A radial velocity estimation method for ship targets in azimuth multi-channel scanning mode

Abstract

A radial velocity estimation method for a ship target in an azimuth multi-channel scanning mode comprises the following steps: S1: acquiring multi-channel echo data and extracting the ship target echo signal; S2: performing an inverse ChirpZ transform and a de-skewing operation on a first main signal and a first fuzzy signal to obtain second main signal echo data and second fuzzy signal echo data; S3: performing an inverse correction on the second main signal echo data to obtain third main signal echo data; S4: performing corrective correction on the second fuzzy signal to obtain third fuzzy signal echo data; S5: constructing an ideal radial velocity subspace; S6: calculating a cost function with respect to the ideal signal subspace based on the third main signal echo data and the third fuzzy signal echo data, and obtaining the radial velocity value by solving the optimal solution; the present invention matches the radial velocity by an iterative optimization method in the constructed radial velocity ideal signal subspace, which can effectively overcome the problem of discontinuity of the azimuth signal in the scanning mode.

Description

Technical Field

[0001] The present invention relates to the field of signal processing technology, and more particularly to a method for estimating the radial velocity of a ship target in an azimuth multi-channel scanning mode. Background Art

[0002] Synthetic Aperture Radar (SAR) is a crucial Earth observation tool, capable of providing all-day, all-weather Earth observation images. With increasing requirements for SAR resolution and swath width, high-resolution and wide-swath (HRWS) imaging is a key research direction for spaceborne SAR. The combination of azimuth multi-channel high-resolution and wide-swath (MC-HRWS) SAR systems and digital beamforming (DBF) technology can overcome the minimum antenna area limitations of SAR systems while achieving both high-resolution and wide-swath imaging.

[0003] The MC-HRWS SAR system divides the azimuth antenna into multiple receiving sub-apertures, utilizing additional spatial sampling to compensate for insufficient temporal sampling. The system transmits chirp signals at a low pulse repetition frequency (PRF) to avoid severe range ambiguity, and all antenna equivalent channels receive echoes simultaneously. Because the system's PRF cannot be arbitrarily set, the multi-channel combined signal acquired using multi-channel technology is often non-uniform. Imaging using non-uniform signals results in significant ambiguity in azimuth. To mitigate azimuth ambiguity, reconstruction algorithms are used in the data processing of the MC-HRWS SAR system to reconstruct the spectrum of the non-uniform signal and obtain Doppler-ambiguity-free echo signals. However, conventional reconstruction filters are designed for signals in static scenes. When non-cooperative moving targets (such as surface ships) are present in the scene, their radial velocity not only introduces additional Doppler shifts, but also causes Doppler spectrum aliasing due to mismatch in the reconstruction filter. This results in positional offset and ambiguity of the imaged ship, severely impacting the recognition and interpretation of moving ships.

[0004] In order to improve the imaging quality of moving ships by MC-HRWS SAR system, it is very important to estimate the radial velocity of moving ships.

[0005] Currently, for the MC-HRWS SAR system's strip-mode SLC product, radial velocity estimation of ship targets can be achieved by reconstructing their full-aperture Doppler spectrum. However, in strip-mode, the SAR system's beam is fixed, limiting its observation scope and making it unsuitable for observing vast ocean areas. In contrast, scanning mode, which provides wide-range swaths, is more suitable for ocean observations. Because scanning mode produces discontinuous echoes in azimuth, estimating radial velocity by reconstructing the ship target's Doppler spectrum becomes ineffective.

[0006] Therefore, how to achieve radial velocity estimation in a multi-channel scanning mode and overcome the problem of discontinuous azimuth signals is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0007] In view of this, the present invention provides a radial velocity estimation method for ship targets in azimuth multi-channel scanning mode, which matches the radial velocity by iterative optimization in the construction of an ideal radial velocity signal subspace, and can effectively overcome the problem of discontinuous azimuth signals in the scanning mode.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] A method for estimating the radial velocity of a ship target in an azimuth multi-channel scanning mode comprises the following steps:

[0010] S1: Acquire SAR multi-channel echo data and extract ship target echo signals, where the ship target echo signals include a first main signal and a first fuzzy signal.

