Translation compensation and azimuth dimension compression integrated imaging method, system and equipment for space orbit target by spaceborne radar and medium

Abstract

The invention discloses an integrated imaging method, system and equipment for translation compensation and azimuth dimension compression of a space orbit target by a spaceborne radar and a medium, and relates to the technical field of microwave remote sensing. The objective of the invention is to solve the problems that in the process of space orbit target imaging by a spaceborne radar, the similarity between echo envelopes is reduced, the echo envelopes may be submerged by noise due to the low echo signal-to-noise ratio, translation compensation is difficult to carry out, and a clear image is difficult to obtain after azimuth dimension compression processing. The method comprises the following steps: establishing a relationship between a translation component coefficient and a transformation parameter of a space orbit target relative to a radar satellite; carrying out fast and slow time fractional Fourier transform on the echo signal of the space orbit target based on the transformation parameter to obtain a transformation spectrum; and searching the transformation parameters by adopting a PSO optimization algorithm, so that the image entropy of the transformation spectrum is minimum, and space orbit target imaging is completed.

Description

Technical Field

[0001] The present invention belongs to the technical field of microwave remote sensing. Background Art

[0002] Space targets refer to artificial celestial bodies and space debris running in the cosmic space. Space orbital targets refer to those targets running stably in orbits. With the increasing strategic value of the cosmic space, it is necessary to effectively monitor space targets. Radar can perform high-precision imaging on targets all day and all weather, and is an effective means for monitoring space targets; spaceborne radar has strong flexibility and a wide observation range, breaking through the limitations of ground-based radar on the limited imaging observation range of space targets and severe ionospheric clutter, and has gradually become an important part of the space surveillance system.

[0003] The imaging scene of spaceborne radar for space orbital targets is small, and the targets are non-cooperative targets. Therefore, the ISAR imaging theory can be used to process the echoes. In the ISAR imaging theory, for a radar that emits a linear frequency modulation pulse signal, after motion compensation and azimuth dimension compression of the range-compressed echo signal, a focused image can be obtained. For motion compensation, the ISAR imaging theory decomposes the relative motion between the target and the radar into a translational component along the radar line of sight and a rotational component perpendicular to the radar line of sight. When the rotation angle is small, the Doppler frequency generated by the rotational component is used to achieve high resolution in the azimuth direction and is effective for imaging; the envelope delay and phase change generated by the translational component cause a decrease in the correlation between radar echoes, and translational compensation is required. The translational compensation methods are divided into non-parametric and parametric compensation methods. The non-parametric method relies on the similarity between envelopes to achieve, and the parametric compensation method models the translational component as a function of slow time and estimates the function parameters for compensation. After translational compensation, azimuth dimension compression is performed to obtain a focused image. For the problem of spaceborne radar imaging of space orbital targets, both the target and the radar have orbital motions, the translational component and the rotational component are coupled with each other, and their relative motion is relatively complex, resulting in a decrease in the similarity between echo envelopes. In addition, due to the long distance between the target and the radar, there is also a problem of low echo signal-to-noise ratio, and the echo envelope may be submerged by noise. Summary of the Invention

[0004] The present invention is to solve the problems that in the process of spaceborne radar imaging of space orbital targets, due to the relatively complex relative motion between the target and the radar, the similarity between echo envelopes decreases; and also due to the long distance between the target and the radar, the echo signal-to-noise ratio is low, resulting in the echo envelope being possibly submerged by noise, making it difficult to perform translational compensation and difficult to obtain a clear image after azimuth dimension compression processing. Now, an integrated imaging method for translational compensation and azimuth dimension compression of spaceborne radar for space orbital targets is provided. This method can estimate the motion parameters of non-cooperative targets at a low signal-to-noise ratio, achieve integrated imaging of translational compensation and azimuth dimension compression, and achieve precise focused imaging.

[0005] Spaceborne radar translational motion compensation and azimuth compression integrated imaging method for space orbital targets, including:

[0006] Establish the relationship between the translational component coefficients of the space orbital target relative to the radar satellite and the transformation parameters;

[0007] Perform fast and slow time fractional Fourier transform on the echo signal of the space orbital target based on the transformation parameters to obtain the transformed spectrum;

[0008] Use the PSO optimization algorithm to search for the transformation parameters to minimize the image entropy of the transformed spectrum and complete the imaging of the space orbital target.

