An improved PGA algorithm-based large downward-looking angle UAV SAR motion compensation method

Abstract

The application relates to a large downward-viewing-angle unmanned aerial vehicle (UAV) SAR motion compensation method based on an improved PGA algorithm, which comprises the following steps: performing inertial navigation coarse compensation and distance compression on echo signals to obtain first echo signals; performing non-space-variant phase error estimation on the first echo signals to obtain Doppler frequency modulation; performing non-space-variant range migration correction and coarse compensation of phase error on radar data according to the non-space-variant phase error to obtain a non-space-variant phase function after coarse compensation; estimating space-variant phase error, performing compensation processing according to the space-variant phase error, and obtaining a compensated space-variant phase function; splicing the compensated space-variant phase functions of all sub-apertures to obtain a full-aperture phase error; removing an overall linear component in the full-aperture phase error through linear fitting to obtain radar data after motion compensation; and performing imaging on the radar data after motion compensation by using a UAV SAR imaging algorithm to obtain imaging results. The application can accurately correct phase errors caused by distance space variation.

Description

Technical Field

[0001] This invention belongs to the field of radar technology and relates to a large down-view UAV SAR motion compensation method based on an improved PGA algorithm. Background Technology

[0002] Synthetic Aperture Radar (SAR), as an active ground detection system, accumulates a certain amount of time through the relative motion between the platform and the radar. It then coherently processes the echo signals received by the radar at different locations to obtain a two-dimensional image of the target, allowing people to see a true image of the target. It has the characteristics of working around the clock and in all weather conditions.

[0003] Compared to optical imaging, SAR (Synthetic Aperture Radar) is not limited by external factors such as weather conditions, thus SAR technology is widely used in both military and civilian fields. In military applications, SAR can effectively conduct battlefield reconnaissance, terrain-matching guidance, and precision weapon delivery; in civilian applications, SAR can be used for low-resolution exploration and terrain mapping. Currently, SAR has experienced rapid development in both military and civilian fields. Today, SAR is widely used in vehicle-mounted, space-based, missile-borne, and airborne applications. Unmanned aerial vehicle (UAV)-borne SAR is widely used due to its rapid and maneuverable response capabilities, enabling missions even in dangerous battlefield environments with zero personnel casualties. UAVs are a crucial combat platform for achieving zero personnel casualties.

[0004] In UAV-based large-angle SAR imaging, motion errors increase with the downward angle of view, and the impact of third-order range-varying phase errors cannot be ignored. Therefore, addressing these third-order range-varying phase errors has become an urgent problem to solve. Summary of the Invention

[0005] To address the aforementioned problems in the existing technology, this invention provides a large down-view UAV SAR motion compensation method based on an improved PGA algorithm. The technical problem to be solved by this invention is achieved through the following technical solution:

[0006] This invention provides a large down-view UAV SAR motion compensation method based on an improved PGA algorithm, the UAV SAR motion compensation method comprising:

[0007] Step 1: Receive the synthetic aperture radar (SAR) echo signal from the UAV, perform inertial navigation coarse compensation and range compression on the echo signal to obtain the first echo signal;

[0008] Step 2: Use an image offset algorithm to perform non-variable phase error estimation on the first echo signal to obtain the Doppler modulation frequency;

[0009] Step 3: Obtain the non-space-varying phase error based on the change in azimuth modulation frequency and the change in synthetic aperture time in the Doppler modulation frequency. Then, perform non-space-varying range migration correction and coarse compensation for phase error on the radar data based on the non-space-varying phase error to obtain the coarsely compensated non-space-varying phase function.

[0010] Step 4: Estimate the phase error of the air transformer using the improved phase-weighted PGA algorithm, and perform compensation processing based on the phase error of the air transformer to obtain the compensated air transformer phase function.

[0011] Step 5: The compensated spatially varying phase functions of all sub-apertures are spliced ​​together to obtain the phase error of the entire aperture. The overall linear component in the phase error of the entire aperture is removed by linear fitting to obtain the motion-compensated radar data.

[0012] Step 6: Use the UAV SAR imaging algorithm to image the motion-compensated radar data to obtain SAR imaging results.