[0011] S2: Perform an inverse ChirpZ transform and a de-skewing operation on the first main signal and the first fuzzy signal to obtain second main signal echo data and second fuzzy signal echo data.

[0012] S3: Perform migration correction and restoration on the second fuzzy signal echo data based on the target reference slant range of the fuzzy target to obtain a third fuzzy signal.

[0013] S4: Performing migration correction on the third ambiguity signal based on the target reference slant range of the main target to obtain fourth ambiguity signal echo data.

[0014] S5: Use different main signals and fuzzy signals to represent the ideal radial velocity and construct the ideal radial velocity subspace.

[0015] S6: Calculate the cost function of the ideal signal subspace based on the second main signal echo data and the fourth fuzzy signal echo data, and obtain the radial velocity value of the ship target by solving the optimal one.

[0016] Preferably, the step S1 further includes pre-processing the extracted main signal and fuzzy signal to shield sea clutter.

[0017] Preferably, the pretreatment step comprises:

[0018] S11: Calculate the absolute value of the echo signal data of the ship target to obtain the amplitude of the echo signal data of the ship target.

[0019] S12: Calculate a histogram of the ship target echo signal data amplitude to obtain the amplitude distribution of the ship target echo signal data.

[0020] S13: Take the peak value of the histogram and multiply it by a preset scale factor to obtain the threshold of the mask, retain the amplitude values of the ship target echo signal data that are greater than the threshold, and set the remaining signals to zero.

[0021] S14: performing morphological closing operation, morphological opening operation and morphological closing operation on the ship target echo signal data greater than the threshold value to obtain a mask matrix.

[0022] S15: Multiplying the mask matrix and the ship target echo signal data to obtain the preprocessed ship target echo signal.

[0023] Preferably, in said S2, the inverse ChirpZ transform comprises the following steps:

[0024] S21: Construct a transition signal according to the transformation ratio.

[0025] S22: Perform zero padding expansion on the transition signal to obtain an expanded signal.

[0026] S23: Convolutionally multiplying the transition signal and the expanded signal and combining them with a transformation ratio to obtain transformed ship target echo signal data.

[0027] Preferably, in S2, the reverse de-skewing operation is specifically as follows:

[0028] s n (t) = s n (t)exp[-jπK a t 2 ]

[0029] In the formula, K a is the azimuth frequency modulation, and t is the azimuth time.

[0030] Preferably, S3 specifically includes: constructing a second filter, and using the second filter to perform migration correction and restoration; the second filter includes an inverse phase correction matching filter, an inverse consistent distance migration correction matching filter and an inverse complementary distance migration correction matching filter, which are respectively used to perform phase correction and restoration, consistent distance correction and restoration and complementary distance correction and restoration.

[0031] Preferably, the inverse phase correction matched filter, the inverse consistent range migration correction matched filter and the inverse complementary range migration correction matched filter are respectively:

[0032] The inverse phase correction matched filter H1 is:

[0033]

[0034] Among them, K m is the range modulation frequency in the range Doppler domain, c is the speed of light, R ref is the fuzzy target reference slant range, f η is the azimuth frequency axis, is the target reference speed, D is the migration factor;

[0035] The reverse consistent distance migration correction matched filter H2 is:

[0036]

[0037] Among them, f τ is the distance-frequency axis;

[0038] The reverse complementary distance migration correction matched filter H3 is

[0039]

[0040] Where τ is the distance time.

[0041] Preferably, the migration factor D is specifically:

[0042]

[0043] Where v is the radar carrier speed and f0 is the radar carrier frequency.

[0044] Preferably, in S6, the cost function of the ideal signal subspace is calculated based on the second main signal echo data and the fourth fuzzy signal echo data, and the steps include:

[0045] Calculating a correlation coefficient based on the second main signal echo data and the fourth fuzzy signal echo data; selecting a signal pair in the ideal radial velocity subspace and calculating a proportional coefficient; and calculating a cost function based on the difference between the correlation coefficient and the proportional coefficient.

