Terahertz frequency band sub-aperture division and rapid focusing imaging method and device

Abstract

The invention relates to a terahertz frequency band sub-aperture division and rapid focusing imaging method and device. The method comprises the following steps: firstly, based on a non-ideal rotation model, performing large-rotation-angle CBP accumulation imaging according to a baseband echo signal to obtain an energy accumulation curve changing along with a rotation angle; then, according to the fluctuation characteristics of the energy accumulation curve along with the change of the rotating angle, performing multi-term Gaussian function fitting on the energy accumulation curve, and performing sub-aperture division according to the wave crest and the wave trough of the fitted energy curve; and finally, iteratively updating the sub-rotation accumulation angle corresponding to each sub-aperture through an MPSO algorithm. And after the iterative updating is finished, generating a final two-dimensional imaging result according to the two-dimensional imaging result corresponding to each sub-aperture. The method does not depend on the extraction quality of the strong scattering point phase or time-frequency distribution of the target, and can quickly perform aperture division and fine imaging on the non-ideal rotating target.

Description

Technical Field

[0001] The present invention relates to the field of radar signal processing technology, and in particular to a method and device for sub-aperture division and rapid focusing imaging in a terahertz frequency band. Background Art

[0002] Terahertz radar has the natural advantages of large bandwidth and short wavelength. While achieving all-day and all-weather observation, it can also obtain ISAR high-resolution imaging and subtle Doppler characteristics of the target. In recent years, not only has the imaging algorithm developed rapidly, but the research and development level of terahertz radar devices has also made great progress, and the radar power has been greatly improved. Since there is no atmospheric attenuation problem in space, it makes it possible for terahertz radar to observe and image space targets. However, space targets are often non-cooperative and have certain mobility. The relative rotation accumulation angle and attitude change between the target and the radar are unknown. When performing high-resolution imaging of large-angle accumulation of the target, the first total rotation accumulation angle of the target and the imaging projection plane are crucial to high-resolution imaging, which brings huge challenges to high-resolution imaging of large angles that rely on specific projection directions and angle accumulation. Therefore, it is crucial to accurately sub-aperture the large-angle echo data with complex attitude changes for fine imaging of the target.

[0003] Traditional ISAR large-angle imaging subaperture division methods include: Gaussian fitting method based on target scattering energy, method based on phase linearity of strong scattering points, and optimal subaperture extraction based on time-frequency distribution. However, the above methods have certain limitations for targets with complex scattering characteristics. On the one hand, the method based on the radar cross-sectional area (RCS) change curve needs to be smoothed first. On the other hand, these methods depend on the extraction quality of the phase or time-frequency distribution of the target's strong scattering points to a certain extent. When performing large-angle imaging of non-ideal rotating targets, the influence of attitude change on imaging must be eliminated first. If the large-angle echo data is sequentially subjected to small-angle imaging and then attitude estimation and analysis, it is not only very time-consuming, but also largely dependent on manual interpretation. Summary of the invention

[0004] Based on this, it is necessary to provide a method and device for sub-aperture division and rapid focusing imaging in the terahertz frequency band that does not require smoothing processing, does not rely on the extraction quality of the phase or time-frequency distribution of the target's strong scattering points, and can perform rapid aperture division and fine imaging in order to address the above-mentioned technical problems.

[0005] A sub-aperture division and fast focusing imaging method in a terahertz frequency band, the method comprising:

[0006] Obtain baseband echo signals and construct non-ideal rotation models;

[0007] Based on the non-ideal rotation model, large-angle CBP accumulation imaging is performed according to the baseband echo signal to obtain an energy accumulation curve that changes with the rotation angle;

[0008] According to the fluctuation characteristics of the energy accumulation curve as the rotation angle changes, the energy accumulation curve is fitted with multiple Gaussian functions to obtain a fitted energy curve; and sub-aperture division is performed according to the peaks and troughs of the fitted energy curve;

[0009] The sub-rotation accumulation angles corresponding to each sub-aperture are iteratively updated by the MPSO algorithm; during the iterative update process, the image contrast of each sub-aperture is calculated to determine whether the image contrast reaches the maximum image contrast or whether the number of iterations reaches the maximum number of iterations; if not, the corresponding rotation speed and rotation acceleration are obtained according to the updated particle position, and each sub-rotation accumulation angle is updated by the rotation speed and the rotation acceleration; until the image contrast reaches the maximum image contrast or the number of iterations reaches the maximum number of iterations, the iterative update ends, and the final two-dimensional imaging result is generated according to the two-dimensional imaging results corresponding to each sub-aperture.