[0009] Furthermore, the relationship expression between the translational component coefficients of the above space orbital target and the transformation parameters is as follows:

[0010]

[0011] Among them, both p and χ are transformation parameters, α = arccot(2β2k2), both k1 and k2 are translational component coefficients of the space orbital target relative to the radar satellite, and β2 = 2f c / c, f c is the carrier frequency of the transmitted signal, and c is the speed of light.

[0012] Furthermore, the above-mentioned performing fast and slow time fractional Fourier transform on the echo signal of the space orbital target based on the transformation parameters to obtain the transformed spectrum includes:

[0013] Perform range compression on the echo signal of the space orbital target to obtain a two-dimensional quadratic phase echo signal;

[0014] Perform fast and slow time fractional Fourier transform on the two-dimensional quadratic phase echo signal based on the transformation parameters and then take the modulus to obtain the transformed spectrum.

[0015] Furthermore, the above-mentioned performing range compression on the echo signal of the space orbital target to obtain a two-dimensional quadratic phase echo signal includes:

[0016] Obtain the two-dimensional quadratic phase echo signal through the following formula

[0017]

[0018] Among them, R i = k0 + x i +(k1 + y i ξ)t + k2t, R i is the slant range equation of the i-th scatterer,

[0019] i is the serial number of the scattering point on the space orbit target, A i is the scattering intensity of the i-th scattering point, T p is the pulse width, t and and fast time respectively, c is the speed of light, B is the bandwidth of radar transmission signal, j is the imaginary unit, f c is the carrier frequency of the transmitted signal, (x i ,y i ) represents the position coordinate of the i-th scattering point in the target coordinate system, k0, k1 and k2 are the translational component coefficients of the space orbit target relative to the radar satellite, and ξ is the rotational angular velocity of the space orbit target relative to the radar satellite.

[0020] Furthermore, the above-mentioned fast and slow time fractional Fourier transform of the two-dimensional quadratic phase echo signal based on the transformation parameters includes:

[0021] The two-dimensional quadratic phase echo signal is subjected to fast and slow time fractional Fourier transform using the following formula:

[0022]

[0023] in, represents the fast and slow time fractional Fourier transform results,

[0024] ρ1, ρ2 and ρ3 are all intermediate parameters, and their expressions are as follows:

[0025] ρ1=y i ξt+k0+x i ,

[0026] ρ2=μ+β2k1 sinα+β2y i ξsinα,

[0027]

[0028] α=arccot(2β2k2), β2=2f c / c, β1=2 / c, μ is the variable corresponding to the fractional domain axis obtained after the traditional fractional Fourier transform, χ is the transformation parameter, T r Accumulation time for echoes.

[0029] Furthermore, the two-dimensional quadratic phase echo signal is subjected to a fast-time and slow-time fractional Fourier transform result to obtain a transform spectrum, including:

[0030] The transformed spectrum is obtained by the following formula

[0031]

[0032] Furthermore, the image entropy E of the above-mentioned transformation spectrum is calculated by the following formula:

[0033]

[0034] Among them, P ave is the transformation spectrum, r is the serial number of each unit of the transformation spectrum,

[0035] The integrated imaging system of spaceborne radar for translational compensation and azimuth compression of space orbit targets includes:

[0036] A relationship unit is constructed for: establishing a relationship between the translation component coefficient of the space orbit target relative to the radar satellite and the transformation parameter;

[0037] a transform unit, configured to perform a fast and slow time fractional Fourier transform on the echo signal of the space orbit target based on the transform parameters to obtain a transform spectrum;

[0038] The imaging unit is used to: use the PSO optimization algorithm to search for the transformation parameters so that the image entropy of the transformation spectrum is minimized to complete the imaging of the space orbit target.

[0039] Furthermore, the relationship between the translational component coefficients of the above-mentioned space orbit target and the transformation parameters is expressed as follows:

[0040]

[0041]

[0042] Where p and χ are transformation parameters, α=arccot(2β2k2), k1 and k2 are the translational component coefficients of the space orbit target relative to the radar satellite, β2=2f c / c,f c is the carrier frequency of the transmitted signal, and c is the speed of light.

