, frequency and initial phase, H, of the m1 st high-frequency sinusoidal error introduced by the transmitter stage Rm2 、f m2 、/>
Respectively representing the amplitude, frequency and initial phase of the m2 high frequency sinusoidal error introduced by the receiver stage.
Step S5: and compensating the high-frequency signal components of the N strong point information in the distance direction according to the maximum frequency deviation, the frequency and the estimated value of the initial phase of the high-frequency signal components of the N strong point information to obtain the echo signal of the small unmanned aerial vehicle-mounted BiSAR system after high-frequency sinusoidal error compensation.
Fig. 7 is a schematic diagram illustrating the analysis result of the high-frequency sinusoidal signal error amplitude space-variant of the azimuth error phase of the echo data of the small-sized unmanned airborne bistar system according to an embodiment of the disclosure.
Under the large squint configuration of the small unmanned aerial vehicle-mounted BiSAR system, the space-variant property of the high-frequency motion error is not negligible. As shown in fig. 7, as can be seen from the result of analyzing the space-variant of the high-frequency motion errors of the transmitter platform and the receiver platform of the small-sized unmanned airborne BiSAR system, only the echo signal of the small-sized unmanned airborne BiSAR system needs to be compensated for the high-frequency sinusoidal error in the upward distance.
In the distance direction, the compensated phase within each distance gate is calculated from the center point position P within that distance gate. The compensation factor may be
The echo signal obtained by multiplying the formula (17) by the formula (1) after high-frequency sinusoidal error compensation of the small unmanned airborne BiSAR system is as follows:
After the high-frequency motion error compensation of the small-sized unmanned aerial vehicle-mounted BiSAR system is completed, the traditional motion error compensation algorithm is used for compensating the residual low-frequency phase error, so that the small-sized unmanned aerial vehicle-mounted BiSAR image can be well focused, and the high-frequency motion error compensation based on the small-sized unmanned aerial vehicle-mounted BiSAR system can be completed.
For example, the following steps are carried out:
fig. 8 shows an experimental scenario diagram of a high-frequency motion error compensation method of a small-scale unmanned airborne bistar system according to an embodiment of the disclosure; figure 9 shows a schematic diagram of the results of a prior art imaging of the echo signals of a small unmanned airborne BiSAR system using the NCS algorithm; fig. 10 is a diagram illustrating the imaging result of the high-frequency motion error compensation method of the small-sized unmanned airborne BiSAR system according to an embodiment of the present disclosure.
As shown in fig. 8, the experimental site is selected from suburbs and includes a transponder, a plot of housing area and three dirt roads. And selecting a radar with Ku waveband, and respectively carrying a receiving radar and a transmitting radar on two small unmanned aerial vehicles to verify the effect of the high-frequency motion compensation method provided by the invention. Wherein, unmanned aerial vehicle is the stationary vane, and the load is 3kg.
In imaging experiments, some points within the room area may constitute strong reflection points. The experimental parameters are shown in table 1 below:
table 1 examples experimental parameters
| Transmitter range | 1.37km | Distance of action of receiver | 1.57km |
| Transmitter height | 471m | Receiver height | 370m |
| Transmitter squint angle | 65.8° | Squint angle of receiver | 64.2° |
| Transmitter speed | 18.22m/s | Receiver speed | 21.17m/s |
| Transmitter acceleration | 0.84m/s 2 | Acceleration of receiver | 0.42m/s 2 |
| Transmitter jerk | 0.14m/s 3 | Receiver acceleration | 0.11m/s 3 |
| Wavelength of light | 0.019m | Bandwidth of | 50MHz |
Firstly, the amplitude and the phase of the echo signal are corrected by using the direct wave signal, then preliminary imaging is performed by using an NCS algorithm, the imaging result is shown in FIG. 9, and the imaging result is data corrected to an actual ground distance plane. As shown in fig. 9, strong scattering points in the transponder and the room area have obvious false targets, and other scene information in the test scene is fuzzy. Under the condition of a small unmanned aerial vehicle-mounted BiSAR system, high-frequency sinusoidal motion errors are introduced into a transmitter platform and a receiver platform, the number of false targets is large, and the mutual relation is complex. Meanwhile, in the Ku waveband, the wavelength of the small unmanned aerial vehicle-mounted BiSAR system is short, so that small-amplitude jitter can cause great influence.
After the high-frequency sinusoidal error of the small-sized unmanned airborne BiSAR system is compensated by the high-frequency motion compensation method provided by the patent, the residual phase is compensated by the traditional motion compensation method, the finally obtained imaging result is shown in figure 10, after the small-sized unmanned airborne BiSAR system passes through the high-frequency sinusoidal error method, the false target caused by the high-frequency sinusoidal error is compensated, the strong scattering point focusing effect in the imaging result is good as shown in figure 10, the outline information in a room area is clear, and the outline of a path is obvious.
