CN104950305B - A kind of real beam scanning radar angle super-resolution imaging method based on sparse constraint - Google Patents

Abstract

The invention discloses a real-beam scanning radar angle super-resolution imaging method based on sparse constraints, comprising the following steps: S1, echo modeling, establishing a scanning radar echo data model based on the geometric relationship between the real-beam scanning radar and the target; S2. Perform range-wise pulse compression on the echo data to achieve high resolution in the range direction; S3. Express the pulse-compressed echo data as a convolution model of the antenna beam and the scattering coefficient of the observed scene; S4. Obtain according to S3 The convolution model establishes the maximum a posteriori objective function and derives the maximum a posteriori solution; S5. Accurately restores the original target distribution through an adaptive iterative method. The invention uses Rayleigh distribution to characterize clutter characteristics, and utilizes sparse constraints to reflect target distribution characteristics, suppresses the influence of noise on imaging results while inverting target distribution, makes the angle super-resolution processing results closer to the actual target distribution, and realizes Real-beam scanning radar angle super-resolution imaging.

Description

A real-beam scanning radar angle super-resolution imaging method based on sparse constraints

technical field

The invention belongs to the technical field of radar imaging, and in particular relates to a real-beam scanning radar angle super-resolution imaging method based on sparse constraints.

Background technique

The high-resolution imaging of real beam scanning radar has great application value in the fields of ship navigation, tower monitoring and remote early warning. At the same time, this imaging mode also has the advantages of small size and low cost, so this imaging mode has broad application prospects. The real beam scanning radar irradiates the imaging area successively at a fixed scanning speed, and obtains the two-dimensional echo signal of the radar active area by transmitting a linear frequency modulation signal (LFM) and receiving the echo signal. Since the transmitted signal dimension is an LFM signal, high range resolution can be achieved by using pulse compression technology. In azimuth, the real beam azimuth resolution is given by decision, where λ is the radar wavelength, and D is the antenna aperture size. Although the azimuth angle resolution of the real beam is much lower than the distance resolution, the signal processing method can break through the antenna wavelength and aperture limitations and significantly improve the azimuth resolution. rate to achieve real-beam radar angle super-resolution imaging.

The literature "Ly C, Dropkin H, Manitius A Z. Extension of the music algorithm tomillimeter-wave (mmw) real-beam radar scanning antennas. AeroSense 2002." According to the echo characteristics of real-beam scanning radar, it is proposed to use spectrum estimation The MUSIC algorithm realizes scanning radar azimuth super-resolution imaging, but this method needs enough snapshots to accurately estimate the covariance matrix of the noise, which is difficult to achieve in the actual mechanical scanning radar application. The angular super-resolution performance of the method will be severely degraded.

Literature "Y. Zhang, Y. Zhang, W. Li, Y. Huang, and J. Yang. Angular superresolution for real beam radar with iterative adaptive approach. in Geoscience and RemoteSensing Symposium (IGARSS), 2013IEEE International.IEEE,2013:3100 -3103” proposed an adaptive iterative angle super-resolution method based on real-beam scanning radar, which is based on the weighted least weighted squares criterion. The calculation complexity of this method is too large, it will occupy a large amount of system resources and seriously affect the real-time performance of imaging, and it is difficult to be extended to practical applications.

The literature "Huang Y, Zha Y, Zhang Y, et al. Real-beam scanning radar angular super-resolution via sparse deconvolution. Geoscience and Remote Sensing Symposium (IGARSS), 2014IEEE International. IEEE, 2014:3081-3084." The radar azimuth echo is established as a convolution model of the antenna pattern and the target scattering coefficient, and the target scene is reconstructed through the deconvolution algorithm to achieve real beam radar angular resolution. However, the noise assumed by this method obeys the Poisson distribution characteristic and does not conform to the actual radar imaging characteristics, so the imaging performance of the algorithm will drop sharply under low signal-to-noise ratio.

