Broadband deconvolution two-dimensional high-resolution sonar imaging method and system

Abstract

The invention provides a broadband deconvolution two-dimensional high-resolution sonar imaging method and system, and the method comprises the steps: converting a received array signal from a real signal to an analysis signal, and carrying out the band-pass filtering of the received signal according to a transmitting signal parameter; performing frequency domain beam forming on each array element receiving signal to obtain initial azimuth information; performing matched filtering operation on the output formed by the frequency domain beam by using the transmitted reference signal, completing pulse compression of the linear frequency modulation signal, and obtaining a preliminary imaging result; calculating a distance-angle two-dimensional point spread function corresponding to the initial imaging result; and performing deconvolution operation on the initial imaging result and the distance-angle two-dimensional point spread function by using a two-dimensional R-L algorithm to obtain a final two-dimensional high-resolution imaging result. The method has the advantages that a high-resolution low-sidelobe-level imaging result can be obtained through the method based on the deconvolution technology, the robustness is good, and the robustness is equivalent to that of a traditional frequency domain beam forming and pulse compression imaging method.

Description

Technical Field

[0001] This application belongs to the field of sonar signal processing, and specifically relates to a broadband deconvolution two-dimensional high-resolution sonar imaging method and system. Background Art

[0002] Sonar imaging technology has a wide range of applications in many fields such as target detection and recognition, underwater positioning and navigation, and underwater security. The research on high-resolution sonar imaging technology is of great significance for improving the performance of underwater target detection and recognition. Compared with narrowband signals, broadband signals have stronger anti-reverberation ability, can adapt to complex ocean environments, and at the same time can improve the sonar range by means of pulse compression technology and compress it into a narrow pulse at the receiving end to obtain a higher range resolution. Therefore, it is widely used in today's high-frequency imaging sonar systems.

[0003] Sonar imaging algorithms based on deconvolution technology can improve the resolution while reducing the sidelobe level and have good robustness, so they have attracted much attention. However, most of these methods are applied to narrowband signal imaging systems, and there is less research on broadband signals. Existing broadband deconvolution algorithms mostly use the approximation of the point spread function of narrowband signals to perform deconvolution operations on the azimuth map of broadband signals, which may lead to the phenomenon of sidelobe level and noise amplification caused by the mismatch of the point spread function. Summary of the Invention

[0004] The purpose of this application is to propose a sonar imaging method and system based on deconvolution technology suitable for broadband signals, and to deduce the range-angle two-dimensional point spread function corresponding to the preliminary imaging result obtained by frequency-domain beamforming and pulse compression of the array received signal, so as to achieve two-dimensional high-resolution imaging.

[0005] To achieve the above purpose, this application proposes a broadband deconvolution two-dimensional high-resolution sonar imaging method, including:

[0006] Step 1: Convert the received array signal from a real signal to an analytic signal, and perform band-pass filtering on the received signal according to the transmitted signal parameters;

[0007] Step 2: Perform frequency-domain beamforming on the received signals of each array element to obtain preliminary azimuth information;

[0008] Step 3: Use the transmitted reference signal to perform a matched filtering operation on the output of the frequency-domain beamforming, complete the pulse compression of the linear frequency modulation signal, and obtain a preliminary imaging result;

[0009] Step 4: Calculate the range-angle two-dimensional point spread function corresponding to the preliminary imaging result;

[0010] Step 5: Apply the 2D R-L algorithm to perform deconvolution on the preliminary imaging result and the range-angle 2D point spread function to obtain the final 2D high-resolution imaging result.

[0011] As an improvement of the above method, step 1 includes:

[0012] The analytical signal received by the m-th array element is:

[0013] s m (n) = I m (n) + jQ m (n)

[0014] where s m (n) is the analytical signal received by the m-th array element at the n-th sampling moment, m ∈ [0, M - 1], and M is the number of array elements; I m (n) and Q m (n) are the signals received by the I and Q channels of the m-th array element respectively; j represents the imaginary unit.