[0046] It can be seen from the above technical solution that compared with the prior art, the present invention discloses a radial velocity estimation method for ship targets in an azimuth multi-channel scanning mode, by constructing an ideal radial velocity signal subspace, and constructing a cost function based on the correlation coefficient between the actual ship target fuzzy signal and the main signal and the fuzzy signal and the main signal in the ideal radial velocity signal subspace, and performing iterative search and optimization, and finally obtaining the radial velocity value that best matches the ship target, which can effectively overcome the problem of discontinuity of the azimuth signal in the scanning mode. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0048] Figure 1 A schematic diagram of a radial velocity estimation method for a ship target in an azimuth multi-channel scanning mode provided by an embodiment of the present invention.

[0049] Figure 2 Schematic diagram of the main signal and analog-to-digital signal in an embodiment of the present invention.

[0050] Figure 3(a) is a schematic diagram of the fuzzy signal before range migration correction.

[0051] Figure 3(b) is a schematic diagram of the fuzzy signal after range migration correction

[0052] Figure 4 Diagram of the imaging geometric model in multi-channel mode.

[0053] Figure 5 Schematic diagram of the movement of moving ships and SAR aircraft within the synthetic aperture time. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0055] The present invention discloses a method for estimating the radial velocity of a ship target in an azimuth multi-channel scanning mode. In this mode, an antenna Tx is provided with N channels, namely Rx0 to RxN-1. One of the channels transmits a chirp signal at a low pulse repetition frequency, and all channels receive echoes to form an image. The method estimates the radial velocity of the ship target based on the echoes received by the multiple channels.

[0056] like Figure 1-Figure 5 , the method comprises the following steps:

[0057] S1: Acquire SAR multi-channel echo data and extract the ship target echo signal, wherein the ship target echo signal includes a first main signal and a first fuzzy signal. Figure 2 As shown in the figure, due to the multi-channel reconstruction error caused by the radial velocity of the ship target, there will be fuzzy signals on both sides of the main signal in the azimuth direction, which is equal to the number of channels. The energy intensity of the main signal is greater than that of the fuzzy signal and the focusing effect is better than that of the fuzzy signal.

[0058] S2: Performing an inverse ChirpZ transform and a de-skewing operation on the first main signal and the first fuzzy signal through a first filter to obtain second main signal echo data and second fuzzy signal echo data.

[0059] S3: offsetting the range migration of the erroneous slant range of the second ambiguity signal through a second filter to obtain echo data of a third ambiguity signal.

[0060] S4: The third ambiguity signal is corrected for range migration using a third filter to obtain the fourth ambiguity signal echo data. As shown in Figures 3(a) and 3(b), before range migration correction, the ambiguity signal is multiplied by an incorrect matched filter, affecting both position and energy, thus affecting the correlation between the ambiguity signal and the main signal. After correct range migration correction, both position and energy are corrected, achieving optimal correlation between the ambiguity signal and the main signal.

[0061] S5: Use different main signals and fuzzy signals to represent the ideal radial velocity and construct the ideal radial velocity subspace.

[0062] S6: Generate a cost function based on the difference between the correlation coefficient between the second main signal echo data and the fourth fuzzy signal echo data and the ratio of the fuzzy signal to the main signal in the ideal radial velocity signal subspace, and iteratively solve the radial velocity value that minimizes the cost function through the particle swarm optimization algorithm.

[0063] The fuzzy signal is generated due to multi-channel reconstruction errors caused by the ship's radial velocity. It is essentially a ship signal component, which should be multiplied by the same matched filter as the main signal during SAR imaging. However, the SAR imaging algorithm treats it as an independent ship target, rather than a fuzzy signal. Therefore, the fuzzy signal is multiplied by the incorrect matched filter during imaging, resulting in poorer focusing than the main signal and significantly reduced correlation with the main signal. To improve the accuracy of radial velocity estimation, the present invention corrects the fuzzy signal's incorrect matched filter, ensuring optimal correlation between the fuzzy signal and the main signal.