Contents of the invention

The purpose of the present invention is to overcome the deficiencies of the prior art, to provide a method that uses Rayleigh distribution to characterize clutter characteristics, and uses sparse constraints to reflect the target distribution characteristics, and suppresses the influence of noise on imaging results while inverting the target distribution. The estimation accuracy of Rayleigh distribution statistical parameters is improved, and a real-beam scanning radar angle super-resolution imaging method based on sparse constraints is realized.

The purpose of the present invention is achieved by the following technical solutions, a real-beam scanning radar angle super-resolution imaging method based on sparse constraints, comprising the following steps:

S1. Echo modeling, based on the geometric relationship between the real beam scanning radar and the target, the echo data model of the scanning radar is established;

S2. Perform range-wise pulse compression on the echo data to achieve high resolution in the range direction;

S3. Expressing the pulse-compressed echo data as a convolution model of the antenna beam and the scattering coefficient of the observed scene;

S4. Establishing a maximum a posteriori objective function according to the convolution model obtained in S3, and deriving a maximum a posteriori solution;

S5. Accurately restore the original target distribution through an adaptive iterative method, including the following sub-steps:

S51. Calculating the iterative initial value: using the TIKHONOV regularization method and the maximum likelihood estimation method to obtain the iterative initial value of the two parameters of the solution of the objective function and the Rayleigh distribution statistical parameter;

S52. Construct an iterative expression according to the maximum a posteriori solution obtained in S4;

S53. Substituting the iteration initial value into the iteration expression to obtain a new maximum a posteriori solution;

S54. Bring the maximum a posteriori solution obtained in S53 into the calculation formula of the statistical parameter of the Rayleigh distribution, and update the value of the statistical parameter of the Rayleigh distribution;

S55. Substituting the maximum a posteriori solution obtained in S53 and the Rayleigh distribution statistical parameter value obtained in S54 into the iterative expression to obtain a new maximum a posteriori solution;

S56. Steps S54 and S55 are repeated until the result of the iterative expression is compared with the result of the previous iterative expression and satisfies the iteration convergence condition, and the result of the iterative expression is recorded as the real beam scanning radar angle super-resolution imaging result.

Further, the specific implementation method of the step S1 is as follows: the angle of view of the radar at the height H is below Scan the -ψ～ψ imaging area in clockwise order; at the initial moment, the initial slant distance between the radar antenna and the target at the center of the scene is r ₀ , and the corresponding coordinates of each target point in the scene are ( _xi , y _i ), each target The azimuth angle between the target and the radar corresponds to θ _i , and the slant distance between each target and the radar is r _i ;

Let the transmitted signal be a chirp signal Among them, rect( ) represents a rectangular signal, which is defined as τ is the fast time variable in the distance direction, T is the duration of the transmitted pulse, c is the speed of light, λ is the wavelength, and K _r is the frequency modulation slope; in order to ensure that the theory is consistent with the actual verification, the received echo is discretely processed; the discretized echo The wave analytical expression is:

Among them, Ω is the range of the target scene, θ is the antenna beam width, f( _xi ,y _i ) is the scattering function of the target at point ( _xi ,y _i ); ω is the scanning speed of the antenna, T _β is the Dwell time for beamwidth.

Further, the specific implementation method of the step S2 includes the following sub-steps:

S21. Constructing a range-wise pulse pressure reference signal:

in, Indicates the distance to the reference time;

S22. Combine s _ref with echo data Carry out the maximum autocorrelation operation, and the two-dimensional signal after pulse compression is obtained as follows:

Among them, B is the transmission signal bandwidth.