[0013] In one embodiment of the invention, the first echo signal is represented as:

[0014] s0(t)=x(t)exp(jπγt 2 )

[0015] Where s0(t) is the first echo signal, t is time, and -T a / 2≤t≤T a / 2,T a Let be the time for synthesizing the aperture, x(t) be all terms excluding the echo from the second phase, exp denotes the exponential operation to base e, and j denotes the imaginary unit sign. π represents the mathematical constant pi, and γ is the Doppler modulation frequency.

[0016] In one embodiment of the invention, step 2 includes:

[0017] Step 2.1: Compensate the first echo signal with the initial modulation frequency to obtain the second echo signal;

[0018] Step 2.2: Divide the second echo signal into two sub-aperture signals along the azimuth direction, and perform azimuth Fourier transform on the two sub-aperture signals respectively to obtain two view images;

[0019] Step 2.3: Obtain the Doppler modulation frequency based on the amount of movement between the two view images.

[0020] In one embodiment of the invention, the Doppler modulation frequency is represented as:

[0021]

[0022] Where, γ e γ0 is the Doppler modulation frequency, γ' is the initial modulation frequency, and γ' is the azimuth modulation frequency.

[0023] The initial modulation frequency is expressed as:

[0024] γ0=-2v 2 cos 2 θ / λR0

[0025] Where v is the velocity, λ is the wavelength, R0 is the nearest slant distance, and cosθ is the cosine value of the slant angle;

[0026] The estimated value of the azimuth frequency modulation is expressed as:

[0027]

[0028] Where PRF is the pulse repetition frequency, N is the azimuth upward sampling point, and Δn is the amount of movement between the two view images.

[0029] In one embodiment of the invention, the non-space-varying phase error is expressed as:

[0030]

[0031] Where, φ ne (n) represents the non-space variable phase error, ΔK a (n) represents the change in azimuth frequency modulation, ΔT a The change in the synthesized aperture over time is denoted by N, where N is the number of sampling points in the azimuth direction, and 1 ≤ n ≤ N.

[0032] In one embodiment of the invention, the non-space-variable phase function of the coarse compensation is expressed as:

[0033]

[0034]

[0035] Where H(η) is the phase function of coarse compensation, ΔR(η) is the non-space-varying distance migration, and f r f is the distance frequency. c λ is the carrier frequency, c is the speed of light, and λ is the wavelength.

[0036] In one embodiment of the invention, step 4 includes:

[0037] Step 4.1: Divide the data corresponding to the non-space variable phase function into D distance blocks on an average basis, and coherently superimpose multiple distance samples in the d-th distance block to estimate the phase gradient estimation kernel, where d is the distance block position, 1≤d≤D;

[0038] Step 4.2: Use the equivalent distance to obtain the difference Δr corresponding to each distance block, where Δr represents the difference between the slant distance of other target points in the scene and the slant distance of the scene center;

[0039] Step 4.3: Construct the range spatial variation matrix A based on the difference Δr, the phase gradient estimation kernel, and the weights of the range blocks. b Phase gradient estimation Φ, signal-to-noise ratio weighting matrix W;

[0040] Step 4.4: Based on the distance spatial variation matrix A b The phase gradient estimate Φ and the signal-to-noise ratio weighting matrix W are used to calculate the least squares estimate of the phase gradient of the range spatially variable polynomial, so as to obtain the first gradient estimate based on the least squares estimate of the phase gradient. Second gradient estimation Third gradient estimation Fourth gradient estimation Among them, the first gradient estimation Second gradient estimation Third gradient estimation Fourth gradient estimation These are the gradient estimates for the constant term b0(η), the coefficient of the first term b1(η), the coefficient of the second term b2(η), and the coefficient of the third term b3(η), respectively.

[0041] Step 4.5: Estimate based on the first gradient Second gradient estimation Third gradient estimation Fourth gradient estimation The constant term b0(η), the coefficient of the first term b1(η), the coefficient of the second term b2(η), and the coefficient of the third term b3(η) are obtained by integration respectively. The phase error of the air variable is obtained according to the calculation function of the phase error of the air variable, and the phase error of the air variable is compensated by the air variable phase function.