[0010] A sub-aperture division and fast focusing imaging device in a terahertz frequency band, the device comprising:

[0011] Signal acquisition and model building module, used to acquire baseband echo signals and build non-ideal rotation models;

[0012] An energy accumulation curve acquisition module, used to perform large-angle CBP accumulation imaging according to the baseband echo signal based on the non-ideal rotation model to obtain an energy accumulation curve that changes with the rotation angle;

[0013] A sub-aperture division module is used to perform multiple Gaussian function fitting on the energy accumulation curve according to the fluctuation characteristics of the energy accumulation curve as the rotation angle changes, so as to obtain a fitted energy curve; and perform sub-aperture division according to the peaks and troughs of the fitted energy curve;

[0014] The iterative update module is used to iteratively update the sub-rotation accumulation angles corresponding to each sub-aperture through the MPSO algorithm; during the iterative update process, the image contrast of each sub-aperture is calculated to determine whether the image contrast reaches the maximum image contrast or whether the number of iterations reaches the maximum number of iterations; if not, the corresponding rotation speed and rotation acceleration are obtained according to the updated particle position, and each sub-rotation accumulation angle is updated according to the rotation speed and the rotation acceleration; until the image contrast reaches the maximum image contrast or the number of iterations reaches the maximum number of iterations, the iterative update ends, and the final two-dimensional imaging result is generated according to the two-dimensional imaging results corresponding to each sub-aperture.

[0015] The above-mentioned terahertz frequency band sub-aperture division and fast focusing imaging method and device first obtain the baseband echo signal and construct a non-ideal rotation model; based on the non-ideal rotation model, large-angle CBP accumulation imaging is performed according to the baseband echo signal to obtain an energy accumulation curve that changes with the rotation angle; then, according to the fluctuation characteristics of the energy accumulation curve that changes with the rotation angle, multiple Gaussian functions are fitted to the energy accumulation curve to obtain the fitted energy curve; sub-aperture division is performed according to the peaks and troughs of the fitted energy curve; finally, the sub-rotation accumulation angle corresponding to each sub-aperture is iteratively updated by the MPSO algorithm; during the iterative update process, the image contrast of each sub-aperture is calculated to determine whether the image contrast reaches the maximum image contrast or the number of iterations reaches the maximum number of iterations; if not, the corresponding rotation speed and rotation acceleration are obtained according to the updated particle position, and each sub-rotation accumulation angle is updated by the rotation speed and the rotation acceleration; until the image contrast reaches the maximum image contrast or the number of iterations reaches the maximum number of iterations, the iterative update ends, and the final two-dimensional imaging result is generated according to the two-dimensional imaging results corresponding to each sub-aperture.

[0016] The present invention uses the energy accumulation curve to perform sub-aperture division, that is, directly calculate the image contrast in the CBP process. On the one hand, there is no need for smoothing processing, and on the other hand, it does not rely on the extraction quality of the phase or time-frequency distribution of the target's strong scattering points. Based on the fluctuation characteristics of the energy accumulation curve as the angle changes, the curve is fitted with multiple Gaussian functions. After fitting, rapid sub-aperture division can be performed based on the peaks and troughs, which can eliminate the impact of the target posture change or maneuvering speed change on the imaging accumulation. Then, the MPSO algorithm is combined with the CBP accumulation imaging to perform rapid focusing imaging of the sub-aperture. The maximum image contrast principle is adopted in the updating process, so that the numerical changes in the updating process are more obvious with the parameter changes, which is more conducive to the improvement of the parameter estimation accuracy, and the anti-noise robustness is also significantly improved, thereby achieving accurate matching of the angle information and high-resolution imaging of the target. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0018] Figure 1 It is a schematic flow chart of the sub-aperture division and fast focusing imaging method in the terahertz frequency band in Example 1;

[0019] Figure 2 It is a flow chart of the sub-aperture division and fast focusing imaging method in the terahertz frequency band in Example 1;

[0020] Figure 3 Schematic diagram of the satellite point target model and target angle change curve in Example 1; wherein, Figure 3 (a) is a schematic diagram of the satellite point target model. Figure 3 (b) is a schematic diagram of the target angle change curve;

[0021] Figure 4 This is a schematic diagram of the one-dimensional range image of a satellite point target and the large-angle CBP accumulation imaging results after obtaining the baseband echo signal in Example 1; wherein, Figure 4 (a) is a schematic diagram of the one-dimensional range image of a satellite point target. Figure 4 (b) is a schematic diagram of the large-angle CBP accumulation imaging results;

[0022] Figure 5 It is a schematic diagram of the energy accumulation curve and sub-aperture division results in Example 1;

[0023] Figure 6 A schematic diagram showing the comparison between the estimated turning angle curve and the actual turning angle information in Example 1;

[0024] Figure 7 The results of subaperture fine imaging and full aperture cumulative imaging in Example 1 are shown; wherein, Figure 7 (a) is the fine imaging result of sub-aperture 1, Figure 7 (b) is the fine imaging result of sub-aperture 2. Figure 7 (c) is the fine imaging result of sub-aperture 3. Figure 7 (d) is the fine imaging result accumulated at full aperture;

[0025] Figure 8 This is a structural block diagram of the sub-aperture division and fast focusing imaging device in the terahertz frequency band in Example 2.