Further, the specific implementation method of the step S3 is: the convolution model of the echo signal is expressed as:

Among them, s=[s(1, 1), s(1, 2), ..., s(N, 1), ..., s(N, M)] ^T is a vector of NM × 1 dimension, which is to combine all pulses The measured value of the compressed echo signal is rearranged in the azimuth direction according to the order of the distance unit, and the superscript T represents the transpose operation;

f=[f(1,1), f(1,2),..., f(N, 1),..., f(N, M)] ^T is a vector of NM×1 dimension, which is all The magnitude of the unknown target is rearranged in the azimuth direction according to the order of range cells;

n=[n(1,1), n(1,2),…,n(N,1),…,n(N,M)] ^T , which is a NM×1-dimensional vector, representing clutter and interference Signal component, subject to statistically independent Rayleigh distribution;

H is a matrix of NM×NM dimensions, which is composed of a convolution measurement matrix H _M×N , where H _M×N =[h ₁ ,h ₂ ,...,h _M ].

Further, the specific implementation method of the step S4 is: on the basis of the Bayesian formula, through the given noise statistical characteristics, combined with the prior information of the sparse target distribution, the maximum a posteriori probability deconvolution super-resolution method is introduced , to realize convolution inversion, which specifically includes the following sub-steps:

S41. For the formula (3), the posterior probability of the echo data is expressed as:

Among them, p( ) represents the probability density function; according to the maximum a posteriori criterion, the deconvolution problem is transformed into solving the optimal solution f so that it satisfies:

in, is the maximum a posteriori estimation of the objective function; p(f/s), p(s/f) and p(f) respectively represent the posterior probability, likelihood probability and prior probability of the target of the echo data;

S42. Assuming that the clutter or interference signal at each sampling point in the real beam scanning radar echo signal obeys a statistically independent Rayleigh distribution, then the likelihood probability is expressed as:

Among them, i is each discrete point target,

^σ2 is a statistical parameter in the Rayleigh distribution;

S43. Select the sparseness feature as the regularization constraint item, and the probability density of target scattering is:

Among them, 0<q≤1; when q=1, p(f)∝exp(-2||f|| ₁ ) is a Laplace distribution; when q→1, the target probability is

S44, obtain maximum a posteriori objective function according to (6) formula and (7) formula:

Taking the negative natural logarithm of formula (8), we get:

Find the gradient operation of formula (9) with respect to f, and get:

Among them, (·) ^T represents the transposition operation, P=diag{p ₁ ,...,p _NM }, p _i =|f _i | ^2-q ;

S45, because formula (10) is a non-linear function, therefore, can only obtain the result of approximating original scene by iterative method, make formula (10) be zero, obtain the maximum a posteriori solution about f as:

Further, the specific implementation of step S51 includes the following steps:

S511. Using the TIKHONOV regularization method, calculate the rough estimation result of the original scene as:

f＝(H ^T H+βI) ^-1 H ^T s (12)

Among them, β is a regularization parameter, and I is an NM×NM dimensional unit diagonal matrix;

S512. Estimate the statistical parameters of the Rayleigh distribution using the maximum likelihood estimation method. First, for an NM-dimensional clutter vector g=[g ₁ ,..., _gi ] (i=1,...,NM ), after taking the logarithm of the joint likelihood function of this vector, we get:

Calculate the derivative of formula (13) with respect to σ, and let the result be zero, we get:

Therefore, the maximum likelihood estimate of ^σ2 is:

For real-beam scanning radar imaging, g _i =s _i -(Hf) _i , so the statistical parameters of the Rayleigh distribution are calculated by using TIKHONOV regularization calculation results combined with formula (15):

Further, the iterative expression constructed in step S52 is:

Among them, the initial value of the iteration is (12) formula and the result of bringing (12) formula into (16) formula, k+1 and k are the number of iterations, P _k =diag{(p ₁ ) _k ,...,( p _NM ) _k }, (p _i ) _k =|(f _i ) _k | ^2−q .

Further, the convergence condition in the step S56 is:

||f _k+1 -f _k || ² <ε (18)

Wherein, f _k+1 and f _k are the results of two adjacent iterations, and ε is a preset threshold.