[0064] In order to further implement the above technical solution, S1 also includes pre-processing the extracted main signal and fuzzy signal to shield the sea clutter. Specifically, the pre-processing steps include:

[0065] S11: Calculate the absolute value of the echo signal data of the ship target to obtain the amplitude of the echo signal data of the ship target.

[0066] S12: Calculate a histogram of the ship target echo signal data amplitude to obtain the amplitude distribution of the ship target echo signal data.

[0067] S13: Take the peak value of the histogram and multiply it by a preset scale factor to obtain the threshold of the mask, retain the amplitude values of the ship target echo signal data that are greater than the threshold, and set the remaining signals to zero.

[0068] S14: Performing morphological closing, morphological opening, and morphological closing operations on the ship target echo signal data greater than the threshold in sequence to obtain a mask matrix. The morphological closing operation is defined as dilation followed by erosion, and the morphological opening operation is defined as erosion followed by dilation.

[0069] The erosion operation is defined as:

[0070]

[0071] Where A is the original image, B is the corrosion operation structure, and x and y are defined as the coordinates of the origin of B in A.

[0072] The expansion operation is defined as:

[0073]

[0074] S15: Multiplying the mask matrix and the ship target echo signal data to obtain the preprocessed ship target echo signal.

[0075] In order to further implement the above technical solution, the first filter in S2 is divided into a ChirpZ inverse transform module and an inverse de-skewing module, which are respectively used to perform inverse ChirpZ transform and inverse de-skewing operations to achieve signal restoration and obtain the second main signal echo data and the second fuzzy signal echo data.

[0076] Specifically, first perform an inverse ChirpZ transform on the ship target echo signal. The steps are as follows:

[0077] S21: Construct a transition signal X(n) according to the transformation ratio.

[0078]

[0079] Among them, S n The ship target echo signal data is obtained from the XML file provided by the measured data. The following parameters can be obtained: R0 is the closest slant distance, R is the instantaneous slant distance of the target, and N a is the number of signal pulses, and n is the pulse sequence number.

[0080] S22: Fill the transition signal with zeros to obtain an expanded signal. In order to avoid circular convolution, fill the transition signal X(n) with N. a Zeros, expanded to 2N a points, and obtain the expanded signal P(m).

[0081] S23: Convolve and multiply the transition signal and the expanded signal, and then combine them with the transformation ratio to obtain the transformed ship target echo signal data S' n .

[0082]

[0083] Then, the reverse de-skew operation is performed based on the data after the inverse ChirpZ transformation. The steps are as follows:

[0084] S24: S″ n (t) = S' n (t)exp[-jπK a t 2 ]

[0085] Among them, S n (t) represents the ship target echo signal output by the first filter, which is composed of the second main signal echo data and the second fuzzy signal echo data; K a is the azimuth frequency modulation slope, which is a constant in the proposed method, and t is the azimuth time, representing the azimuth time axis.

[0086] In this embodiment, the present invention constructs an inverse ChirpZ transform and de-skew operation matched filter based on the masked ship target main signal and its blurred signal echo signal data, and multiplies the matched filter with the ship target echo signal data to obtain the ship target echo signal data with ChirpZ transform and de-skew operation in the restored scanning mode Specan algorithm.

[0087] To further implement the above technical solution, a second filter is used to multiply the Doppler spectrum data of the ship target's main signal echo, which has undergone azimuth Fourier transform, to perform inverse correction and restoration on the second main signal output by S2. In S3, the second filter includes an inverse phase correction matched filter, an inverse consistent range migration correction matched filter, and an inverse complementary range migration correction matched filter.