[0026] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] It should be noted that in the present invention, descriptions such as "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0029] In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0030] The following will describe the implementation of the present invention in detail with reference to the accompanying drawings in the embodiment diagram of the present invention.

[0031] Example 1

[0032] This embodiment discloses a sub-aperture division and fast focusing imaging method in the terahertz frequency band, which uses an energy accumulation curve for sub-aperture division, that is, directly calculating the image contrast in the CBP process. On the one hand, no smoothing is required, and on the other hand, it does not depend on the extraction quality of the phase or time-frequency distribution of the strong scattering points of the target. Based on the fluctuation characteristics of the energy accumulation curve changing with the angle, the curve is fitted with multiple Gaussian functions. After fitting, fast sub-aperture division can be performed according to the peaks and valleys, which can eliminate the influence of the target posture change or maneuvering speed change on the imaging accumulation. Then, the MPSO (improved particle swarm optimization) algorithm is combined with the CBP (large angle convolution back projection) accumulation imaging to perform fast focusing imaging of the sub-aperture. The maximum image contrast principle is adopted in the updating process, so that the numerical changes in the updating process are more obvious with the parameter changes, which is more conducive to the improvement of the parameter estimation accuracy, and the anti-noise robustness is also significantly improved, thereby realizing accurate estimation of the angle information and high-resolution large angle imaging of the target. Compared with the existing methods, the method proposed in the present invention has the characteristics of simplicity and high efficiency.

[0033] The main concept of the present invention is: first, large-angle CBP accumulation imaging is performed according to the assumption that the target rotates at a uniform speed, and the image energy change is calculated while accumulating the imaging, that is, the image contrast is calculated, and an energy accumulation curve that changes with the angle is generated according to the image contrast. The energy accumulation curve is fitted with multiple Gaussian functions to achieve adaptive sub-aperture division, and then the divided sub-apertures are respectively subjected to fine imaging processing. Since the rotation speed and acceleration of the non-cooperative target are unknown, it is necessary to accurately estimate the rotation parameters of the target. The method of estimating the rotation parameters based on the optimal image quality is relatively accurate and robust. At this time, the two-dimensional precise imaging problem is transformed into the classic two-dimensional rotation parameters, that is, the estimation problem of the two parameters of rotation speed and rotation acceleration. The present invention adopts the MPSO algorithm with relatively fast search speed and not easy to fall into the local optimum, and the criterion function adopts the maximum image contrast.

[0034] In addition, when using the MPSO algorithm, a method of adaptively adjusting the two-parameter search step size is added on the basis of the particle swarm optimization algorithm. That is, a larger step size is first used to perform a large-range parameter search, and then the step size is gradually reduced according to the maximum value change of the image contrast solved by each generation of particles. At this time, the estimated rotation parameters and the corresponding well-focused two-dimensional imaging results are obtained.

[0035] like Figure 1 and 2 As shown, the sub-aperture division and fast focusing imaging method in the terahertz frequency band provided includes the following steps:

[0036] Step 201: Acquire a baseband echo signal and construct a non-ideal rotation model.

[0037] The baseband echo signal is the original signal that is transmitted back after modulation. The baseband echo signal can be used to measure distance, speed and target identification.

[0038] Due to the maneuverability and non-cooperativeness of the target, in actual work, during long-term observation, the target rotation is usually not completely uniform, and even contains multi-order acceleration. It is very difficult to directly perform multi-dimensional parameter search on large-angle echo data. However, if the large-angle echo data can be decomposed into several sub-aperture echoes with relatively small rotation angles, the rotational motion of the target in the shorter observation time corresponding to each sub-aperture echo can be approximately equivalent to uniformly accelerated rotation. Based on this, a non-ideal rotation model is constructed to improve the accuracy of target feature extraction.

[0039] Step 202: Based on the non-ideal rotation model, large-angle CBP accumulation imaging is performed according to the baseband echo signal to obtain an energy accumulation curve that varies with the rotation angle.

[0040] It can be understood that the key to the quality of CBP imaging lies in whether the solved angle information accurately matches the actual rotation state of the target, and the accuracy of the angle information affects the quality of imaging. In the process of energy accumulation with the angle, if the angle information deviates greatly from the actual state, or the target has posture adjustment behavior during the rotation process, the energy accumulation will be dispersed, and the imaging result of large angle accumulation will inevitably lead to deterioration of the image result. The non-ideal rotation model of the present invention approximates the rotational motion of the target to multiple shorter uniform acceleration motions, and based on this, large-angle CBP accumulation imaging is performed. While accumulating the imaging, the image energy change is calculated, that is, the image contrast is calculated. The energy accumulation curve is described by the image contrast, and adaptive sub-aperture division is performed according to the energy accumulation curve.