The beneficial effects of the present invention are: using Rayleigh distribution to characterize clutter characteristics, and using sparse constraints to reflect target distribution characteristics, suppressing the influence of noise on imaging results while inverting target distribution, in addition, in iterative processing, using The adaptive method improves the estimation accuracy of Rayleigh distribution statistical parameters, making the angle super-resolution processing results closer to the actual target distribution, and finally realizes the real-beam scanning radar angle super-resolution imaging.

Description of drawings

Fig. 1 is the flow chart of imaging method of the present invention;

Fig. 2 is a real beam scanning radar imaging mode diagram of a specific embodiment of the present invention;

Fig. 3 is the radar antenna pattern of the present embodiment;

FIG. 4 is a simulation scene diagram of the present embodiment;

FIG. 5 is a profile diagram of an echo in a clutter scene (SCR=25dB) in this embodiment;

FIG. 6 is a diagram of the final scanning radar imaging result after processing in this embodiment.

detailed description

The technical solution of the present invention will be further described below in conjunction with the accompanying drawings, but the content protected by the present invention is not limited to the following description.

As shown in Figure 1, a real-beam scanning radar angle super-resolution imaging method based on sparse constraints includes the following steps:

S1. Echo modeling, based on the geometric relationship between the real beam scanning radar and the target, the echo data model of the scanning radar is established; the specific implementation method is: the radar is at a height H below the angle of view Scan the -ψ～ψ imaging area in clockwise order; at the initial moment, the initial slant distance between the radar antenna and the target at the center of the scene is r ₀ , and the corresponding coordinates of each target point in the scene are ( _xi , y _i ), each target The azimuth angle between the target and the radar corresponds to θ _i , and the slant distance between each target and the radar is r _i ;

Let the transmitted signal be a chirp signal Among them, rect( ) represents a rectangular signal, which is defined as τ is the fast time variable in the distance direction, T is the duration of the transmitted pulse, c is the speed of light, λ is the wavelength, and K _r is the frequency modulation slope; in order to ensure that the theory is consistent with the actual verification, the received echo is discretely processed; the discretized echo The wave analytical expression is:

Among them, Ω is the range of the target scene, θ is the antenna beam width, f( _xi ,y _i ) is the scattering function of the target at point ( _xi ,y _i ); ω is the scanning speed of the antenna, T _β is the Dwell time for beamwidth. This embodiment adopts the forward-looking scanning radar imaging motion geometry mode, as shown in FIG. 2 , and the scanning radar imaging parameters are shown in Table 1.

Table 1 Scanning radar system parameters

parameter symbol value carrier frequency f _c 10GHz Transmit signal time width T 10μs Transmit signal bandwidth B 50MHz Pulse sampling frequency PRF 1500Hz Antenna scan speed ω 60°/s Antenna Beamwidth θ 2.5° scan range Φ -12°～12° Scanning radar range R ₀ 10km

S2. Perform range-wise pulse compression on the echo data to achieve high resolution in the range direction; the specific implementation method includes the following sub-steps:

S21. Constructing a range-wise pulse pressure reference signal:

in, Indicates the distance to the reference time;

S22. Combine s _ref with echo data Carry out the maximum autocorrelation operation, and the two-dimensional signal after pulse compression is obtained as follows:

Among them, B is the transmission signal bandwidth.

S3. Expressing the pulse-compressed echo data as a convolution model of the antenna beam and the scattering coefficient of the observed scene; the specific implementation method is: the convolution model of the echo signal is expressed as:

Among them, s=[s(1, 1), s(1, 2), ..., s(N, 1), ..., s(N, M)] ^T is a vector of NM × 1 dimension, which is to combine all pulses The measured value of the compressed echo signal is rearranged in the azimuth direction according to the order of the distance unit, and the superscript T represents the transpose operation;

f=[f(1,1), f(1,2),..., f(N, 1),..., f(N, M)] ^T is a vector of NM×1 dimension, which is all The magnitude of the unknown target is rearranged in the azimuth direction according to the order of range cells;

n=[n(1,1), n(1,2),…,n(N,1),…,n(N,M)] ^T , which is a NM×1-dimensional vector, representing clutter and interference Signal component, subject to statistically independent Rayleigh distribution;

H is a matrix of NM×NM dimensions, which is composed of a convolution measurement matrix H _M×N , where H _M×N =[h ₁ ,h ₂ ,...,h _M ].