[0043] Furthermore, the above-mentioned fast and slow time fractional Fourier transform is performed on the echo signal of the space orbit target based on the transformation parameters to obtain the transformation spectrum, including:

[0044] performing range compression on the echo signal of the space orbit target to obtain a two-dimensional secondary phase echo signal;

[0045] The two-dimensional quadratic phase echo signal is subjected to fast and slow time fractional Fourier transform based on the transformation parameters and then modulo-transformed to obtain a transformation spectrum.

[0046] Furthermore, the above-mentioned performing range compression on the echo signal of the space orbit target to obtain a two-dimensional secondary phase echo signal includes:

[0047] The two-dimensional secondary phase echo signal is obtained by the following formula

[0048]

[0049] Among them, R i =k0+x i +(k1+y i ξ)t+k2t,R i is the slope range equation of the i-th scattering point,

[0050] i is the serial number of the scattering point on the space orbit target, A i is the scattering intensity of the i-th scattering point, T p is the pulse width, t and and fast time respectively, c is the speed of light, B is the bandwidth of radar transmission signal, j is the imaginary unit, f c is the carrier frequency of the transmitted signal, (x i ,y i ) represents the position coordinate of the i-th scattering point in the target coordinate system, k0, k1 and k2 are the translational component coefficients of the space orbit target relative to the radar satellite, and ξ is the rotational angular velocity of the space orbit target relative to the radar satellite.

[0051] Furthermore, the above-mentioned fast and slow time fractional Fourier transform of the two-dimensional quadratic phase echo signal based on the transformation parameters includes:

[0052] The two-dimensional quadratic phase echo signal is subjected to fast and slow time fractional Fourier transform using the following formula:

[0053]

[0054] in, represents the fast and slow time fractional Fourier transform results,

[0055] ρ1, ρ2 and ρ3 are all intermediate parameters, and their expressions are as follows:

[0056] ρ1=y i ξt+k0+x i ,

[0057] ρ2=μ+β2k1 sinα+β2y i ξsinα,

[0058]

[0059] α=arccot(2β2k2), β2=2f c / c, β1=2 / c, μ is the variable corresponding to the fractional domain axis obtained after the traditional fractional Fourier transform, χ is the transformation parameter, Tr Accumulation time for echoes.

[0060] Furthermore, the two-dimensional quadratic phase echo signal is subjected to a fast-time and slow-time fractional Fourier transform result to obtain a transform spectrum, including:

[0061] The transformed spectrum is obtained by the following formula

[0062]

[0063] Furthermore, the image entropy E of the above-mentioned transformation spectrum is calculated by the following formula:

[0064]

[0065] Among them, P ave is the transformation spectrum, r is the serial number of each unit of the transformation spectrum,

[0066] A spaceborne radar imaging device for integrated translational compensation and azimuth dimension compression of space orbital targets, comprising a processor and a memory, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the above-mentioned spaceborne radar imaging method for integrated translational compensation and azimuth dimension compression of space orbital targets.

[0067] A computer storage medium stores at least one instruction, which is loaded and executed by a processor to implement the above-mentioned integrated imaging method for space-borne radar translation compensation and azimuth dimension compression of space orbit targets.

[0068] The present invention includes the following beneficial effects:

[0069] 1. The present invention can more accurately estimate the second-order translational parameters of non-cooperative space orbital targets;

[0070] 2. The present invention can realize the integrated processing of translation compensation and azimuth compression, and the process is simple;

[0071] 3. Data processing results show that the present invention has good noise resistance and performs well when the signal-to-noise ratio is low. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 This is a flow chart of the integrated imaging method for translational compensation and azimuth dimension compression of space-borne radar for space orbit targets;

[0073] Figure 2 is the MFDFrFT imaging effect diagram under the condition of different distances between two scattering points in azimuth, where;

[0074] Figure 3 Schematic diagrams of one-dimensional range profiles at different SNRs, where (a) is without noise, (b) SNR = -5 dB, (c) SNR = -10 dB, (d) SNR = -15 dB, and (e) SNR = -20 dB;