[0015] As an improvement of the above method, step 2 includes:

[0016] Perform discrete Fourier transform on the signals received by each array element to obtain the narrowband data S m (k):

[0017]

[0018] where N is the number of time-domain sampling points for discrete Fourier transform, and k ∈ [0, N - 1] is the corresponding number of points in the frequency domain; σ p is the reflection coefficient of the p-th target;

[0019] Perform narrowband beamforming on the narrowband signals corresponding to each frequency point, and the output result Y(k, θ) is:

[0020] Y(k, θ) = w H (f k , θ)S(k)

[0021] where the superscript H represents conjugate transpose; S(k) = [S0(k) S1(k) … S M-1 (k)] T is the received signal of each array element, and the superscript T represents vector transpose; w(f k , θ) represents the conventional beamforming weighting vector corresponding to each angle of the uniform linear array:

[0022]

[0023] where is the frequency corresponding to each angle of the uniform linear array; f sf is the sampling frequency; θ is the angle corresponding to each angle of the uniform linear array; d is the element spacing of the uniform linear array; c is the speed of sound;

[0024] Perform an inverse Fourier transform on Y(k,θ) to obtain the time-domain result:

[0025]

[0026] As an improvement of the above method, step 3 includes:

[0027] The specific operation of pulse compression is:

[0028]

[0029] where is the conjugate of the flipped reference signal, * is the convolution operator, and s(n) is the discrete reference signal obtained by performing time-domain sampling on the reference signal s(t) transmitted by the active sonar at the sampling frequency f s to obtain the discrete reference signal;

[0030] s(t) is the reference signal transmitted by the active sonar:

[0031]

[0032] where f0 is the center frequency; K is the modulation slope; T is the signal pulse width; t is the continuous time.

[0033] As an improvement of the above method, step 4 includes:

[0034] The range-angle two-dimensional point spread function PSF(n,sinθ) is:

[0035] PSF(n,sinθ) = r(n) * B(n,sinθ)

[0036] where r(n) is the discrete signal after pulse compression of the chirp signal; B(n,sinθ) is the time-domain result obtained by performing an inverse Fourier transform on the frequency-domain beam response, and the specific formula is as follows:

[0037]

[0038] This application also provides a broadband deconvolution two-dimensional high-resolution sonar imaging system, which is implemented based on the above method. The system includes:

[0039] A band-pass filtering module, which is used to convert the received array signal from a real signal into an analytic signal and perform band-pass filtering on the received signal according to the transmitted signal parameters;

[0040] A beamforming module, which is used to perform frequency-domain beamforming on the received signals of each element to obtain preliminary azimuth information;

[0041] A pulse compression module, which is used to perform a matched filtering operation on the output of the frequency-domain beamforming by using the transmitted reference signal, complete the pulse compression of the chirp signal, and obtain a preliminary imaging result;

[0042] A two-dimensional point spread function calculation module, which is used to calculate the range-angle two-dimensional point spread function corresponding to the preliminary imaging result;

[0043] An imaging module, which is used to perform a deconvolution operation on the preliminary imaging result and the range-angle two-dimensional point spread function by applying the two-dimensional R-L algorithm to obtain the final two-dimensional high-resolution imaging result.

[0044] Compared with the prior art, the advantages of the present application are as follows:

[0045] 1. The present invention provides a range-angle two-dimensional point spread function corresponding to the preliminary imaging result obtained by frequency-domain beamforming and pulse compression of a broadband signal.

[0046] 2. By performing a deconvolution operation on the preliminary imaging result and the two-dimensional point spread function by means of the two-dimensional R-L algorithm, a sonar image with two-dimensional high resolution and low sidelobe level can be obtained.

[0047] 3. The method based on the deconvolution technology has good robustness and is comparable to the traditional frequency-domain beamforming and pulse compression imaging methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 The figure shows a schematic diagram of a horizontal uniform linear array;

[0049] Figure 2 The figure shows a flowchart based on a horizontal uniform linear array and a chirp signal;

[0050] Figure 3 The figure shows the preliminary imaging result obtained by frequency-domain beamforming and pulse compression of the array received signal;

[0051] Figure 4 The figure shows the imaging result obtained by performing two-dimensional deconvolution on the preliminary imaging result by the method provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] The technical solution of the present application will be described in detail below with reference to the accompanying drawings.

[0053] The present application provides a broadband deconvolution two-dimensional high-resolution sonar imaging method and system, directly deriving the range-angle two-dimensional point spread function corresponding to the two-dimensional imaging result obtained by frequency-domain beamforming and pulse compression of the array received signal, and obtaining a sonar image with high resolution and low sidelobe level through a two-dimensional deconvolution operation on the conventional imaging result.