[0041] Step 203, according to the fluctuation characteristics of the energy accumulation curve with the change of the rotation angle, multiple Gaussian functions are fitted to the energy accumulation curve to obtain a fitted energy curve; and sub-aperture division is performed according to the peaks and troughs of the fitted energy curve.

[0042] It can be understood that due to the non-ideal rotation of the target, the energy accumulation curve of its two-dimensional imaging result will inevitably show ups and downs with the change of the angle. Then, when the image contrast is enhanced, that is, the energy accumulation curve increases, it means that the target rotation is basically matched with the set angle information. Otherwise, it means that the target has posture adjustment or the angle information matching error is large. Based on this characteristic, the sub-aperture can be adaptively divided based on the peaks and troughs of the energy change curve. The division process is summarized as follows:

[0043] (1) When performing large-angle CBP accumulation imaging, it is first assumed that the target rotation speed is constant. Then, during the convolution back projection process, the image contrast of the two-dimensional imaging results after projection at different accumulation angles is calculated to obtain the energy accumulation curve that changes with the rotation angle.

[0044] (2) Rapid sub-aperture division is performed based on the obtained energy accumulation curve: Based on the fluctuation characteristics of the energy accumulation curve as the rotation angle changes, the curve is fitted with multiple Gaussian functions. After fitting, rapid sub-aperture division is performed based on the peaks and troughs.

[0045] Step 204, iteratively update the sub-rotation accumulation angles corresponding to each sub-aperture through the MPSO algorithm; during the iterative update process, calculate the image contrast of each sub-aperture, and determine whether the image contrast reaches the maximum image contrast or whether the number of iterations reaches the maximum number of iterations; if not, obtain the corresponding rotation speed and rotation acceleration according to the updated particle position, and update each sub-rotation accumulation angle through the rotation speed and rotation acceleration; until the image contrast reaches the maximum image contrast or the number of iterations reaches the maximum number of iterations, the iterative update ends, and the final two-dimensional imaging result is generated according to the two-dimensional imaging results corresponding to each sub-aperture.

[0046] It can be understood that when performing iterative updates, the MPSO algorithm is combined with CBP accumulation imaging in the imaging process, and the maximum image contrast criterion function is used for iterative updates. In addition, the MPSO algorithm adds a method of adaptively adjusting the search step of two parameters on the basis of the particle swarm optimization algorithm. First, a large step size is used for large-range parameter search, that is, when the position and speed of the target are iteratively updated, the acceleration constant is adaptively adjusted to control the search range; then, the two parameters of rotation speed and rotation acceleration are obtained in the particle iteration process, and the step size is gradually reduced according to the changes in the image contrast solved by each sub-aperture until the search ends.

[0047] In the specific implementation process of step 201, a signal is transmitted through the terahertz broadband radar system, and the transmitted signal is shown in the following formula:

[0048]

[0049] According to the distance R between the radar and the center of the turntable ref , get the reference echo signal, the expression is:

[0050]

[0051] In the formula, is the distance time; t m is the azimuth slow time; T p is the pulse width of pulse radar or the sweep period of frequency modulated continuous wave radar; f c is the radar carrier frequency; γ is the modulation rate; rect(·) represents a square pulse; exp represents an exponential function with e as the base; j represents an imaginary unit; the target scattering coefficient defaults to 1.

[0052] Assuming that the target echo data acquired by the terahertz radar has been processed by translation compensation, the ISAR imaging can be simplified to a turntable imaging model under far-field conditions, that is, the target rotates around the rotation center. Assuming that the distance between the rotating target and the radar is R, a rectangular coordinate system (xoy) is established with the center of the turntable as the origin. The distance from the target point P (x0, y0) to the radar in the turntable mode can be expressed as R = R0 + x0 sinθ + y0 cosθ, where R0 is the initial distance from the radar to the center of the turntable, and θ is the target rotation accumulation angle. The received echo can be written as:

[0053]

[0054] Assuming that the reference distance is equal to the center distance of the turntable, the baseband echo signal is obtained by demodulating the line frequency modulation and compensating the residual video phase term. The expression is:

[0055] S(k,θ)=∫ y∫ x f(x,y)exp[-jk(-xsinθ+ycosθ)]dxdy (4)

[0056] Where f(x, y) represents the final two-dimensional imaging result to be solved; k represents the radial wave number; (x, y) represents the coordinate point of the target; θ represents the first total rotation accumulation angle of the target.