[0088] Specifically, the inverse phase correction matched filter H1 is:

[0089]

[0090] Among them, K m is the range modulation frequency in the range Doppler domain, c is the speed of light, R ref is the fuzzy signal target reference slant distance, f η is the azimuth frequency axis, is the reference speed of the radar carrier, and D is the migration factor;

[0091] The reverse consistent range migration correction matched filter H2 is:

[0092]

[0093] Among them, f τ is the range-frequency axis, representing the change of range frequency along the range direction;

[0094] The reverse complementary distance migration correction matched filter H3 is:

[0095]

[0096] Among them, τ is the distance time axis, which represents the change of distance time with the difference of distance position.

[0097] In S4, the third filter includes a phase correction matched filter for correct slant range, a consistent range migration correction matched filter, and a complementary range migration correction matched filter.

[0098] Specifically, the phase correction matched filter for the correct slant range, the consistent range migration correction matched filter, and the complementary range migration correction matched filter are:

[0099]

[0100] Among them, R main Reference slant range to the main signal target.

[0101] In order to further implement the above technical solution, the construction process of the ideal radial velocity subspace in S5 is as follows:

[0102] The impulse response function (IRF) of the nth channel in the MC-HRWS SAR system can be expressed as:

[0103]

[0104] Where t represents the azimuth time, v r represents the radial velocity component of the ship target; σ represents the backscatter coefficient of the ship target, w a (·) represents the antenna pattern, λ represents the wavelength, R n (t) represents the function of slant range changing with azimuth time t; R0 represents the minimum slant range, v represents the speed of the radar carrier, v r represents the radial velocity component of the ship target, v a Represents the azimuth velocity component of the ship target, x n Indicates the Equivalent Phase Center (EPC) position of the nth channel at time t=0.

[0105] x n =n·d / 2,n=0,1,…,N-1

[0106] Where d is the actual distance between the subapertures. The first subaperture is considered to be the transmitting antenna (Tx) that transmits the signal, and all subapertures (Rx) receive the echo signal reflected from the target. In addition, it is assumed that the initial coordinates of the antenna phase center of the first channel are (0,0). The X axis points to the direction of the SAR carrier speed, the Y axis is perpendicular to the orbital plane, the negative half of the Z axis points to the earth, θ is the downward viewing angle of the radar, and W g is the range width. In the MC-HRWS SAR system, by compensating the constant phase relative to the reference channel, the received echo of each channel can be regarded as generated in the EPC. The azimuth distance between the two EPCs is d / 2. Since the radial velocity of the moving target will introduce a r The phase term is related to both radial velocity and azimuth time. By compensating for the constant phase term, the IRF of the nth channel can be expressed as an azimuth time shift relative to the reference channel:

[0107]

[0108] Among them, sr (·) represents the IRF of the reference channel. The range Doppler received signal of the nth channel is expressed as follows:

[0109]

[0110] Among them, f a represents the Doppler frequency, S r (f a , v r ) represents the unambiguous Doppler spectrum of the reference channel including the Doppler frequency shift.

[0111] The Doppler shift caused by radial velocity can be expressed as follows:

[0112]

[0113] Therefore, the radial velocity information of the ship target is included in the Doppler frequency shift of the ship target signal. However, the azimuth sampling of the scanning mode is not continuous, and it is impossible to obtain the full-aperture Doppler spectrum of the ship target like the strip mode. However, in the multi-channel data, the radial velocity of the ship target will not only cause the Doppler frequency shift of the ship target, but also cause the ship target to produce a blurred target in the azimuth direction. The signal of the blurred target also contains the radial velocity of the ship target. Therefore, the ideal signal subspace of radial velocity is constructed, and the subspace that best matches the actual ship target echo signal is searched to obtain the radial velocity of the ship target. The ideal signal subspace of radial velocity is defined as X:

[0114] X=P(f a )Γ(v r )H(f a )

[0115] Each item P(f a )Γ(v r )H(f a ) are defined as follows:

[0116]

[0117] H(f a )=[h0(f a ),h1(f a ),…,h N-1 ( f a)]