[0054] Example 1

[0055] The broadband deconvolution two-dimensional high-resolution sonar imaging method includes:

[0056] Step 1: Convert the received array signal from a real signal to an analytic signal, and perform band-pass filtering on the received signal according to the transmit signal parameters;

[0057] Convert the real signal received by the array element into an analytic signal. Assume that the signals received by the I and Q channels of the m-th array element are I m (n) and Q m (n) respectively. The analytic signal received by the m-th array element is:

[0058] s m (n) = I m (n) + jQ m (n)

[0059] where s m (n) is the analytic signal received by the m-th array element at the n-th sampling moment, m ∈ [0, M - 1], and M is the number of array elements.

[0060] Step 2: Perform frequency-domain beamforming on the received signals of each array element to obtain preliminary azimuth information.

[0061] First, perform discrete Fourier transform on the received signals of each array element to obtain frequency-domain narrowband data, perform narrowband beamforming on the narrowband signals corresponding to each frequency point, and perform inverse Fourier transform on the results after beamforming at each frequency point to obtain the output of frequency-domain beamforming. The array involved in the present invention is a uniform linear array, and the narrowband beamforming method adopted is conventional beamforming. The discrete Fourier transform of the received signals of each array element is:

[0062]

[0063] where N is the number of time-domain sampling points for discrete Fourier transform, k ∈ [0, N - 1] is the corresponding number of points in the frequency domain, and σ p is the reflection coefficient of the p-th target. Write the received signals of each array element in vector form:

[0064] S(k) = [S0(k) S1(k) … S M-1 (k)] T

[0065] where the superscript T represents vector transpose.

[0066] The conventional beamforming weighting vectors corresponding to each angle of the uniform linear array are as follows:

[0067]

[0068] where, is the corresponding frequency, f s is the sampling frequency, θ is the corresponding angle, d is the element spacing of the uniform linear array, and c is the speed of sound. The output of conventional beamforming at each frequency point is:

[0069] Y(k,θ) = w H (f k ,θ)S(k)

[0070] where the superscript H represents conjugate transpose.

[0071] Performing an inverse Fourier transform on Y(k,θ) gives the time-domain result:

[0072]

[0073] Step 3: Use the transmitted reference signal to perform a matched filtering operation on the output of frequency-domain beamforming, complete the pulse compression of the chirp signal, and obtain a preliminary imaging result.

[0074] Assume that the reference signal transmitted by the active sonar is a chirp signal:

[0075]

[0076] where f0 is the center frequency, K is the modulation slope, and T is the signal pulse width. Sampling s(t) in the time domain at the sampling frequency f s gives the discrete reference signal s(n). The specific operation of pulse compression is:

[0077]

[0078] where is the conjugate of the flipped reference signal, and * is the convolution operator.

[0079] Step 4: Calculate the range-angle two-dimensional point spread function corresponding to the preliminary imaging result.

[0080] Calculate the range-angle two-dimensional point spread function corresponding to the preliminary imaging result y c (n,θ). Write y c (n,θ) in the form of a two-dimensional convolution [σ p δ(n - n p )δ(sinθ - sinθ p )] ** [r(n) * B(n, sinθ)], where σ p δ(n - n p )δ(sinθ - sinθ p) is the azimuth-range two-dimensional high-resolution imaging result containing the target azimuth and distance information. r(n)*B(n, sinθ) is the corresponding two-dimensional point spread function, and B(n, sinθ) is the time-domain result after the inverse Fourier transform of the frequency-domain beam response.

[0081] For the chirp signal r(t):

[0082]

[0083] r(n) is the discrete sampling signal of r(t) after pulse compression.

[0084] For a uniform linear array:

[0085]

[0086] Therefore, the range-angle two-dimensional point spread function is:

[0087] PSF(n, sinθ) = r(n)*B(n, sinθ)

[0088] Step Five: Apply the two-dimensional R-L algorithm to perform deconvolution on the preliminary imaging result and the two-dimensional point spread function in Step Four to obtain the final two-dimensional high-resolution imaging result.