S4. Establish the maximum a posteriori objective function according to the convolution model obtained in S3, and derive the maximum a posteriori solution; the specific implementation method is: based on the Bayesian formula, through the given noise statistical characteristics, combined with the sparse target distribution Prior information, the maximum a posteriori probability deconvolution super-resolution method is introduced to realize convolution inversion, which specifically includes the following sub-steps:

S41. For the formula (3), the posterior probability of the echo data is expressed as:

Among them, p( ) represents the probability density function; according to the maximum a posteriori criterion, the deconvolution problem is transformed into solving the optimal solution f so that it satisfies:

in, is the maximum a posteriori estimation of the objective function; p(f/s), p(s/f) and p(f) respectively represent the posterior probability, likelihood probability and prior probability of the target of the echo data;

S42. Assuming that the clutter or interference signal at each sampling point in the real beam scanning radar echo signal obeys a statistically independent Rayleigh distribution, then the likelihood probability is expressed as:

Among them, i is each discrete point target,

^σ2 is a statistical parameter in the Rayleigh distribution;

S43. Select the sparseness feature as the regularization constraint item, and the probability density of target scattering is:

Among them, 0<q≤1; when q=1, p(f)∝exp(-2||f|| ₁ ) is a Laplace distribution; when q→1, the target probability is

S44, obtain maximum a posteriori objective function according to (6) formula and (7) formula:

Taking the negative natural logarithm of formula (8), we get:

Find the gradient operation of formula (9) with respect to f, and get:

Among them, (·) ^T represents the transposition operation, P=diag{p ₁ ,...,p _NM }, p _i =|f _i | ^2-q ;

S45, because formula (10) is a non-linear function, therefore, can only obtain the result of approximating original scene by iterative method, make formula (10) be zero, obtain the maximum a posteriori solution about f as:

S5. Accurately restore the original target distribution through an adaptive iterative method, including the following sub-steps:

S51. Calculating the iterative initial value: using the TIKHONOV regularization method and the maximum likelihood estimation method to obtain the iterative initial value of the two parameters of the solution of the objective function and the Rayleigh distribution statistical parameter; its specific implementation includes the following steps:

S511. Using the TIKHONOV regularization method, calculate the rough estimation result of the original scene as:

f＝(H ^T H+βI) ^-1 H ^T s (12)

Among them, β is a regularization parameter, and I is an NM×NM dimensional unit diagonal matrix;

S512. Estimate the statistical parameters of the Rayleigh distribution using the maximum likelihood estimation method. First, for an NM-dimensional clutter vector g=[g ₁ ,..., _gi ] (i=1,...,NM ), after taking the logarithm of the joint likelihood function of this vector, we get:

Calculate the derivative of formula (13) with respect to σ, and let the result be zero, we get:

Therefore, the maximum likelihood estimate of ^σ2 is:

For real-beam scanning radar imaging, g _i =s _i -(Hf) _i , so the statistical parameters of the Rayleigh distribution are calculated by using TIKHONOV regularization calculation results combined with formula (15):

S52. Construct an iterative expression according to the maximum a posteriori solution obtained in S4:

Among them, the initial value of the iteration is (12) formula and the result of bringing (12) formula into (16) formula, k+1 and k are the number of iterations, P _k =diag{(p ₁ ) _k ,...,( p _NM ) _k }, (p _i ) _k =|(f _i ) _k | ^2−q .