[0042] In one embodiment of the invention, the phase gradient estimation kernel is represented as:

[0043]

[0044] in, Let arg be the phase gradient estimation kernel for the d-th range block, J be the number of range samples, h be the azimuth position, 1 ≤ h ≤ J, and m be the distance. d,j For the j-th sample unit s in the d-th distance block d (j,:) corresponds to the signal-to-noise ratio weight, conj is the conjugate operation, K is the number of sample units, 1≤l≤K;

[0045] The difference Δr is equivalent to an equivalent distance, which is expressed as:

[0046]

[0047] in, For the equivalent distance, Δr d (j) represents the difference in slant distance between the j-th distance unit of the d-th distance block and the scene center, w d Let d be the weight of the d-th distance block.

[0048] In one embodiment of the invention, the constructed distance space-variable matrix A b Represented as:

[0049]

[0050] The phase gradient estimate Φ is expressed as:

[0051]

[0052] The signal-to-noise ratio weighted matrix W is expressed as:

[0053] W = diag[w1,…,w D ] D*D

[0054] Where diag represents a diagonal matrix;

[0055] The least squares estimate of the phase gradient is expressed as:

[0056]

[0057] in, These are the gradient estimates for b0(η), b1(η), b2(η), and b3(η), respectively, where T is the transpose operation, (·). -1 This is the operation for finding the inversion of a matrix.

[0058] In one embodiment of the invention, the calculation function for the phase error of the spatial variable is expressed as:

[0059] Φ(η,R b )=b0(n)+b1(n)Δr+b2(n)Δr 2 +b3(η)Δr 3

[0060] Wherein, Φ(η,R) b ) represents the phase error of the spatial variation, η represents the azimuth time, and Δr represents the difference between the slant distance of other target points in the scene and the slant distance of the scene center.

[0061] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0062] In UAV-based large-angle SAR imaging, motion errors increase with the downward angle, and the impact of third-order range-space-varying phase errors cannot be ignored. To address this third-order range-space-varying phase error, an improved PGA algorithm in strip mode and a novel motion compensation method based on this algorithm are proposed. This method, by expanding the phase error into a third-order polynomial with respect to range and constructing a weighted least-squares estimate of the polynomial coefficients, can accurately correct the phase error caused by range-space variation.

[0063] In the phase gradient estimation algorithm of this invention, the phase error is spatially invariant within the distance sub-block. The phase gradient within each sub-block is estimated with high accuracy through maximum likelihood estimation. Then, the polynomial coefficients are estimated through least squares estimation to complete the accurate compensation of the phase error. The algorithm has a fast convergence speed and good performance.

[0064] Other aspects and features of the invention will become apparent from the following detailed description with reference to the accompanying drawings. However, it should be understood that the drawings are for illustrative purposes only and not as a limitation of the scope of the invention, as reference should be made to the appended claims. It should also be understood that, unless otherwise indicated, the drawings are not necessarily drawn to scale; they are merely intended to conceptually illustrate the structures and processes described herein. Attached Figure Description

[0065] Figure 1 This is a flowchart illustrating a large downward-view UAV SAR motion compensation method based on an improved PGA algorithm provided in an embodiment of the present invention.

[0066] Figure 2 This is a flowchart illustrating another large downward-view UAV SAR motion compensation method based on an improved PGA algorithm provided in an embodiment of the present invention.

[0067] Figure 3 This is a schematic diagram of the velocity error in the celestial direction provided by an embodiment of the present invention;

[0068] Figure 4 This is a schematic diagram of speed error along the heading provided by an embodiment of the present invention;

[0069] Figure 5 This is another schematic diagram of the celestial velocity error provided in an embodiment of the present invention.

[0070] Figure 6 This is a schematic diagram showing the overall focusing effect comparison provided by an embodiment of the present invention;

[0071] Figure 7This is a schematic diagram of the performance of a center point target provided in an embodiment of the present invention;

[0072] Figure 8 This is a schematic diagram of the performance of an edge point target provided in an embodiment of the present invention;

[0073] Figure 9 This is a diagram showing the estimation result of a non-vacuum variable motion error provided by an embodiment of the present invention;

[0074] Figure 10 This is a diagram showing the spatial variation error estimation results for different distance units provided in an embodiment of the present invention;

[0075] Figure 11 This is a schematic diagram of a comparison of measured data processing results provided in an embodiment of the present invention. Detailed Implementation