According to the high-frequency motion error compensation method of the small-sized unmanned aerial vehicle-mounted BiSAR system, echo data of the small-sized unmanned aerial vehicle-mounted BiSAR system are obtained, rough imaging is carried out on the echo data to obtain N strong point information, and the azimuth error phase of the N strong point information is extracted, wherein N is a positive integer; performing Fourier transform on the azimuth error phases of the N strong point information to obtain time-frequency information of the N strong point information, and filtering low-frequency signal components of the time-frequency information of the N strong point information according to time-frequency ridges of the time-frequency information of the N strong point information to obtain high-frequency signal components of the time-frequency information of the N strong point information; carrying out inverse radon transformation on the high-frequency signal components of the time-frequency information of the N strong point information to obtain the error estimation values of the maximum frequency deviation, the frequency and the initial phase of the high-frequency signal components of the N strong point information; obtaining estimated values of the frequency and the initial phase of the high-frequency signal components of the N strong point information by using a weighted average algorithm, and obtaining an estimated value of the maximum frequency offset of the high-frequency signal components of the N strong point information by using a least square method; and compensating the high-frequency signal components of the N strong point information in the distance direction according to the maximum frequency deviation, the frequency and the estimated value of the initial phase of the high-frequency signal components of the N strong point information to obtain the echo signal of the small unmanned airborne BiSAR system after high-frequency error compensation. The space-variant motion compensation and the low-frequency motion compensation of high-frequency errors are carried out on the echo data of the small unmanned aerial vehicle BiSAR system, the SAR image is well focused, the parameter estimation result is more accurate, the anti-interference capability is higher, and the operation efficiency is improved compared with that of the traditional SFM signal parameter estimation algorithm.
Although the embodiments of the present invention have been described above, the above descriptions are only for the convenience of understanding the present invention, and are not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (3)
1. A high-frequency motion error compensation method of a small unmanned aerial vehicle-mounted BiSAR system is characterized by comprising the following steps:
acquiring echo data of the small unmanned aerial vehicle-mounted BiSAR system, performing coarse imaging on the echo data to obtain N strong point information, and extracting azimuth error phases of the N strong point information, wherein N is a positive integer;
firstly, acquiring inertial navigation data of the small-sized unmanned aerial vehicle-mounted BiSAR system through an inertial navigation sensor of the small-sized unmanned aerial vehicle-mounted BiSAR system, and then performing range-oriented pulse compression and range walk correction on original echo data of the inertial navigation data of the small-sized unmanned aerial vehicle-mounted BiSAR system; when the small unmanned aerial vehicle-mounted BiSAR system has a high-frequency sinusoidal motion error, the azimuth error phase is the sum of sinusoidal frequency modulation signals;
the echo signal containing the sine error of the small unmanned aerial vehicle-mounted BiSAR system can be expressed as follows:
where u is the azimuth time, λ is the wavelength, a em Is the maximum frequency offset, f, of the mth error component em Is the modulation frequency of the m-th error component,
is the initial phase of the mth error component; Δ R (u, P) is the error to be compensated, P is the target position, R e Bistatic distance error, V, introduced by low frequency motion error e Is a velocity error, A, introduced by a low frequency motion error e Is the acceleration error introduced by the low frequency motion error, and G (u) is a window function of azimuth time;
the sinusoidal error of the azimuth error phase is:
under the condition of the small-sized unmanned aerial vehicle-mounted BiSAR system, high-frequency errors are introduced into both a transmitter platform and a receiver platform of the small-sized unmanned aerial vehicle-mounted BiSAR system, and the high-frequency errors comprise two groups of independent SFM components; according to the formula (1) and the formula (2), the azimuth error phase of the nth strong point information is:
wherein the content of the first and second substances,
carrying out Fourier transform on azimuth error phases of the N strong point information to obtain time-frequency information of the N strong point information; filtering the low-frequency signal component of the time-frequency information of the N strong point information according to the time-frequency ridge of the time-frequency information of the N strong point information to obtain the high-frequency signal component of the time-frequency information of the N strong point information, wherein the specific mode is as follows: obtaining the bandwidth of the time-frequency information of the N strong point information according to the estimated value of the time-frequency ridge of the time-frequency information of the N strong point information; filtering the low-frequency signal component of the time-frequency information by using a band elimination filter based on the bandwidth of the time-frequency information to obtain the high-frequency signal component of the time-frequency information of the N strong point information;
wherein, the time-frequency ridge extraction is expressed as:
wherein f is frequency;
carrying out inverse radon transformation on the high-frequency signal components of the time-frequency information of the N strong point information to obtain the error estimation values of the maximum frequency deviation, the frequency and the initial phase of the high-frequency signal components of the N strong point information;
obtaining estimated values of the frequency and the initial phase of the high-frequency signal components of the N strong point information by using a weighted average algorithm, and obtaining an estimated value of the maximum frequency offset of the high-frequency signal components of the N strong point information by using a least square method;
and compensating the high-frequency signal components of the N strong point information in the distance direction according to the maximum frequency deviation, the frequency and the estimated value of the initial phase of the high-frequency signal components of the N strong point information to obtain the echo signal of the small unmanned airborne BiSAR system after high-frequency error compensation.
2. The high-frequency motion error compensation method according to claim 1, wherein the filtering, by using a band-elimination filter, the low-frequency signal component of the time-frequency information based on the bandwidth of the time-frequency information to obtain the high-frequency signal component of the time-frequency information of the N strong point information includes:
and setting a stop band of the band elimination filter according to the bandwidth of the time-frequency information of each strong point information to separate the time-frequency information of each strong point information, and filtering the low-frequency signal component of the time-frequency information of each strong point information to obtain the high-frequency signal component of the time-frequency information.
3. The high frequency motion error compensation method of claim 1 or 2, wherein the band-stop of the band-stop filter is twice the bandwidth of the time-frequency information.
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