S53. Substituting the iteration initial value into the iteration expression to obtain a new maximum a posteriori solution;

S54. Bring the maximum a posteriori solution obtained in S53 into the statistical parameter calculation formula of the Rayleigh distribution, and update the statistical parameter value of the Rayleigh distribution;

S55. Substituting the maximum a posteriori solution obtained in S53 and the Rayleigh distribution statistical parameter value obtained in S54 into the iterative expression to obtain a new maximum a posteriori solution;

S56, repeating steps S54 and S55, until the result of the iterative expression is compared with the result of the previous iterative expression, and when the iterative convergence condition is satisfied, the result of the iterative expression is recorded as the real beam scanning radar angle super-resolution imaging result, so The stated convergence condition is:

||f _k+1 -f _k || ² <ε (18)

Wherein, f _k+1 and f _k are the results of two adjacent iterations, and ε is a preset threshold.

This embodiment adopts the radar antenna pattern shown in Figure 3, Figure 4 is the simulation scene diagram of this embodiment, Figure 5 is the echo profile (SCR=25dB) in the clutter scene of this embodiment, and Figure 6 is The final image of the scanning radar imaging result after processing in this embodiment. It can be seen from the figure that through the method provided by the present invention, the angle information of the target is well restored under the background of Rayleigh clutter.

Those skilled in the art will appreciate that the embodiments described here are to help readers understand the principles of the present invention, and it should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical revelations disclosed in the present invention without departing from the essence of the present invention, and these modifications and combinations are still within the protection scope of the present invention.

Claims (4)