[0076] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0077] Example 1

[0078] Compared to spaceborne SAR, UAV platforms are more flexible, capable of multiple imaging operations in a relatively short time, resulting in lower development and flight costs. Furthermore, they can image scenes of interest in real time, making them widely applicable in both military and civilian fields. However, UAV-borne SAR is susceptible to external factors such as airflow, leading to relatively large motion errors. Even with advanced motion sensors, achieving high resolution is difficult. Moreover, high-precision motion sensors are expensive, and some radar systems don't even include inertial navigation systems. Therefore, motion compensation techniques based on echo signals can be used to compensate for motion errors. Phase Gradient Autofocus (PGA) algorithms estimate phase errors based on the defocusing of strong scattering points in the image. PGA algorithms improve accuracy through multiple iterations. Strip PGA algorithms have a limitation: they don't consider spatial variation in phase error. Phase Weighted Estimation (PGA) provides a method for estimating spatially varied phase errors by constructing a spatially varied matrix and then performing least-squares estimation on this matrix to obtain the spatially varied phase. At large downward angles, motion error is spatially variable with respect to distance in the third order. This invention proposes an improved phase-weighted PGA to construct a more accurate imaging compensation function and improve the imaging compensation effect.

[0079] For UAV SAR ultra-close-range blind zone imaging, the two-dimensional coupling between range and azimuth is severe due to the close proximity of the imaging altitude and reference slant range. Motion errors also increase phase modulation and envelope shift. A motion compensation method suitable for high-precision UAV SAR needs to be constructed to address the phase error caused by third-order range spatial variation, thereby improving the strip imaging resolution of large down-view SAR. This invention provides a motion compensation method for large down-view UAV SAR based on an improved PGA algorithm, primarily addressing the problems of low imaging accuracy and poor performance in large down-view airborne SAR blind zone imaging scenarios.

[0080] Please see details. Figure 1 and Figure 2 , Figure 1 This is a flowchart illustrating a large downward-view UAV SAR motion compensation method based on an improved PGA algorithm, provided in an embodiment of the present invention. Figure 2 This is a flowchart illustrating another large downward-view UAV SAR motion compensation method based on an improved PGA algorithm, provided by an embodiment of the present invention. The embodiment of the present invention provides a large downward-view UAV SAR motion compensation method based on an improved PGA algorithm, which specifically includes steps 1 to 6, wherein:

[0081] Step 1: Receive the synthetic aperture radar (SAR) echo signal from the UAV. Perform inertial navigation coarse compensation and range compression on the echo signal sequentially to obtain the first echo signal, which is represented as:

[0082] s0(t)=x(t)exp(jπγt 2 )

[0083] Where s0(t) is the first echo signal, t is time, and -T a / 2≤t≤T a / 2,T a Let be the time for synthesizing the aperture, x(t) be all terms excluding the echo from the second phase, exp denotes the exponential operation to base e, and j denotes the imaginary unit sign. π represents the mathematical constant pi, and γ is the Doppler modulation frequency.

[0084] Step 2: Use the Map Drift Algorithm (MDA) to estimate the phase error of the first echo signal in a non-space-varying manner to obtain the Doppler modulation frequency.

[0085] Step 2.1: Compensate the first echo signal with the initial modulation frequency to obtain the second echo signal.

[0086] Specifically, the initial modulation frequency is expressed as:

[0087] γ0=-2v 2 cos 2 θ / λR0

[0088] Where v is the velocity, λ is the wavelength, R0 is the nearest slant distance, and cosθ is the cosine value of the slant angle.

[0089] Step 2.2: Divide the second echo signal into two sub-aperture signals along the azimuth direction. Perform azimuth Fourier transform on each of the two sub-aperture signals to obtain two view images, which are two spectrum diagrams. The spectrum diagrams are represented as follows:

[0090]

[0091] Where S(f) is the sub-aperture frequency domain signal, representing the azimuth frequency f after azimuth-to-fast Fourier transform, ∫dt represents the integration operation over time t, and s(t) is the sub-aperture time domain signal.