[0075] Figure 4 Schematic diagrams of MFDFrFT imaging results at different SNRs, where (a) is without noise, (b) SNR = -5 dB, (c) SNR = -10 dB, (d) SNR = -15 dB, and (e) SNR = -20 dB;

[0076] Figure 5 Schematic diagrams of MFDFrFT imaging results at different SNRs when Δy = 1 m, where (a) is without noise, (b) SNR = -5 dB, (c) SNR = -10 dB, (d) SNR = -15 dB, and (e) SNR = -20 dB;

[0077] Figure 6 Schematic diagrams of MFDFrFT imaging results at different SNRs when Δy = 0.75 m, where (a) is without noise, (b) SNR = -5 dB, (c) SNR = -10 dB, (d) SNR = -15 dB, and (e) SNR = -20 dB;

[0078] Figure 7 Schematic diagrams of MFDFrFT imaging results at different SNRs when Δy = 0.5 m, where (a) is without noise, (b) SNR = -5 dB, (c) SNR = -10 dB, (d) SNR = -15 dB, and (e) SNR = -20 dB;

[0079] Figure 8 Schematic diagram of the dot matrix of the target to be imaged;

[0080] Figure 9 Schematic diagram of the one-dimensional range profile after range compression;

[0081] Figure 10 Schematic diagram of the convergence process of the adaptation function;

[0082] Figure 11 Schematic diagram of the imaging result;

[0083] Figure 12 Schematic diagram of the one-dimensional range profile (with noise) after range compression;

[0084] Figure 13 Schematic diagram of the convergence process of the adaptation function (with noise);

[0085] Figure 14Schematic diagram of imaging result (with noise). Detailed implementation manners

[0086] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments may be combined with each other.

[0087] Compared with the non-parametric compensation method that is more dependent on the envelope, the parametric compensation method is more suitable for this scenario. Since both the radar satellite and the space orbital target move smoothly on a specific orbit, a second-order polynomial model can be used for the function modeling of the translational component with respect to slow time. This implementation manner mainly focuses on the echo signal after range compression of the space orbital target by the spaceborne radar, and performs integrated processing of translational compensation and azimuth dimension compression based on the fractional Fourier transform of fast and slow time to achieve precise focused imaging.

[0088] Detailed implementation manner 1: Refer to Figure 1 To specifically illustrate this implementation manner, the integrated imaging method for translational compensation and azimuth dimension compression of the space orbital target by the spaceborne radar described in this implementation manner includes:

[0089] Step 1: Modeling the echo after range compression of the space orbital target by the spaceborne radar.

[0090] The spaceborne radar emits a linear frequency modulation pulse signal The expression of which is:[[]]

[0091]

[0092] where rect(·) represents the rectangular window function, f c is the carrier frequency of the transmitted signal, T p is the pulse width, j is the imaginary unit, γ is the frequency modulation slope, t and are slow time and fast time respectively.

[0093] The echo signal of a single isolated scatterer i is expressed as:[[]]

[0094]

[0095] where c is the speed of light, A is the scattering intensity, and R I is the slant range equation of the scatterer, and this slant range equation is a function of slow time.

[0096] An imaging coordinate system O'x0y0z0 is established. The origin O' of the coordinate system is the position of the target satellite, that is, the target satellite relative to the rotation center of the radar satellite. The direction of the x0 axis is the radar line-of-sight direction, and the direction of the y0 axis is the azimuth direction of the imaging plane. Then the slant range equation R I of the scattering point is expressed as:

[0097] R I (t) = R OO' (t) + R Ir (t) = k0 + x I + (k1 + y I ξ)t + k2t 2 ,

[0098] where R OO' (t) is the distance from the target rotation center to the radar, and its variation with slow time, that is, the translational component of the target relative to the radar's motion; R Ir (t) is the motion amount of the scattering point in the range dimension caused by the rotational component of the target relative to the radar; k0, k1, and k2 are all translational component coefficients of the space orbital target relative to the radar satellite, ξ is the rotational angular velocity of the target relative to the radar satellite, and (x I , y I ) represents the coordinates of the scattering point I in the imaging coordinate system O'x0y0z0.

[0099] R I (t) represents the slant range between the radar satellite and the target scattering point i at slow time t.