[0057] Constructing a non-ideal rotation model includes: dividing the large-angle echo data into a number of sub-aperture echoes with relatively small rotation angles, and then the first total rotation accumulation angle is equal to the sum of the sub-rotation accumulation angles. Based on this, the non-ideal rotation model is constructed. The expression of the non-ideal rotation model is:

[0058]

[0059] Where, θ represents the first total rotation accumulation angle of the target; S represents the total number of sub-apertures; θ i represents the sub-rotation accumulation angle corresponding to the i-th sub-aperture.

[0060] After dividing the large-angle echo data into several sub-aperture echoes with relatively small angles, the rotational motion of the target can be approximately equivalent to uniformly accelerated rotation within the relatively short observation time corresponding to the sub-angle. Assuming that the rotation speed of the target corresponding to a certain sub-aperture data is ω i , the acceleration is α i , then the sub-rotation accumulation angle expression is:

[0061]

[0062] In the formula, θ i (t m ) represents the slow time t in the ith sub-aperture. m The changing rotation accumulation angle; θ i0 represents the initial position of the rotation angle of the i-th sub-aperture; ω i Indicates the rotation speed of the target; α i Indicates the rotational acceleration of the target.

[0063] In the specific implementation process of step 202, it can be seen from the expression of the non-ideal rotation model that the baseband echo signal is actually the two-dimensional spectrum F(k x ,k y ) is the slice information when the azimuth angle is the first total rotation accumulation angle θ, and the expression is:

[0064] S(k,θ)=F(-ksinθ,kcosθ) (7)

[0065] According to the converted baseband echo signal, large-angle CBP accumulation imaging is performed to generate a two-dimensional imaging result. The expression of the two-dimensional imaging result is:

[0066]

[0067] Let g(x',θ) be the inverse Fourier transform of kS(k,θ), then the two-dimensional imaging result can also be expressed as:

[0068]

[0069] In the formula, FFT k (·) represents one-dimensional fast Fourier transform; θ M represents the second total rotation accumulation angle of the i-th sub-aperture; θ represents the first total rotation accumulation angle of the target; k max and k min They represent the maximum and minimum values ​​of the radial wavenumber respectively.

[0070] Among them, x'=-xsinθ+ycosθ, with the accumulation of azimuth angle, the two-dimensional imaging result after projection is obtained. i When , the corresponding subaperture two-dimensional imaging result is f i (x,y).

[0071] Then the image contrast of the two-dimensional imaging result is calculated, and the expression is:

[0072]

[0073] Where C(·) represents the image contrast; N r and N a Respectively represent the two-dimensional image f corresponding to the i-th sub-aperture i The total number of points of (x,y) in the range and azimuth directions; n1 and n2 correspond to their point indexes respectively.

[0074] The energy accumulation curve that changes with the rotation angle is generated according to the image contrast, and the expression is:

[0075]

[0076] In the specific implementation process of step 203, the fitted energy curve expression is:

[0077]

[0078] Where S represents the total number of subapertures; a i 、b i 、c i Represents the coefficients in a multinomial Gaussian function; exp represents an exponential function with e as the base.

[0079] In the specific implementation process of step 204, when the sub-rotation accumulation angle corresponding to each sub-aperture is iteratively updated by the MPSO algorithm, it also includes:

[0080] Initialize the position and speed information of each sub-aperture target. The position information includes the rotation speed ω i and the rotational acceleration α i , so it can be regarded as initializing the rotation speed ω0 and rotation acceleration α0 of each sub-aperture target. In the iterative update process, the acceleration constant is adaptively adjusted according to the relationship between the image contrast increase value corresponding to the i-th sub-aperture in two adjacent iterations and the preset threshold to control the search range of position and speed.

[0081] Specifically, during the entire particle search process, the expressions for the velocity and position of the target in each sub-aperture are as follows:

[0082]

[0083] In the formula, and Respectively represent the velocity and position of the i-th particle at the g-th iteration; and p g -1,gbest They represent the optimal position of the ith particle after g-1 iterations and the global optimal position of the entire particle swarm after g-1 iterations respectively; n1 and n2 represent acceleration constants, both of which are random numbers between 0 and 1.

[0084] The present invention adaptively adjusts the acceleration constants η1 and η2. When the value of the increase in image contrast corresponding to the i-th particle during two iterations is greater than the threshold ε, it indicates that a better solution has been found. At this time, the acceleration constants n1 and n2 are adjusted to between 0.2 and 0.9. When the value of the increase in image contrast is less than the threshold ε, the acceleration constants n1 and n2 are increased and adjusted to between 0.9 and 2. First, a large-scale search is performed in this way to control the search range of position and speed, and then the step size is gradually reduced according to the change in image contrast solved by each sub-aperture until the minimum step size L is reached to find the optimal solution.