[0092] Step 2.3: Obtain the Doppler modulation frequency based on the amount of movement between the two view images. The Doppler modulation frequency is expressed as:

[0093]

[0094] Where, γ e γ is the Doppler modulation frequency, γ0 is the initial modulation frequency, and γ' is the azimuth modulation frequency.

[0095] The estimated value of the azimuth frequency modulation is expressed as:

[0096]

[0097] Where PRF is the pulse repetition frequency, N is the azimuth upward sampling point, and Δn is the amount of movement between the two view images.

[0098] Step 3: Obtain the non-space-varying phase error based on the change in azimuth modulation frequency and the change in synthetic aperture time in the Doppler modulation frequency. Then, perform non-space-varying range migration correction and coarse compensation for phase error on the radar data based on the non-space-varying phase error to obtain the coarsely compensated non-space-varying phase function.

[0099] Specifically, the non-spatial phase error obtained by MDA estimation is φ ne According to the non-space variable phase error φ ne Non-space-varying range migration correction and coarse phase error compensation are performed on the radar data to complete the coarse compensation of the radar data, and the non-space-varying phase error φ is reduced. ne Represented as:

[0100]

[0101] Where, φ ne(n) represents the non-space variable phase error, ΔK a (n) represents the change in azimuth frequency modulation, ΔT a The change in the synthesized aperture over time is denoted by N, where N is the number of sampling points in the azimuth direction, and 1 ≤ n ≤ N.

[0102] Specifically, the coarse-compensated non-space-varying phase function is expressed as:

[0103]

[0104] Specifically, non-space-variable distance migration is represented as:

[0105]

[0106] Where H(η) is the phase function of coarse compensation, ΔR(η) is the non-space-varying distance migration, and f r f is the distance frequency. c λ is the carrier frequency, c is the speed of light, and λ is the wavelength.

[0107] Step 4: Estimate the phase error of the air variable using the improved phase-weighted PGA algorithm, and then perform compensation processing based on the phase error of the air variable to obtain the compensated air variable phase function.

[0108] Step 4.1: Divide the data corresponding to the non-space-varying phase function into D distance blocks on an average basis. Coherently superimpose multiple distance samples in the d-th distance block to estimate the phase gradient estimation kernel, where d is the distance block position and 1≤d≤D.

[0109] Specifically, the phase gradient estimation kernel is expressed as:

[0110]

[0111] in, Let arg be the phase gradient estimation kernel for the d-th range block, J be the number of range samples, h be the azimuth position, 1 ≤ h ≤ J, and m be the distance. d,j For the j-th sample unit s in the d-th distance block d (j,:) corresponds to the signal-to-noise ratio weight, conj is the conjugate operation, K is the number of sample units, 1≤l≤K.

[0112] Step 4.2: Use the equivalent distance to obtain the difference Δr for each distance block, where Δr represents the difference between the slant distance of other target points in the scene and the slant distance of the scene center.

[0113] Specifically, the difference Δr is equivalent to the equivalent distance, which is expressed as:

[0114]

[0115] in, For the equivalent distance, Δr d (j) represents the difference in slant distance between the j-th distance unit of the d-th distance block and the scene center, w d Let d be the weight of the d-th distance block.

[0116] Step 4.3: Construct the range spatially variable matrix A based on the difference Δr, the phase gradient estimation kernel, and the weights of the range blocks. b Phase gradient estimation Φ, signal-to-noise ratio weighted matrix W.

[0117] Specifically, construct the distance space-variable matrix A. b Represented as:

[0118]

[0119] Specifically, the phase gradient estimate Φ is expressed as:

[0120]

[0121] Specifically, the signal-to-noise ratio weighted matrix W is expressed as:

[0122] W = diag[w1,…,w D ] D*D

[0123] Here, diag represents a diagonal matrix.

[0124] Step 4.4: Based on the distance spatial variation matrix A b The phase gradient estimate Φ and the signal-to-noise ratio weighted matrix W are used to calculate the least squares estimate of the phase gradient of the range spatially variable polynomial. The first gradient estimate is then obtained based on the least squares estimate of the phase gradient. Second gradient estimation Third gradient estimation Fourth gradient estimation Among them, the first gradient estimation Second gradient estimation Third gradient estimation Fourth gradient estimation These are the gradient estimates for the constant term b0(η), the coefficient of the linear term b1(η), the coefficient of the quadratic term b2(η), and the coefficient of the cubic term b3(η), respectively. The least squares phase gradient estimate is expressed as:

[0125]

[0126] in, These are the gradient estimates for b0(η), b1(η), b2(η), and b3(η), respectively, where T is the transpose operation, and (·) -1This is the operation for finding the inversion of a matrix.