[0118] h k (f a )=[h k,0 (f a ),h k,1 (f a ),…,h k,N-1(f a )] T

[0119]

[0120] P(f a )=H -1 (f a )

[0121] Among them, P(f a ) is the reconstruction filter, Γ(v r ) is the phase term related to the radial velocity, H(f a ) is the inverse of the reconstruction filter, k is the fuzzy number, and N represents the number of azimuth channels. When k is 0, it corresponds to the main signal, while non-zero values correspond to fuzzy signals of the corresponding order. Therefore, the ship's return signal data, after correcting for range migration, can be matched with the radial velocity signal subspace. By searching for the signal subspace with the most similar characteristics, a highly accurate radial velocity of the ship target can be obtained.

[0122] To further implement the above technical solution, in S6, the cost function of the ideal signal subspace is calculated based on the second main signal echo data and the fourth fuzzy signal echo data, and the steps include:

[0123] S61: Calculating a correlation coefficient based on the second main signal echo data and the fourth fuzzy signal echo data;

[0124] The calculation formula for the correlation coefficient between the nth fuzzy signal and the main signal of the ship target echo signal data is:

[0125]

[0126] Among them, SA n is the nth fuzzy signal, SM is the main signal, is the complex conjugate of the main signal.

[0127] S62: In the radial velocity signal subspace, the ratio of the fuzzy signal to the main signal is defined as follows:

[0128] Xt=X(AC,MC) / X(MC,MC)

[0129] Among them, AC is the fuzzy signal number, and MC is the main signal number.

[0130] S63: The cost function constructed by the ratio of the ideal fuzzy signal to the main signal generated by the correlation coefficient of the fuzzy signal and the main signal of the ship target echo signal data and the radial velocity is defined as follows:

[0131]

[0132] Furthermore, in order to perform iterative optimization, the method further includes S64: iteratively solving the velocity value that minimizes the cost function according to the particle swarm optimization algorithm to obtain the radial velocity value that best matches the moving ship target.

[0133] Particle swarm optimization algorithm is a heuristic search algorithm based on group search. The algorithm needs to continuously update the position and velocity of each particle in each iteration. Its update equation is as follows:

[0134]

[0135] z i =z i +b i

[0136] Where i = 1, 2, ..., n1, n1 is the total number of particles. i is the velocity of the particle. i is the current position of the particle. c1 and c2 are learning factors, usually set to c1 = c2 = 2.0. r1 and r2 are random numbers between (0, 1). i The maximum value is b max If b i Greater than b max , then b i =b max . represents the optimal solution found by the particle, z gbest Represents the optimal solution found by the entire group. The first part of the particle update equation represents the influence of the previous velocity, the second part represents the influence of the particle from its own search results, and the third part reflects the optimal information sharing between particles. The next velocity of the particle is finally determined by the particle's own information and the information of the optimal particle. φ1 is the inertia weight coefficient, which is used to adjust the influence of the previous particle velocity on the optimization process. A larger φ1 has good global search capabilities, while a smaller φ1 has good local search capabilities. A dynamic φ1 can obtain better search results. The linear decreasing function of φ1 is designed as follows:

[0137]

[0138] Among them, e is the iteration index, e max is the maximum value of the iteration index. Based on the cost function min(C), the radial velocity signal subspace that best matches the ship target echo signal data is searched to obtain the radial velocity value that best matches the ship target.

[0139] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0140] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for estimating the radial velocity of a ship target in an azimuth multi-channel scanning mode, characterized in that: The following steps are involved: S1: Acquire SAR multi-channel echo data and extract ship target echo signals, where the ship target echo signals include a first main signal and a first fuzzy signal; S2: performing an inverse ChirpZ transform and an inverse de-skewing operation on the first main signal and the first fuzzy signal to obtain second main signal echo data and second fuzzy signal echo data; S3: performing migration correction and restoration on the second fuzzy signal echo data based on the target reference slant range of the fuzzy target to obtain a third fuzzy signal; S4: performing migration correction on the third fuzzy signal based on the target reference slant range of the main target to obtain fourth fuzzy signal echo data; S5: Use different main signals and fuzzy signals to represent the ideal radial velocity and construct the ideal radial velocity subspace; S6: Calculate the difference between the correlation coefficient and the ratio of the fuzzy signal to the main signal in the ideal radial velocity signal subspace based on the second main signal echo data and the fourth fuzzy signal echo data, generate a cost function, and obtain the radial velocity value of the ship target by solving the optimal one.