[0100] After range compression, high range resolution is achieved. At this time, the two-dimensional echo signal is expressed as:

[0101]

[0102] where B is the bandwidth of the radar transmitted signal, and the two-dimensional echo signal is a two-dimensional quadratic phase signal with fast time delay.

[0103] A target can be considered to be composed of multiple scattering points. Therefore, the echo signal of a target is composed of the superposition of multiple two-dimensional quadratic phase signal components and is expressed as:

[0104]

[0105] where R i is the slant range equation of the i-th scattering point, and A i is the scattering intensity of the i-th scattering point.

[0106] Step 2: Define the relationship between the translational parameters and transformation parameters of a single scattering point.

[0107] Consider a slope distance equation of R P (t) = k2t 2 +k1t+k0, isolated scattering point, ignoring the rectangular window, the form of the noise-free signal after distance compression is a two-dimensional quadratic phase signal The expression is:

[0108]

[0109] Where β1 = 2 / c, β2 = 2f c / c.

[0110] Two-dimensional secondary phase echo signal The fast and slow time fractional Fourier transform of is defined as:

[0111]

[0112] Where η(α,χ,t) represents the fast time delay that varies with the slow time, and is expressed as follows:

[0113]

[0114] K(α,μ,t) represents the kernel function, which is expressed as follows:

[0115]

[0116] μ is the variable corresponding to the fractional domain axis obtained after the traditional fractional Fourier transform, n = 1, 2, ..., δ(·) is the impulse function, and p and χ are both transformation parameters.

[0117] As can be seen from the above formula, when searching for the transformation parameters, when formula (10) is satisfied, that is, when the transformation parameters p and χ match the coefficients k2 and k1 of the target's translational component, respectively, the transformation result of a single scattering point will have a peak. When the algorithm parameters do not match the target parameters, the peak focusing effect deteriorates.

[0118]

[0119] Substituting the ideal values of p and χ (i.e., peak values) into equation (6), we obtain the following equation:

[0120]

[0121] Since the echo accumulation time is T r , if the accumulation time processing window is considered, then formula (11) can be transformed into:

[0122]

[0123] Arranging formula (11) yields:

[0124]

[0125] After taking the modulus, the point spread function can be obtained:

[0126]

[0127] The position of the peak is described by two components in the range direction and the azimuth direction. The range direction is characterized by , the main lobe width is 1 / B, and the peak position is The azimuth direction is characterized by μ, the main lobe width is sinα / T r , and the peak position is μ = χ = β2k1sinα.

[0128] Step 3: Define the relationship between the target translational parameters and the transformation parameters, and how multiple scatterers in the target are spread on the spectrum.

[0129] For a target with a slant range equation of R P (t) = k2t 2 +k1t + k0 and consisting of multiple isolated scatterers, assume that the position coordinates of any scatterer i in the target coordinate system are (x i , y i ), then the slant range equation R i (t) of this scatterer i is:

[0130] R i (t) = k2t 2 +k1t + k0 + x i +y i ξt = k2t 2 +(k1 + y i ξ)t + k0 + x i (15),

[0131] where k0 + x i = ν is a constant that does not change with time.

[0132] The form of its noiseless signal after range compression is the superposition of multiple two-dimensional quadratic phase signal components, and the expression is:

[0133]

[0134] Assume that the center position coordinates of this target are (0,0). Then, when performing the fast and slow time fractional Fourier transform on it, since the translational component is the main component, when still satisfying Equation (17), that is, when the transformation parameters p and χ match the motion parameters k2 and k1 of the target respectively, most scatterers appear as peaks in the transformation result.

[0135]

[0136] When most of the scattering points have peak values in the transformation results, the fast and slow time fractional Fourier transform results of equation (16) are expressed as follows:

[0137]

[0138] ρ1, ρ2 and ρ3 are all intermediate parameters, and their expressions are as follows:

[0139] ρ1=y i ξt+k0+x i ,

[0140] ρ2=μ+β2k1 sinα+β2y i ξsinα,

[0141]

[0142] After taking the modulo, the transformed spectrum can be obtained, and the result is the superposition of the point spread functions of a single scattering point.