[0085] In the process of iterative updating, CBP imaging is performed on each sub-aperture. As the azimuth angle accumulates, when the sub-rotation accumulation angle corresponding to the i-th sub-aperture is θ i When , the two-dimensional imaging result f corresponding to the i-th sub-aperture is calculated according to the expression of the two-dimensional imaging result i (x, y); and calculate the image contrast corresponding to the i-th sub-aperture according to the image contrast expression.

[0086] In the process of estimating two-dimensional rotation parameters, the expressions of rotation speed and rotation acceleration are:

[0087]

[0088] In the formula, represents the estimated rotation speed; represents the estimated rotation acceleration; C(·) represents the image contrast; f i (x,y) represents the two-dimensional image corresponding to the i-th sub-aperture.

[0089] Substituting the rotation speed and rotation acceleration into formula (6), the sub-rotation accumulation angle under the corresponding sub-aperture echo can be obtained, and the high-resolution two-dimensional imaging result of this sub-aperture can be obtained at the same time.

[0090] In one of the embodiments, the method proposed in the present invention is verified. Taking a broadband radar system with a carrier frequency of 220 GHz and a bandwidth of 10 GHz as an example, a satellite point target model is used as the observed target, and the total length of the target is about 1.7 m. During the terahertz radar observation process, the relative total rotation angle between the target and the radar is set to about 50°, and three different uniform acceleration / deceleration rotations are performed during the rotation. The target model and the target angle change curve are shown in FIG. Figure 3 shown.

[0091] After obtaining the target's radar echo, its one-dimensional distance image is as follows: Figure 4 As shown in (a), if it is assumed that the target rotates uniformly and the echo data is directly subjected to large-angle CBP imaging, the imaging result is as follows: Figure 4 As shown in (b), the imaging result is obviously severely defocused. For non-ideal rotating targets, the direct use of the CBP algorithm fails.

[0092] Therefore, based on the proposed sub-aperture division method, the energy accumulation curve is first calculated and multiple Gaussian curves are fitted. Then, adaptive sub-aperture division is performed according to formula (12). The energy accumulation curve and the corresponding sub-aperture division results are shown in the figure. Figure 5 As shown, the dotted line is the energy accumulation curve, and the solid line is the multi-Gaussian fitting curve.

[0093] Depend on Figure 5 It can be seen that after fitting the energy accumulation curve of the large turning angle with multiple Gaussian functions, three sub-apertures are basically divided. The starting and ending points of each sub-aperture are basically consistent with the time when the target performs maneuvering and acceleration, which proves the correctness of the proposed method.

[0094] According to the result of sub-aperture division, the rotation parameters of each sub-aperture data are estimated under the maximum image contrast criterion. According to the rotation parameters of each segment, the estimated rotation angle curve with slow time is as follows: Figure 6 As shown by the dotted line in Figure 6The actual rotation angle information represented by the solid line is basically consistent with that of the original image, which proves the accuracy of the sub-aperture rotation parameter estimation method. The imaging results obtained by fine imaging of each sub-aperture and the full aperture accumulation imaging results are shown in Figure 2. Figure 7 shown.

[0095] Comparing the imaging results, it can be seen that the fine imaging focusing effect of each sub-aperture is good, and the imaging result of full aperture accumulation based on the rotation angle information of each sub-aperture is also better than Figure 3 (b) Direct CBP imaging at full aperture. Due to the large matching error of the angle information, serious defocusing occurs. The imaging results prove the correctness and effectiveness of the proposed algorithm.

[0096] Although this embodiment Figure 1 and Figure 2 The steps in the figure are shown in sequence as indicated by the arrows, but the steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of the steps, and the steps can be executed in other orders. Figure 1 and Figure 2 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0097] Example 2

[0098] Based on the sub-aperture division and fast focusing imaging method in the terahertz frequency band in Example 1, this embodiment discloses a sub-aperture division and fast focusing imaging device in the terahertz frequency band, such as Figure 8 As shown, the sub-aperture division and fast focusing imaging device in the terahertz frequency band includes: a signal acquisition and model building module 401, an energy accumulation curve acquisition module 402, a sub-aperture division module 403 and an iterative update module 404, wherein:

[0099] The signal acquisition and model building module 401 is used to acquire the baseband echo signal and build a non-ideal rotation model.

[0100] The energy accumulation curve acquisition module 402 is used to perform large-angle CBP accumulation imaging according to the baseband echo signal based on the non-ideal rotation model to obtain an energy accumulation curve that changes with the rotation angle.

[0101] The sub-aperture division module 403 is used to fit the energy accumulation curve with multiple Gaussian functions according to the fluctuation characteristics of the energy accumulation curve with the rotation angle to obtain the fitted energy curve; and perform sub-aperture division according to the peaks and troughs of the fitted energy curve.