2. The radial velocity estimation method of a ship target in an azimuth multi-channel scanning mode according to claim 1 is characterized in that: Said S1 also includes pre-processing the extracted main signal and fuzzy signal to shield sea clutter.

3. The radial velocity estimation method of a ship target in an azimuth multi-channel scanning mode according to claim 2 is characterized in that: The pre-processing steps include: S11: Calculate the absolute value of the echo signal data of the ship target to obtain the amplitude of the echo signal data of the ship target; S12: Calculating a histogram of the ship target echo signal data amplitude to obtain the amplitude distribution of the ship target echo signal data; S13: taking the peak value of the histogram and multiplying it by a preset scale factor to obtain the threshold value of the mask, retaining the amplitude values of the ship target echo signal data that are greater than the threshold value, and setting the remaining signals to zero; S14: performing morphological closing operation, morphological opening operation and morphological closing operation on the ship target echo signal data greater than the threshold in sequence to obtain a mask matrix; S15: Multiplying the mask matrix and the ship target echo signal data to obtain the preprocessed ship target echo signal.

4. The method for estimating radial velocity of a ship target in an azimuth multi-channel scanning mode according to claim 1, characterized in that: The step S2 specifically includes: constructing a first filter to perform an inverse ChirpZ transform, the steps including: S21: constructing a transition signal according to the transformation ratio; S22: performing zero padding expansion on the transition signal to obtain an expanded signal; S23: Convolutionally multiplying the transition signal and the expanded signal and combining them with a transformation ratio to obtain transformed ship target echo signal data.

5. The method for estimating radial velocity of a ship target in an azimuth multi-channel scanning mode according to claim 4, characterized in that: In S2, the first filter is also used to perform a reverse de-skewing operation, specifically: S″ n (t)=S' n (t)exp[-jπK a t 2 ] Among them, S' n (t) is the ship target echo signal data after ChirpZ inverse transformation, S″ n (t) represents the ship target echo signal output by the first filter, which is composed of the second main signal echo data and the second fuzzy signal echo data; K a is the azimuth frequency modulation slope, t is the azimuth time, and represents the azimuth time axis.

6. The radial velocity estimation method of a ship target in an azimuth multi-channel scanning mode according to claim 1, characterized in that: The S3 specifically includes: constructing a second filter and using the second filter to perform migration correction and restoration; the second filter includes an inverse phase correction matched filter, an inverse consistent distance migration correction matched filter and an inverse complementary distance migration correction matched filter, which are respectively used to perform phase correction and restoration, consistent distance correction and restoration and complementary distance correction and restoration.

7. The method for estimating radial velocity of a ship target in an azimuth multi-channel scanning mode according to claim 6, characterized in that: The inverse phase correction matched filter, the inverse consistent range migration correction matched filter and the inverse complementary range migration correction matched filter are respectively: The inverse phase correction matched filter H1 is: Among them, K m is the range modulation frequency in the range Doppler domain, c is the speed of light, R ref is the fuzzy target reference slant range, f η is the azimuth frequency axis, is the reference speed of the radar carrier, D is the migration factor, and R0 represents the minimum slant range; The reverse consistent distance migration correction matched filter H2 is: Among them, f τ is the distance-frequency axis; The reverse complementary distance migration correction matched filter H3 is Where τ is the distance time.

8. The method for estimating radial velocity of a ship target in an azimuth multi-channel scanning mode according to claim 7, characterized in that: The migration factor D is specifically: Where v is the radar carrier speed and f0 is the radar carrier frequency.

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