[0143]

[0144] At this time, different scattering points of the same target are spread out in the transformation spectrum. In the distance upward, the position of the peak point is When the angle is small, the position of the peak point in the distance direction is approximately the same as the distance coordinate x of the scattering point. i Positive correlation. In the azimuth direction, the peak point position is:

[0145] μ i =(β2k1+β2y i ξ)sinα (20).

[0146] That is, the azimuth coordinate y of the scattering point i Therefore, scattering points at different positions in the range and azimuth directions have different peak positions in the transformation spectrum, enabling imaging.

[0147] Regarding resolution, this embodiment does not involve range processing, so the range resolution is still c / 2B. Since the μ axis is obtained by rotating the frequency axis, for point (x i ,y i ), whose Doppler frequency f di for:

[0148]

[0149] λ is the wavelength.

[0150] The corresponding frequency of this point on the μ axis is:

[0151]

[0152] The main lobe width of the point spread function is 2sinα / T r , and the azimuth resolution Δy can be obtained as follows. It is the same as the azimuth resolution of RD imaging.

[0153]

[0154] Among them, is the relative rotation angle.

[0155] However, it should be noted at this time that points far from the center position may be defocused due to the movement of the range cells.

[0156] Step 4: Obtain the initial parameters and obtain the preliminary imaging result.

[0157] After range compression, the echo as shown in Equation (4) is obtained.

[0158] Since the space-based radar and the target satellite both have their fixed orbits of motion. According to the known prior information, the parameters p and χ can be searched through traversal to make a preliminary estimate of k1 and k2. Substitute the preliminary estimated values into Equation (17) to obtain the initial parameters p0 and χ0. Substitute the echo and the initial parameters p0 and χ0 into Equation (6) to obtain the transform spectrum as shown in Equation (19).

[0159] Step 5: Take the minimum entropy as the criterion and use an optimization algorithm to optimize the parameters until the entropy value of the imaging result is the smallest.

[0160] After obtaining the transform spectrum, calculate the image entropy of this spectrum diagram. Let P ave represent the transform spectrum, and its minimum entropy formula is as follows:

[0161]

[0162] Among them, r is the serial number of each unit of the transform spectrum,

[0163]

[0164] When the transform parameters p and χ are the ideal values respectively, the imaging focusing effect is the best and the image entropy is the smallest. Therefore, taking the minimum entropy as the criterion, using the PSO optimization algorithm, search for the possible values of the transform parameters p and χ, and obtain the corresponding transform spectrum according to Step 4 and calculate the image entropy for the possible values, and keep searching until the image entropy reaches the minimum to obtain the ideal values and their corresponding transform spectra, that is, the focused image.

[0165] Specific Embodiment 2: The spaceborne radar for translational motion compensation and azimuthal compression integrated imaging system for space orbit targets described in this embodiment is characterized in that it includes:

[0166] Construct a relationship unit for establishing the relationship between the translational component coefficients of a space orbit target relative to a radar satellite and transformation parameters;

[0167] A transformation unit for performing fast and slow time fractional Fourier transform on the echo signal of a space orbit target based on the transformation parameters to obtain a transformed spectrum;

[0168] An imaging unit for searching the transformation parameters using a PSO optimization algorithm to minimize the image entropy of the transformed spectrum and complete the imaging of the space orbit target.

[0169] Specific Embodiment 3: The spaceborne radar integrated imaging device for translational compensation and azimuthal compression of a space orbit target described in this embodiment. The spaceborne radar integrated imaging device for translational compensation and azimuthal compression of a space orbit target includes a processor and a memory. At least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to implement the spaceborne radar integrated imaging method for translational compensation and azimuthal compression of a space orbit target as described in Specific Embodiment 1.

[0170] Specific Embodiment 4: A computer storage medium described in this embodiment is characterized in that at least one instruction is stored in the computer storage medium, and the at least one instruction is loaded and executed by the processor to implement the spaceborne radar integrated imaging method for translational compensation and azimuthal compression of a space orbit target as described in Specific Embodiment 1.