[0127] Step 4.5: Estimate based on the first gradient Second gradient estimation Third gradient estimation Fourth gradient estimation The constant term b0(η), the coefficient of the first term b1(η), the coefficient of the second term b2(η), and the coefficient of the third term b3(η) are obtained by integration respectively. The phase error of the air variable is obtained according to the calculation function of the phase error of the air variable, and the phase error of the air variable is compensated by the air variable phase function.

[0128] Specifically, the remaining phase error, excluding the non-space-varying phase error obtained in step 3, is expanded into a third-order polynomial of distance. This third-order polynomial is the calculation function for the space-varying phase error. This phase error function is a comprehensive consideration of the space-varying distance error, and it is expressed as follows:

[0129] Φ(η,R b )=b0(n)+b1(n)Δr+b2(n)Δr 2 +b3(η)Δr 3

[0130] Wherein, Φ(η,R) b ) represents the phase error of the spatial variation, η represents the azimuth time, and Δr represents the difference between the slant distance of other target points in the scene and the slant distance of the scene center.

[0131] Step 5: The spatially variable phase functions of all sub-apertures are spliced ​​together to obtain the phase error of the entire aperture. The overall linear component in the phase error of the entire aperture is removed by linear fitting to obtain the motion-compensated radar data.

[0132] Step 6: Use the UAV SAR imaging algorithm to image the motion-compensated radar data to obtain SAR imaging results.

[0133] In UAV SAR imaging with large downward angle, the method proposed in this invention efficiently estimates the phase error of spatially variable motion errors, thereby achieving accurate imaging of blind areas under large downward angle.

[0134] In UAV large-view SAR imaging, the motion compensation method proposed in this invention has a higher convergence speed and higher estimation accuracy, which not only meets the resolution requirements of actual engineering, but also provides better imaging results.

[0135] The method proposed in this invention can be applied to future UAV SAR imaging. Facing the blind spots of airborne SAR with large downward angles and complex flight paths, it solves the problems of low imaging accuracy and poor performance in UAV SAR imaging scenarios. It also compensates for the limited perception capabilities of cameras and lidar in harsh environments, achieving a more reliable perception level even in poor lighting conditions and severe weather such as wind, sand, rain, and snow. This enables UAV SAR to acquire target image information around the clock and in all weather conditions, and to perform high-risk missions deep within enemy territory, in severe weather, or in polluted environments. The long detection range and lateral observation capabilities of SAR allow UAVs to conduct detection from a distance, avoiding danger and improving survivability. It possesses enormous application potential.

[0136] Unmanned aerial vehicle (UAV)-borne SAR, due to its small and lightweight platform, is susceptible to external airflow factors, leading to decreased radar echo coherence, particularly for imaging blind zones with large downward angles, thus affecting the achievement of high-resolution radar. Typically, to overcome the influence of aircraft motion errors, radar employs a combination of inertial measurement units (IMUs) and autofocus for motion error extraction and compensation. However, due to limitations in loading conditions and cost, UAV platforms cannot accommodate high-precision IMU systems, posing a significant challenge to achieving high-resolution radar. The method proposed in this invention effectively solves the problem of spatially varying phase errors at higher ranges, reduces computational load, and improves resolution and imaging quality. It can be widely used in multi-functional UAV-borne SAR systems, such as extracting multi-dimensional high-resolution information of ground features, high-coverage surface imaging, and improving the ability to acquire details and achieve high-resolution imaging of target areas.

[0137] Example 2

[0138] This embodiment provides a simulation experiment based on the large downward-view UAV SAR motion compensation method provided in Embodiment 1.