[0102] The iterative update module 404 is used to iteratively update the sub-rotation accumulation angles corresponding to each sub-aperture through the MPSO algorithm; during the iterative update process, the image contrast of each sub-aperture is calculated to determine whether the image contrast reaches the maximum image contrast or whether the number of iterations reaches the maximum number of iterations; if not, the corresponding rotation speed and rotation acceleration are obtained according to the updated particle position, and each sub-rotation accumulation angle is updated according to the rotation speed and rotation acceleration; until the image contrast reaches the maximum image contrast or the number of iterations reaches the maximum number of iterations, the iterative update ends, and the final two-dimensional imaging result is generated according to the two-dimensional imaging results corresponding to each sub-aperture.

[0103] In this embodiment, the specific working process and working principle of the signal acquisition and model building module 401, the non-ideal rotation model 402, the sub-aperture division module 403 and the iterative update module 404 are the same as those in the method in Embodiment 1, so they are not described in detail in this embodiment. Each unit module can be implemented in whole or in part by software, hardware and a combination thereof, and each unit module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above unit modules.

[0104] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0105] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0106] The above-mentioned embodiments only express several implementation modes of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A sub-aperture division and fast focusing imaging method in the terahertz frequency band, characterized in that: The method comprises: Obtain baseband echo signals and construct non-ideal rotation models; Based on the non-ideal rotation model, large-angle CBP accumulation imaging is performed according to the baseband echo signal to obtain an energy accumulation curve that changes with the rotation angle; According to the fluctuation characteristics of the energy accumulation curve as the rotation angle changes, the energy accumulation curve is fitted with multiple Gaussian functions to obtain a fitted energy curve; and sub-aperture division is performed according to the peaks and troughs of the fitted energy curve; The sub-rotation accumulation angles corresponding to each sub-aperture are iteratively updated by the MPSO algorithm; during the iterative update process, the image contrast of each sub-aperture is calculated to determine whether the image contrast reaches the maximum image contrast or whether the number of iterations reaches the maximum number of iterations; if not, the corresponding rotation speed and rotation acceleration are obtained according to the updated particle position, and each sub-rotation accumulation angle is updated by the rotation speed and the rotation acceleration; until the image contrast reaches the maximum image contrast or the number of iterations reaches the maximum number of iterations, the iterative update ends, and the final two-dimensional imaging result is generated according to the two-dimensional imaging results corresponding to each sub-aperture.

2. The method for sub-aperture division and rapid focusing imaging in the terahertz frequency band according to claim 1, characterized in that: Get baseband echo signal, including: The signal is transmitted through the terahertz broadband radar system, and a reference echo signal is obtained according to the distance between the radar and the center of the turntable; Assuming that the target rotates around the rotation center, a rectangular coordinate system is established with the center of the turntable as the coordinate origin to calculate the reference distance between the rotating target and the radar; If the reference distance is equal to the turntable center distance, the baseband echo signal is obtained by demodulating the line frequency modulation and compensating the residual video phase term.

3. The sub-aperture division and fast focusing imaging method in the terahertz frequency band according to claim 2, characterized in that: The baseband echo signal expression is: S(k,θ)=∫ y ∫ x f(x,y)exp[-jk(-xsinθ+ycosθ)]dxdy; Wherein, f(x, y) represents the final two-dimensional imaging result to be solved; k represents the radial wave number; j represents the imaginary unit; (x, y) represents the coordinate point of the target; θ represents the first total rotation accumulation angle of the target.

4. The sub-aperture division and fast focusing imaging method in the terahertz frequency band according to claim 1, characterized in that: Construct non-ideal rotation models, including: It is proposed to divide the large-angle echo data into several sub-aperture echoes with relatively smaller rotation angles. Then the first total rotation accumulation angle is equal to the sum of the sub-rotation accumulation angles, based on which a non-ideal rotation model is constructed.

5. The method for sub-aperture division and rapid focusing imaging in the terahertz frequency band according to claim 4, characterized in that: The non-ideal rotation model expression is: Where, θ represents the first total rotation accumulation angle of the target; S represents the total number of sub-apertures; θ i represents the sub-rotation accumulation angle corresponding to the i-th sub-aperture.

6. The method for sub-aperture division and rapid focusing imaging in the terahertz frequency band according to claim 5, characterized in that: The sub-rotation accumulation angle expression is: Where, t m Indicates the direction of slow time; θ i (t m ) represents the slow time t in the ith sub-aperture. m Varying rotation accumulation angle; θ i0 represents the initial position of the rotation angle of the i-th sub-aperture; ω i Indicates the rotation speed of the target; α i Indicates the rotational acceleration of the target.