[0171] To verify the beneficial effects of the present invention, the following simulation experiments are carried out:

[0172] In the simulation experiment, the basic radar parameters are set as follows in the table:

[0173] Table 1 Basic Radar Parameters

[0174]

[0175] On the premise of ideal parameter settings, the performance of the method of the present invention is tested, mainly including resolution test, anti-noise performance test, and combined test of anti-noise performance and resolution. Set k2 = 30, k1 = 1, ξ = 0.02 rad / s. At this time, the theoretical value of the azimuth resolution can reach 0.375 m. The imaging effects under the condition that the distances of the simulation scatter points in the azimuth direction are different are as Figure 2 shown. Analyzing the experimental results, it can be seen that when the distance between two scatter points reaches twice the theoretical value of the resolution, the two points can be clearly distinguished in the transformed spectrum. The one-dimensional range profiles and the corresponding imaging results are compared under different signal-to-noise ratios in the simulation as Figure 3 and Figure 4As shown in the figure, by analyzing the experimental results, it can be seen that the method described in this embodiment has good anti-noise performance. When the SNR is as low as -20 dB, it is difficult to distinguish the scattering points from the noise. At the same time, by changing the distance between the scattering points in the azimuth direction and the signal-to-noise ratio, the experimental simulation results are as shown in Figure 5 , Figure 6 and Figure 7 . By analyzing the experimental results, it can be seen that the noise has basically no effect on the resolution of this embodiment, further confirming the relatively good anti-noise performance of this embodiment.

[0176] To further test the feasibility of the present invention, STK is used to simulate the satellite imaging scenario of the spaceborne radar, the orbital data is exported, and the scientific computing software is used to simulate the satellite lattice. By optimizing the algorithm to search for parameters, imaging simulation is carried out. The lattice of the target to be imaged is as shown in Figure 8 . It is set that the orbital of the radar satellite is located in the equatorial plane, the orbital radius is 6678.14 km, and the eccentricity is 0. It is set that the orbital radius of the target satellite is 6928.14 km, the eccentricity is 0, the orbital inclination angle, that is, the included angle with the radar satellite orbit is 28.5°, and the initial angle φ = 0.03°.

[0177] The one-dimensional range image before translational compensation is as shown in Figure 9 .

[0178] Using PSO to search for parameters, integrated imaging of translational compensation and azimuth compression is realized. The convergence process of the PSO optimization fitness function and the imaging results are as shown in Figure 10 .

[0179] After the integrated imaging processing of translational compensation and azimuthal dimension compression, the final image has a good focusing effect, presenting a clear imaging result as shown in Figure 11 .

[0180] On the basis of this simulation experiment, while keeping the basic parameters of the radar and the target motion unchanged, Gaussian noise is added, and SNR = -10 dB is set. The one-dimensional range image before translational compensation is as shown in Figure 12 , where the one-dimensional range image is difficult to identify.

[0181] Using PSO to search for parameters, integrated imaging of translational compensation and azimuth compression is realized. The convergence process of the PSO optimization fitness function and the imaging results are as shown in Figure 13 and Figure 14 . The present invention makes full use of the phase information in the echo, can basically operate normally under the condition of SNR = -10 dB, and the clarity of the imaging is basically not affected, showing good anti-noise performance.

[0182] Although the present invention has been described herein with reference to particular embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the present invention. It should therefore be understood that numerous modifications may be made to the exemplary embodiments, and other arrangements may be devised, without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the different dependent claims and the features described herein may be combined in ways different from those described in the original claims. It should also be understood that the features described in connection with separate embodiments may be used in other described embodiments.

Claims

1. An integrated imaging method for translational compensation and azimuth compression of space orbital targets by spaceborne radar, characterized in that, Including: Establish the relationship between the translational component coefficients of the space orbital target relative to the radar satellite and the transformation parameters; Perform fast and slow time fractional Fourier transform on the echo signal of the space orbital target based on the transformation parameters to obtain a transformed spectrum; Use the PSO optimization algorithm to search for the transformation parameters to minimize the image entropy of the transformed spectrum, and complete the imaging of the space orbital target.

2. The on-orbit radar translational compensation and azimuth compression integrated imaging method for space orbital targets according to claim 1, wherein The relationship expression between the translational component coefficients of the space orbital target and the transformation parameters is as follows: where p and χ are both transformation parameters, α = arccot(2β2k2), k1 and k2 are both translational component coefficients of the space orbital target relative to the radar satellite, β2 = 2f c / c, and f c is the carrier frequency of the transmitted signal, and c is the speed of light.