[0139] (1) Point target simulation

[0140] The simulation parameters of the point targets in this invention are shown in Table 1. Figures 3-5 Because of the velocity errors introduced in three directions, the velocity changes are relatively uneven. Figure 6 The results are the imaging simulation of the scene. Figure 6 The leftmost image in the middle shows the result of direct imaging without motion compensation. Figure 6 The middle figure shows the result after coarse compensation using MDA. Figure 6 The rightmost image in the middle shows the result after fine compensation. Figure 7 Focusing performance at the center point Figure 8The focusing performance at edge points is shown in the simulation results. The focusing effect at both edge and center points meets the requirements, indicating that the focusing effect of the method of this invention is good.

[0141] Table 1 Simulation Parameters

[0142] Pulse width 1μs signal bandwidth 1500MHz carrier frequency 35GHz Carrier speed 45m / s Sampling rate 2100MHz Flight altitude 3000m Oblique angle 0° Central slope distance 3045m Repetition frequency 625Hz Target Spacing 200 meters

[0143] (2) Actual measurement verification

[0144] The highest resolution of the data used in the actual measurements of this invention is 0.2 meters. The radar's transmission signal is in the Ka band with a bandwidth of 1500M, a pulse width of 1μs, and a center slant range of 9 kilometers. Figure 9 The figure shows the estimation results of the motion error of the non-vacuum variable. The envelope offset caused by the phase error of the non-vacuum variable ranges from -2.5m to 1m, which is more than 10 distance cells. The envelope offset caused by the phase error of the non-vacuum variable must be compensated. Figure 10 The diagram shows the motion error estimation results for the spatial variable corresponding to eight different distance units. The motion error caused by different distance units varies significantly. The phase error estimation method for the spatial variable proposed in this invention can estimate and compensate for this error.

[0145] Figure 11 The middle image (a) shows the result without motion compensation. At this time, no information in the scene can be obtained, and the defocus is quite severe. Figure 11 The middle image (b) shows the result after coarse compensation. The center of the scene has been well focused, and the terrain is also relatively clear. Figure 11 Figure (c) shows the result after fine compensation. After spatially varying motion error compensation, the target in the scene is clearer, and the focusing effect of the center and edge points is also better. This shows that the imaging details of the present invention are better and the focusing quality is higher, thus verifying the correctness of the algorithm.

[0146] In the description of the invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0147] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or feature data point described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or feature data points described may be combined in any suitable manner in one or more embodiments or examples. In addition, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0148] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A large downward-view UAV SAR motion compensation method based on an improved PGA algorithm, characterized in that, The UAV SAR motion compensation method includes: Step 1: Receive the synthetic aperture radar (SAR) echo signal from the UAV, perform inertial navigation coarse compensation and range compression on the echo signal to obtain the first echo signal; Step 2: Use an image offset algorithm to perform non-variable phase error estimation on the first echo signal to obtain the Doppler modulation frequency; Step 3: Obtain the non-space-varying phase error based on the change in azimuth modulation frequency and the change in synthetic aperture time in the Doppler modulation frequency. Then, perform non-space-varying range migration correction and coarse compensation for phase error on the radar data based on the non-space-varying phase error to obtain the coarsely compensated non-space-varying phase function. Step 4: Estimate the phase error of the air transformer using the improved phase-weighted PGA algorithm, and perform compensation processing based on the phase error of the air transformer to obtain the compensated air transformer phase function. Step 5: The compensated spatially varying phase functions of all sub-apertures are spliced ​​together to obtain the phase error of the entire aperture. The overall linear component in the phase error of the entire aperture is removed by linear fitting to obtain the motion-compensated radar data. Step 6: Use the UAV SAR imaging algorithm to image the motion-compensated radar data to obtain SAR imaging results; Step 4 includes: Step 4.1: Divide the data corresponding to the non-space-varying phase function into equal parts. D The distance block, for the _th distance block, d Multiple distance samples within a distance block are coherently superimposed to estimate the phase gradient estimation kernel, where... d The distance to the block position is 1 ≤ d ≤ D ; Step 4.2: Obtain the difference corresponding to each distance block using the equivalent distance. ,in, This represents the difference between the slant distance of other target points in the scene and the slant distance of the scene center; Step 4.3: Based on the difference... The phase gradient estimation kernel and the weights of the range blocks respectively construct a range spatially variable matrix. Phase gradient estimation Indicator-to-noise ratio weighted matrix ; Step 4.4: Based on the distance spatial variation matrix... The phase gradient estimation The signal-to-noise ratio weighting matrix Calculate the least squares estimate of the phase gradient of the space-varying polynomial, and obtain the first gradient estimate based on the least squares estimate of the phase gradient. Second gradient estimation Third gradient estimation Fourth gradient estimation Among them, the first gradient estimation Second gradient estimation Third gradient estimation Fourth gradient estimation These are the constant terms. coefficient of the first term coefficient of quadratic term coefficient of the cubic term Gradient estimation; Step 4.5: Estimate based on the first gradient Second gradient estimation Third gradient estimation Fourth gradient estimation Integrating each term yields the constant term. coefficient of the first term coefficient of quadratic term coefficient of the cubic term The phase error of the air variable is obtained based on the calculation function of the phase error of the air variable, and the phase error of the air variable is compensated by the air variable phase function. The calculation function for the phase error of the spatial variable is expressed as: in, The phase error is due to the spatial variation. For location, slow time, This represents the difference between the slant distance of other target points in the scene and the slant distance of the scene center.