7. The method for sub-aperture division and rapid focusing imaging in the terahertz frequency band according to claim 1, characterized in that: Based on the non-ideal rotation model, large-angle CBP accumulation imaging is performed according to the baseband echo signal to obtain an energy accumulation curve that changes with the rotation angle, including: Based on the non-ideal rotation model, converting the baseband echo signal into slice information of a two-dimensional spectrum when the azimuth angle is a first total rotation accumulation angle; Large-angle CBP accumulation imaging is performed based on the converted baseband echo signal to generate a two-dimensional imaging result, and the image contrast of the two-dimensional imaging result is calculated. According to the image contrast, an energy accumulation curve that changes with the angle is generated.

8. The method for sub-aperture division and rapid focusing imaging in the terahertz frequency band according to claim 7, characterized in that: The baseband echo signal is converted into slice information of a two-dimensional spectrum when the azimuth angle is the first total rotation accumulation angle, and the expression is: S(k,θ)=F(-ksinθ,kcosθ); Where k represents the radial wave number; θ represents the first total rotation accumulation angle of the target.

9. The method for sub-aperture division and rapid focusing imaging in the terahertz frequency band according to claim 8, characterized in that: The expression of the two-dimensional imaging result is: In the formula, θ M represents the second total rotation accumulation angle of the i-th sub-aperture; g(x',θ) is the inverse Fourier transform of kS(k,θ); θ represents the first total rotation accumulation angle of the target.

10. The method for sub-aperture division and rapid focusing imaging in the terahertz frequency band according to claim 9, characterized in that: The image contrast expression is: Where C(·) represents the image contrast; N r and N a Respectively represent the two-dimensional image f corresponding to the i-th sub-aperture i The total number of points of (x,y) in the range and azimuth directions; n1 and n2 correspond to their point indexes respectively.

11. The method for sub-aperture division and rapid focusing imaging in the terahertz frequency band according to claim 10, characterized in that: The energy accumulation curve expression is:

12. The method for sub-aperture division and rapid focusing imaging in the terahertz frequency band according to claim 11, characterized in that: The fitted energy curve expression is: Where S represents the total number of subapertures; a i , b i 、c i Represents the coefficients in a multinomial Gaussian function; exp represents an exponential function with e as the base.

13. The method for sub-aperture division and rapid focusing imaging in the terahertz frequency band according to any one of claims 9 to 12, characterized in that: When the sub-rotation accumulation angle corresponding to each sub-aperture is iteratively updated by the MPSO algorithm, it also includes: Initialize the position and speed information of each sub-aperture target. During the iterative update process, the acceleration constant is adaptively adjusted according to the relationship between the image contrast increase value corresponding to the i-th sub-aperture in two adjacent iterations and the preset threshold to control the search range of position and speed.

14. The method for sub-aperture division and rapid focusing imaging in the terahertz frequency band according to claim 13, characterized in that: During the iterative update process, as the azimuth angle accumulates, when the sub-rotation accumulation angle corresponding to the i-th sub-aperture is θ i When , the two-dimensional imaging result f corresponding to the i-th sub-aperture is calculated according to the expression of the two-dimensional imaging result i (x, y); and calculate the image contrast corresponding to the i-th sub-aperture according to the image contrast expression.

15. The method for sub-aperture division and rapid focusing imaging in the terahertz frequency band according to claim 14, characterized in that: The expressions for rotational speed and rotational acceleration are: In the formula, represents the estimated rotation speed; represents the estimated rotation acceleration; C(·) represents the image contrast; f i (x,y) represents the two-dimensional image corresponding to the i-th sub-aperture.

16. A sub-aperture division and fast focusing imaging device in the terahertz frequency band, characterized in that: The device comprises: Signal acquisition and model building module, used to acquire baseband echo signals and build non-ideal rotation models; An energy accumulation curve acquisition module, used to perform large-angle CBP accumulation imaging according to the baseband echo signal based on the non-ideal rotation model to obtain an energy accumulation curve that changes with the rotation angle; A sub-aperture division module is used to perform multiple Gaussian function fitting on the energy accumulation curve according to the fluctuation characteristics of the energy accumulation curve as the rotation angle changes, so as to obtain a fitted energy curve; and perform sub-aperture division according to the peaks and troughs of the fitted energy curve; The iterative update module is used to iteratively update the sub-rotation accumulation angles corresponding to each sub-aperture through the MPSO algorithm; during the iterative update process, the image contrast of each sub-aperture is calculated to determine whether the image contrast reaches the maximum image contrast or whether the number of iterations reaches the maximum number of iterations; if not, the corresponding rotation speed and rotation acceleration are obtained according to the updated particle position, and each sub-rotation accumulation angle is updated according to the rotation speed and the rotation acceleration; until the image contrast reaches the maximum image contrast or the number of iterations reaches the maximum number of iterations, the iterative update ends, and the final two-dimensional imaging result is generated according to the two-dimensional imaging results corresponding to each sub-aperture.

Images