[0089] Use the two-dimensional R-L algorithm to perform deconvolution on the preliminary imaging result. The R-L algorithm is a method based on Bayes' formula. Therefore, the variables participating in the operation need to be non-negative real numbers between 0 and 1. First, take the modulus of y c (n, θ), and then assume the following approximation under the condition that the target echoes are incoherent:

[0090]

[0091] For |y c (n, θ)| 2 and |r(n)*B(n, sinθ)| 2 Use the two-dimensional R-L algorithm to perform deconvolution operation to obtain the high-resolution results in the azimuth and range directions |σ p δ(n - n p )δ(sinθ - sinθ p )| 2 .

[0092] Example 2

[0093] Referring to Figure 2 the flowchart shown, the imaging method proposed in this example includes the following steps:

[0094] Step One: Refer to Figure 1 the schematic diagram of the horizontal uniform linear array shown to set the array parameters and the relevant parameters of the sound source signal:

[0095] In this embodiment, the number of elements of the horizontal linear array is set to 48, and the element spacing is 10 mm. The nine targets are located at (4.8 m, 0°), (4.8 m, 5°), (4.8 m, 10°), (5 m, 0°), (5 m, 5°), (5 m, 10°), (5.2 m, 0°), (5.2 m, 5°), and (5.2 m, 10°). The reference signal transmitted by the active sonar is a chirp signal with a center frequency of 80 kHz, a bandwidth of 20 kHz, and a pulse width of 8 ms. The signal-to-noise ratio is -10 dB, and the underwater sound speed is 1500 m / s.

[0096] Step 2: Construct the reference signal s(t) and the received signals s m (t), m ∈ [0, M - 1], where M is the number of elements.

[0097]

[0098] The received signals of each element are as follows:

[0099]

[0100] Among them, P is the number of targets. In this example, P = 9, and σ p is the reflection coefficient of the p-th target, and τ p = 2r p / c is the time delay of the echo signal of the p-th target to the reference element. τ m (θ p ) = (m - 1)dsinθ p / c is the difference between the time delay of the echo signal of the p-th target to the m-th element and its time delay to the reference element. Sampling the continuous signal at the sampling rate f s to obtain a discrete signal, and let t = n / f s , n ∈ [0, N - 1], where N is the number of time-domain sampling points.

[0101] Step 3: Perform frequency-domain beamforming on the array received signals. Considering the computational complexity, first segment the long signal received by the array. The segment length is preferably a power of 2, and then perform discrete Fourier transform on each segment of the signal to obtain the frequency-domain signal and write the frequency-domain signals of each element in vector form:

[0102]

[0103] S(k) = [S0(k) S1(k)…S M-1 (k)] T

[0104] The weighted vectors of each frequency point corresponding to the uniform linear array:

[0105]

[0106] in, For the corresponding frequency, the beam pattern obtained by narrowband conventional beamforming at each frequency point is:

[0107]

[0108] By taking the inverse Fourier transform of Y(k,θ), we can obtain the time domain result y(n,θ) of frequency domain beamforming.

[0109] Step 4: Use the reference signal to perform pulse compression on the frequency domain beamforming result y(n,θ):

[0110]

[0111] The matched filter is the conjugate of the flipped reference signal, and * is the convolution operator. c The preliminary imaging results of the traditional broadband signal imaging method based on frequency domain beamforming and pulse compression can be obtained by taking the modulus square of (n,θ), such as Figure 3 shown.

[0112] Step 5: y c (n,θ) can be decomposed into the following form of convolution of distance-angle two-dimensional point spread function and target position distribution function [σ p δ(nn p )δ(sinθ-sinθ p )]**[r(n)*B(n,sinθ)], thereby calculating the distance-angle two-dimensional point spread function PSF(n,sinθ)=r(n)*B(n,sinθ).

[0113] Step 6: Use the two-dimensional RL algorithm to perform deconvolution operation on the preliminary imaging results. Since the RL algorithm is an algorithm based on the Bayesian formula, it requires that the variables involved in the operation are non-negative real numbers between 0 and 1, so y c Taking the square of the modulus of (n,θ) and assuming that the target echo signals are incoherent, we get the following approximation:

[0114] |y c (n,θ)| 2 =|[σ p δ(nn p )δ(sinθ-sinθ p )]**[r(n)*B(n,sinθ)]| 2

[0115] ≈|σ p δ(nn p)δ(sinθ - sinθ p )| 2 **|r(n)*B(n,sinθ)| 2

[0116] The specific iterative formula of the two-dimensional R - L algorithm is as follows:

[0117]

[0118]

[0119] Among them, q(x, y), B(x, y) and p(x, y) satisfy the relationship B(x, y) = q(x, y) ** p(x, y), (.) (r) represents the result of the r - th iteration. In this example, the number of iterations selected is 12 times, and the finally obtained two - dimensional high - resolution and low - sidelobe - level imaging result is as Figure 4 shown. From the comparison of the two - dimensional images of Figure 3 and Figure 4 , it can be seen that under the condition of relatively low signal - to - noise ratio, after the conventional imaging result is processed by the method proposed in the present invention, a narrower main lobe width and a lower sidelobe level can be obtained in both the azimuth direction and the range direction.

[0120] Embodiment 3

[0121] This application also provides a wide - band deconvolution two - dimensional high - resolution sonar imaging system, which is implemented based on the above - mentioned method. The system includes:

[0122] A band - pass filtering module, which is used to convert the received array signal from a real signal into an analytic signal and perform band - pass filtering on the received signal according to the transmission signal parameters;

[0123] A beam - forming module, which is used to perform frequency - domain beam - forming on the received signals of each array element to obtain preliminary azimuth information;

[0124] A pulse - compression module, which is used to perform a matched - filtering operation on the output of the frequency - domain beam - forming by using the transmitted reference signal to complete the pulse compression of the linear frequency - modulated signal and obtain a preliminary imaging result;

[0125] A module for calculating the two - dimensional point - spread function, which is used to calculate the range - angle two - dimensional point - spread function corresponding to the preliminary imaging result;

[0126] An imaging module, which is used to perform deconvolution operation on the preliminary imaging result and the range - angle two - dimensional point - spread function by applying the two - dimensional R - L algorithm to obtain the final two - dimensional high - resolution imaging result.

[0127] The present application may also provide a computer device, including: at least one processor, a memory, at least one network interface, and a user interface. Each component in the device is coupled together through a bus system. It can be understood that the bus system is used to implement the connection and communication between these components. In addition to the data bus, the bus system also includes a power bus, a control bus, and a status signal bus.

[0128] Among them, the user interface may include a display, a keyboard, or a pointing device. For example, a mouse, a trackball, a touchpad, or a touch screen, etc.

[0129] It can be understood that the memory in the disclosed embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchlink dynamic random access memory (SLDRAM), and direct rambus random access memory (DRRAM). The memories described herein are intended to include but not be limited to these and any other suitable types of memories.

[0130] In some embodiments, the memory stores the following elements, executable modules, or data structures, or subsets thereof, or extended sets thereof: an operating system and an application program.

[0131] Among them, the operating system includes various system programs, such as the framework layer, the core library layer, the driver layer, etc., which are used to implement various basic services and handle hardware-based tasks. The application programs include various application programs, such as Media Player, Browser, etc., which are used to implement various application services. The program for implementing the method of the embodiment of the present disclosure may be included in the application programs.

[0132] In the above-mentioned embodiment, the program or instruction stored in the memory may also be called. Specifically, it may be the program or instruction stored in the application program. The processor is used for:

[0133] Executing the steps of the above method.

[0134] The above method can be applied to the processor or implemented by the processor. The processor may be an integrated circuit chip with signal processing capabilities. During the implementation process, each step of the above method can be completed by the integrated logic circuit in the hardware of the processor or the instruction in the form of software. The above-mentioned processor may be a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps and logic block diagrams disclosed above. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. Combining the steps of the above-disclosed method can be directly embodied as being executed and completed by the hardware decoding processor, or by a combination of the hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method.

[0135] It can be understood that these embodiments described in the present application can be implemented by hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in the present application, or a combination thereof.

[0136] For software implementation, the techniques of the present application can be implemented by executing the functional modules of the present application (such as procedures, functions, etc.). The software code can be stored in a memory and executed by a processor. The memory can be implemented inside or outside the processor.

[0137] The present application can also provide a non-volatile storage medium for storing a computer program. When the computer program is executed by a processor, the various steps in the above method embodiments can be implemented.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit them. Although the present application has been described in detail with reference to the embodiments, those of ordinary skill in the art should understand that any modification or equivalent replacement of the technical solutions of the present application does not depart from the spirit and scope of the technical solutions of the present application, and they should all be covered within the scope of the claims of the present application.