1. a real beam scanning radar angle super-resolution imaging method based on sparse constraints, is characterized in that, comprises the following steps: S1. Echo modeling. Based on the geometric relationship between the real beam scanning radar and the target, the echo data model of the scanning radar is established; the specific implementation method is: the radar is at a height H below the angle of view Scan the -ψ～ψ imaging area in clockwise order; at the initial moment, the initial slant distance between the radar antenna and the target at the center of the scene is r ₀ , and the corresponding coordinates of each target point in the scene are ( _xi , y _i ), each target The azimuth angle between the target and the radar corresponds to θ _i , and the slant distance between each target and the radar is r _i ; Let the transmitted signal be a chirp signal Among them, rect( ) represents a rectangular signal, which is defined as τ is the fast time variable in the distance direction, T is the duration of the transmitted pulse, c is the speed of light, λ is the wavelength, and K _r is the frequency modulation slope; in order to ensure that the theory is consistent with the actual verification, the received echo is discretely processed; the discretized echo The wave analytical expression is: $\begin{array}{c}\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&theta;</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&tau;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; \&Omega;</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; \&theta;</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; \&beta;</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&tau;</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; )</span>\\ <span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j</span>}\frac{\mathrm{<span\; class="notranslate">\; 4</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; j\&pi;K</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}}{<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; \&tau;</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; \&rsqb;</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>\end{array}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$ Among them, Ω is the range of the target scene, θ is the antenna beam width, f( _xi ,y _i ) is the scattering function of the target at point ( _xi ,y _i ); ω is the scanning speed of the antenna, T _β is the Dwell time for beamwidth; S2. Perform range-wise pulse compression on the echo data to achieve high resolution in the range direction; the specific implementation method includes the following sub-steps: S21. Constructing a range-wise pulse pressure reference signal: ${\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; f</span>}}}{\mathrm{<span\; class="notranslate">\; T</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&CenterDot;</span>\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; j\&pi;K</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}}{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; f</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \}</span>$ Among them, τ _ref represents the range reference time; S22. Perform maximum autocorrelation calculation on s _ref and echo data s(θ, τ), and obtain the two-dimensional signal after pulse compression as follows: ${\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&theta;</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&tau;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; \&Omega;</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; \&theta;</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; \&beta;</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; sin</span>}\mathrm{<span\; class="notranslate">\; c</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&tau;</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; \&CenterDot;</span>\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j</span>}\frac{\mathrm{<span\; class="notranslate">\; 4</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$ Among them, B is the transmission signal bandwidth; S3. Expressing the pulse-compressed echo data as a convolution model of the antenna beam and the scattering coefficient of the observed scene; the specific implementation method is: the convolution model of the echo signal is expressed as: Among them, s=[s (1, 1), s (1, 2), ..., s (N, 1), ..., s (N, M)] ^T is a vector of NM × 1 dimension, is to combine all pulses The measured value of the compressed echo signal is rearranged in the azimuth direction according to the order of the distance unit, and the superscript T represents the transpose operation; f=[f(1,1), f(1,2), ..., f(N, 1), ..., f(N, M)] ^T is a vector of NM×1 dimension, which is all the The magnitude of the unknown target is rearranged in the azimuth direction according to the order of range cells; n=[n(1,1), n(1,2),...,n(N,1),...,n(N,M)] ^T , which is a NM×1-dimensional vector, representing clutter and interference Signal component, subject to statistically independent Rayleigh distribution; H is a matrix of NM×NM dimensions, which is composed of a convolution measurement matrix H _M×N , where H _M×N =[h ₁ , h ₂ ,...,h _M ]; S4. Establish the maximum a posteriori objective function according to the convolution model obtained in S3, and derive the maximum a posteriori solution; the specific implementation method is: based on the Bayesian formula, through the given noise statistical characteristics, combined with the sparse target distribution. Based on the empirical information, the maximum a posteriori probability deconvolution super-resolution method is introduced to realize the convolution inversion, which specifically includes the following sub-steps: S41. For the formula (3), the posterior probability of the echo data is expressed as: $\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$ Among them, p( ) represents the probability density function; according to the maximum a posteriori criterion, the deconvolution problem is transformed into solving the optimal solution f so that it satisfies: $\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; arg</span>}\underset{\mathrm{<span\; class="notranslate">\; f</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; x</span>}}\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; arg</span>}\underset{\mathrm{<span\; class="notranslate">\; f</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$ in, is the maximum a posteriori estimate of the objective function; p(f/s), p(s/f) and p(f) respectively represent the posterior probability, likelihood probability and prior probability of the target of the echo data; S42. Assuming that the clutter or interference signal at each sampling point in the real beam scanning radar echo signal obeys a statistically independent Rayleigh distribution, then the likelihood probability is expressed as: $\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Pi;</span>}}\frac{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; )</span>}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\frac{{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; )</span>}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$ Among them, i is each discrete point target, ${<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; )</span>}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}$ ^σ2 is a statistical parameter in the Rayleigh distribution; S43. Select the sparseness feature as the regularization constraint item, and the probability