3. The on-orbit radar translational compensation and azimuthal compression integrated imaging method for space orbital targets according to claim 1, characterized in that The performing fast and slow time fractional Fourier transform on the echo signal of the space orbital target based on the transformation parameters to obtain a transformed spectrum includes: Perform range compression on the echo signal of the space orbital target to obtain a two-dimensional quadratic phase echo signal; Take the modulus after performing fast and slow time fractional Fourier transform on the two-dimensional quadratic phase echo signal based on the transformation parameters to obtain a transformed spectrum.

4. The on-orbit radar translational compensation and azimuth compression integrated imaging method for space orbital targets according to claim 3, characterized in that The performing range compression on the echo signal of the space orbital target to obtain a two-dimensional quadratic phase echo signal includes: The two-dimensional quadratic phase echo signal is obtained by the following formula where R i = k0 + x i + (k1 + y i ξ)t + k2t, R i is the slant range equation of the i-th scatter point i is the serial number of the scattering point on the space orbital target, A i is the scattering intensity of the i-th scattering point, T p is the pulse width, t and are the slow time and fast time respectively, c is the speed of light, B is the radar transmit signal bandwidth, j is the imaginary unit, f c is the carrier frequency of the transmit signal, (x i , y i ) represents the position coordinates of the i-th scattering point in the target coordinate system, k0, k1, and k2 are all translational component coefficients of the space orbital target relative to the radar satellite, and ξ is the rotational angular velocity of the space orbital target relative to the radar satellite.

5. The spaceborne radar translational motion compensation and azimuth compression integrated imaging method according to claim 4, characterized in that The performing fast and slow time fractional Fourier transform on the two-dimensional quadratic phase echo signal based on the transformation parameters includes: Perform fast and slow time fractional Fourier transform on the two-dimensional quadratic phase echo signal through the following formula: Among them, represents the result of the fast and slow time fractional Fourier transform, ρ1, ρ2, and ρ3 are all intermediate parameters, and the expressions are as follows: ρ1 = y i ξt + k0 + x i , ρ2 = μ + β2k1 sinα + β2y i ξsinα, α = arccot(2β2k2), β2 = 2f c / c, β1 = 2 / c, μ is the variable corresponding to the fractional domain axis obtained after the traditional fractional Fourier transform, χ is the transformation parameter, T r is the echo accumulation time.

6. The on-orbit radar translational compensation and azimuth compression integrated imaging method for space orbital targets according to claim 5, wherein Taking the modulus of the result of performing fast and slow time fractional Fourier transform on the two-dimensional quadratic phase echo signal to obtain a transformed spectrum includes: The transformed spectrum is obtained by the following formula 7. The on-orbit radar translational compensation and azimuthal compression integrated imaging method for space orbital targets according to claim 1, characterized in that The image entropy E of the transformed spectrum is calculated through the following formula: Among them, P ave is the transformed spectrum, r is the serial number of each unit of the transformed spectrum, 8. Spaceborne radar translational motion compensation and azimuth compression integrated imaging system for space orbital targets, characterized in that, Including: Construct a relationship unit for establishing the relationship between the translational component coefficients of the space orbital target relative to the radar satellite and the transformation parameters; A transformation unit for performing fast and slow time fractional Fourier transform on the echo signal of the space orbital target based on the transformation parameters to obtain a transformed spectrum; An imaging unit for using the PSO optimization algorithm to search for the transformation parameters to minimize the image entropy of the transformed spectrum and complete the imaging of the space orbital target.

9. Spaceborne radar integrated imaging equipment for translational compensation and azimuth compression of space orbital targets, characterized in that, The on-board radar for integrated imaging of translational compensation and azimuth dimension compression of space orbital targets includes a processor and a memory. At least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to implement the on-board radar for integrated imaging of translational compensation and azimuth dimension compression of space orbital targets as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, At least one instruction is stored in the computer storage medium, and the at least one instruction is loaded and executed by the processor to implement the on-board radar for integrated imaging of translational compensation and azimuth dimension compression of space orbital targets as described in any one of claims 1 to 7.

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