2. The large downward-view UAV SAR motion compensation method based on the improved PGA algorithm according to claim 1, characterized in that, The first echo signal is represented as: in, This is the first echo signal. For time, , The time for synthesizing the aperture, To remove all other terms from the echo of the second phase, This indicates exponential operations with base e. The symbol representing the imaginary unit, and , Represents pi (π). Frequency modulation for Doppler.

3. The large downward-view UAV SAR motion compensation method based on the improved PGA algorithm according to claim 1, characterized in that, Step 2 includes: Step 2.1: Compensate the first echo signal with the initial modulation frequency to obtain the second echo signal; Step 2.2: Divide the second echo signal into two sub-aperture signals along the azimuth direction, and perform azimuth Fourier transform on the two sub-aperture signals respectively to obtain two view images; Step 2.3: Obtain the Doppler modulation frequency based on the amount of movement between the two view images.

4. The large downward-view UAV SAR motion compensation method based on the improved PGA algorithm according to claim 3, characterized in that, The Doppler modulation frequency is expressed as: in, To tune the frequency for Doppler, For the initial frequency modulation, For azimuth frequency tuning; The initial modulation frequency is expressed as: in, For speed, For wavelength, The closest slope distance, The cosine value of the oblique angle; The estimated value of the azimuth frequency modulation is expressed as: in, The pulse repetition frequency, For azimuth upward sampling points, This represents the amount of movement between the two view images.

5. The large downward-view UAV SAR motion compensation method based on the improved PGA algorithm according to claim 1, characterized in that, The non-space variable phase error is expressed as: in, This is a non-space variable phase error. This represents the change in azimuth frequency. This represents the change in pore size over time. For sampling points in the azimuth direction, 1≤ ≤ .

6. The large downward-view UAV SAR motion compensation method based on the improved PGA algorithm according to claim 5, characterized in that, The non-space-variable phase function of the coarse compensation is expressed as: in, For coarse compensation phase function, For non-space variable distance migration, For distance frequency, The carrier frequency, At the speed of light, λ is the wavelength.

7. The large downward-view UAV SAR motion compensation method based on the improved PGA algorithm according to claim 1, characterized in that, The phase gradient estimation kernel is expressed as: in, For the first d Phase gradient estimation kernel for each distance block, To obtain the phase function, The distance is the number of samples. For azimuth and location, 1 ≤ h ≤ , For the j-th sample unit in the d-th distance block The corresponding signal-to-noise ratio weights, To obtain the conjugate operation, The number of sample units. ; The difference Equivalent to an equivalent distance, which is expressed as: in, For equivalent distance, Let be the difference between the j-th distance unit of the d-th distance block and the slant distance to the scene center. Let d be the weight of the d-th distance block. .

8. The large downward-view UAV SAR motion compensation method based on the improved PGA algorithm according to claim 7, characterized in that, The constructed distance space-variable matrix Represented as: The phase gradient estimation Represented as: The signal-to-noise ratio weighting matrix Represented as: in, Represents a diagonal matrix; The least squares estimate of the phase gradient is expressed as: in, , They are respectively for The gradient estimation, where T is the transpose operation, is given. This is the operation for finding the inversion of a matrix.

Images