density of target scattering is: $\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Proportional;</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Pi;</span>}}\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; \&lsqb;</span><span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; q</span>}}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{<span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; q</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$ Among them, 0<q≤1; when q=1, p(f)∝exp(-2||f|| ₁ ) is a Laplace distribution; when q→1, the target probability is S44, obtain maximum a posteriori objective function according to (6) formula and (7) formula: $\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Pi;</span>}}\frac{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; )</span>}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\frac{{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; )</span>}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \&CenterDot;</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Pi;</span>}}\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; \&lsqb;</span><span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; q</span>}}{<span\; class="notranslate">\; (</span><span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; q</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 8</span>}<span\; class="notranslate">\; )</span>$ Taking the negative natural logarithm of formula (8), we get: Find the gradient operation of formula (9) with respect to f, and get: Among them, (·) ^T represents the transposition operation, P=diag{p ₁ ,...,p _NM }, p _i =|f _i | ^2-q ; S45, because formula (10) is a non-linear function, therefore, can only obtain the result of approximating original scene by iterative method, make formula (10) be zero, obtain the maximum a posteriori solution about f as: $\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; h</span>}}^{\mathrm{<span\; class="notranslate">\; T</span>}}\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; P</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; h</span>}}^{\mathrm{<span\; class="notranslate">\; T</span>}}\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; h</span>}}^{\mathrm{<span\; class="notranslate">\; T</span>}}\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 11</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ;</span>$ S5. Accurately restore the original target distribution through an adaptive iterative method, including the following sub-steps: S51. Calculating the iterative initial value: using the TIKHONOV regularization method and the maximum likelihood estimation method to obtain the iterative initial value of the two parameters of the solution of the objective function and the Rayleigh distribution statistical parameter; S52. Construct an iterative expression according to the maximum a posteriori solution obtained in S4; S53. Substituting the iteration initial value into the iteration expression to obtain a new maximum a posteriori solution; S54. Bring the maximum a posteriori solution obtained in S53 into the calculation formula of the statistical parameter of the Rayleigh distribution, and update the value of the statistical parameter of the Rayleigh distribution; S55. Substituting the maximum a posteriori solution obtained in S53 and the Rayleigh distribution statistical parameter value obtained in S54 into the iterative expression to obtain a new maximum a posteriori solution; S56. Steps S54 and S55 are repeated until the result of the iterative expression is compared with the result of the previous iterative expression and satisfies the iteration convergence condition, and the result of the iterative expression is recorded as the real beam scanning radar angle super-resolution imaging result. 2. real beam scanning radar angle super-resolution imaging method according to claim 1, is characterized in that, the concrete realization of described step S51 comprises the following steps: S511. Using the TIKHONOV regularization method, calculate the rough estimation result of the original scene as: f＝(H ^T H+βI) ^-1 H ^T s (12) Among them, β is a regularization parameter, and I is an NM×NM dimensional unit diagonal matrix; S512. Utilize the maximum likelihood estimation method to estimate the statistical parameters of the Rayleigh distribution. First, for an NM-dimensional clutter vector g=[g ₁ ,..., _gi ](i=1,...,NM ), after taking the logarithm of the joint likelihood function of this vector, we get: $\mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&sigma;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; N</span>}\mathrm{<span\; class="notranslate">\; m</span>}{\mathrm{<span\; class="notranslate">\; ln\&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\frac{{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 13</span>}<span\; class="notranslate">\; )</span>$ Calculate the derivative of formula (13) with respect to σ, and let the result be zero, we get: $\frac{<span\; class="notranslate">\; \&part;</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&sigma;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; \&part;</span>\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; N</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 3</span>}}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 14</span>}<span\; class="notranslate">\; )</span>$ Therefore, the maximum likelihood estimate of ^σ2 is: ${\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; N</span>}\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 15</span>}<span\; class="notranslate">\; )</span>$ For real-beam scanning radar imaging, g _i =s _i -(Hf) _i , so the statistical parameters of the Rayleigh distribution are calculated by using TIKHONOV regularization calculation results combined with formula (15): ${\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; )</span>}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; N</span>}\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 16</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; .</span>$ 3. real beam scanning radar angle super-resolution imaging method according to claim 2, is characterized in that, the iterative expression that described step S52 builds is: ${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; h</span>}}^{\mathrm{<span\; class="notranslate">\; T</span>}}\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; +</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>}_{\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span>}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; h</span>}}^{\mathrm{<span\; class="notranslate">\; T</span>}}\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>}_{\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; h</span>}}^{\mathrm{<span\; class="notranslate">\; T</span>}}\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Hf</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 17</span>}<span\; class="notranslate">\; )</span>$ Among them, the initial value of v iteration is (12) formula and the result of bringing (12) formula into (16) formula, k+1 and k are the number of iterations, P _k =diag{(p ₁ ) _k ,...,( p _NM ) _k }, (pi)k=|(f _i )k| ^2q . 4. real beam scanning radar angle super-resolution imaging method according to claim 3, is characterized in that, the convergence condition in the described step S56 is: ||f _k+1 -f _k || ² <ε (18) Wherein, f _k+1 and f _k are the results of two adjacent iterations, and ε is a preset threshold.