Claims

1. A broadband deconvolution two-dimensional high-resolution sonar imaging method, comprising: Step 1: Convert the received array signal from a real signal to an analytical signal, and perform bandpass filtering on the received signal according to the transmit signal parameters; Step 2: Perform frequency domain beamforming on the received signals of each array element to obtain preliminary azimuth information; Step 3: Use the transmitted reference signal to perform a matched filtering operation on the output of the frequency domain beamforming to complete the pulse compression of the linear frequency modulation signal and obtain preliminary imaging results; Step 4: Calculate the distance-angle two-dimensional point spread function corresponding to the preliminary imaging results; Step 5: Apply the 2D RL algorithm to perform deconvolution operation on the preliminary imaging results and the distance-angle 2D point spread function to obtain the final 2D high-resolution imaging result.

2. The broadband deconvolution two-dimensional high-resolution sonar imaging method according to claim 1, wherein The step 1 comprises: The analytical signal received by array element m is: s m (n) = I m (n) + jQ m (n) Among them, s m (n) is the analytical signal received by the array element m at the nth sampling moment, m∈[0,M-1], M is the number of array elements; I m (n) and Q m (n) are the signals received by the two channels I and Q of array element m; j represents a complex unit.

3. The broadband deconvolution two-dimensional high-resolution sonar imaging method according to claim 2, characterized in that, The step 2 includes: The discrete Fourier transform is performed on the received signals of each array element to obtain the narrowband data S m (k): Where N is the number of sampling points in the time domain for discrete Fourier transform, k∈[0,N-1] is the number of points corresponding to the frequency domain; σ p is the reflection coefficient of the pth target; Perform narrowband beamforming on the narrowband signals corresponding to each frequency point, and the output result Y(k,θ) is: Y(k,θ) = w H (f k ,θ)S(k) where the superscript H represents the conjugate transpose; S(k) = [S0(k) S1(k) … S M-1 (k)] T is the received signal of each array element, and the superscript T represents the vector transpose; w(f k , θ) represents the conventional beamforming weighting vector corresponding to each angle of the uniform linear array: in, is the frequency corresponding to each angle of the uniform linear array; f s is the sampling frequency; θ is the angle corresponding to each angle of the uniform linear array; d is the element spacing of the uniform linear array; c is the speed of sound; Perform inverse Fourier transform on Y(k,θ) to get the time domain result:

4. The broadband deconvolution two-dimensional high-resolution sonar imaging method according to claim 3, characterized in that The step 3 comprises: The specific operation of pulse compression is: wherein, is the conjugate of the flipped reference signal, * is the convolution operator, and s(n) is the discrete reference signal obtained by sampling the reference signal s(t) transmitted by the active sonar in the time domain at the sampling frequency f s for time-domain sampling; s(t) is the reference signal emitted by active sonar: Where f0 is the center frequency; K is the modulation slope; T is the signal pulse width; and t is the continuous time.

5. The broadband deconvolution two-dimensional high-resolution sonar imaging method according to claim 4, characterized in that: The step 4 comprises: The distance-angle two-dimensional point spread function PSF(n,sinθ) is: PSF(n,sinθ)=r(n)*B(n,sinθ) Where r(n) is the discrete signal of the linear frequency modulation signal after pulse compression; B(n, sinθ) is the time domain result of the frequency domain beam response after inverse Fourier transform:

6. A broadband deconvolution two-dimensional high-resolution sonar imaging system, implemented based on the method of any one of claims 1 to 5, characterized in that: The system comprises: The bandpass filter module is used to convert the received array signal from a real signal to an analytical signal and perform bandpass filtering on the received signal according to the transmit signal parameters; The beamforming module is used to perform frequency domain beamforming on the received signals of each array element to obtain preliminary azimuth information; The pulse compression module is used to perform a matched filtering operation on the output of the frequency domain beamforming using the transmitted reference signal to complete the pulse compression of the linear frequency modulation signal and obtain preliminary imaging results; A two-dimensional point spread function calculation module is used to calculate the distance-angle two-dimensional point spread function corresponding to the preliminary imaging result; and The imaging module is used to apply the two-dimensional RL algorithm to perform deconvolution operation on the preliminary imaging results and the distance-angle two-dimensional point spread function to obtain the final two-dimensional high-resolution imaging results.

Images