A broadband deconvolution two-dimensional high-resolution sonar imaging method and system

Abstract

The application provides a broadband deconvolution two-dimensional high-resolution sonar imaging method and system, which comprises the following steps: converting the received array signal from a real signal to an analytic signal, and performing band-pass filtering on the received signal according to the transmission signal parameter; performing frequency domain beam forming on each array element received signal to obtain preliminary azimuth information; performing matched filtering operation on the output of the frequency domain beam forming by using the transmitted reference signal to complete pulse compression of the linear frequency modulation signal and obtain preliminary imaging results; calculating the distance-angle two-dimensional point spread function corresponding to the preliminary imaging results; and performing deconvolution operation on the preliminary imaging results and the distance-angle two-dimensional point spread function by using a two-dimensional R-L algorithm to obtain the final two-dimensional high-resolution imaging results. The application has the advantages that: the method based on the deconvolution technology can obtain high-resolution low-sidelobe imaging results, and the robustness is good, and the robustness is equivalent to that of the traditional frequency domain beam forming and pulse compression imaging method.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of sonar signal processing, and particularly relates to a wideband deconvolution two-dimensional high-resolution sonar imaging method and system. BACKGROUND

[0002] Sonar imaging technology has a wide range of applications in target detection and identification, underwater positioning and navigation, underwater security and other fields. The research on high-resolution sonar imaging technology is of great significance to improve the performance of underwater target detection and identification. Wideband signals have stronger anti-reverberation ability than narrowband signals, can adapt to complex marine environments, and can also improve the sonar range by means of pulse compression technology and compress narrow pulses at the receiving end to obtain higher range resolution, so they are widely used in today's high-frequency imaging sonar systems.

[0003] Sonar imaging algorithms based on deconvolution technology can improve resolution while reducing sidelobe level and have good robustness, so they are widely concerned. However, most of these methods are applied to narrowband signal imaging systems, and there are few studies on wideband signals. Existing wideband deconvolution algorithms mostly use the point spread function of narrowband signals to approximate the azimuth map of wideband signals for deconvolution operation, which may cause sidelobe level and noise amplification due to mismatch of the point spread function. SUMMARY

[0004] The purpose of the present application is to propose a sonar imaging method and system based on deconvolution technology suitable for wideband signals, and to derive the distance-angle two-dimensional point spread function corresponding to the preliminary imaging results of the array received signal after frequency domain beamforming and pulse compression, so as to realize two-dimensional high-resolution imaging.

[0005] In order to achieve the above purpose, the present application proposes a wideband deconvolution two-dimensional high-resolution sonar imaging method, which comprises:

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

[0007] Step 2: performing frequency domain beamforming on each array element received signal to obtain preliminary azimuth information;

[0008] Step 3: performing matched filtering operation on the output of frequency domain beamforming using the transmitted reference signal to complete pulse compression of the linear frequency modulation signal, and obtaining preliminary imaging results;

[0009] Step 4: calculating the distance-angle two-dimensional point spread function corresponding to the preliminary imaging results;

[0010] Step 5: 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.

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

[0012] The analyzed signal received by the array element is:

[0013]

[0014] in, For the first Each sampling time The parsed signal received by the array element , The number of array elements; and They are respectively The signals received by the I and Q channels of the array element; j represents the complex unit.

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

[0016] The frequency domain narrowband data is obtained by performing Discrete Fourier Transform on the received signals of each array element. :

[0017]

[0018] in, The number of time-domain sampling points for performing the Discrete Fourier Transform. This represents the number of points corresponding to the frequency domain. For the first The reflection coefficient of the target;

[0019] Narrowband beamforming is performed on the narrowband signals corresponding to each frequency point, and the output results are obtained. for:

[0020]

[0021] The superscript H indicates conjugate transpose; The received signals for each array element are represented by the superscript T, which indicates vector transpose. This represents the conventional beamforming weighting vector corresponding to each angle of a uniform linear array:

[0022]

[0023] in, These are the frequencies corresponding to each angle of a uniform linear array; The sampling frequency; These are the angles corresponding to each angle of a uniform linear array; is the element spacing of the uniform linear array; is the sound speed;

[0024] is the inverse Fourier transform of

[0025] .

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

[0027] The specific operation of pulse compression is:

[0028]

[0029] wherein, is the conjugate of the flipped reference signal, is the convolution operator, is the reference signal transmitted by the active sonar, is sampled in time domain at a sampling frequency to obtain a discrete reference signal;

[0030] is the reference signal transmitted by the active sonar:

[0031]

[0032] wherein, is the center frequency; is the modulation slope; is the signal pulse width; t is the continuous time.

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

[0034] The distance-angle two-dimensional point spread function is:

[0035]

[0036] wherein, is the discrete signal after pulse compression of the linear frequency modulation signal; is the time domain result obtained by inverse Fourier transform of the frequency domain beam response, and the specific formula is as follows:

[0037] .

[0038] The application also provides a wideband deconvolution two-dimensional high-resolution sonar imaging system, which is realized based on the above method, and the system comprises:

[0039] ​​A band-pass filtering module is configured to convert the received array signal from a real signal to a complex signal, and perform band-pass filtering on the received signal according to a parameter of the transmitted signal.

[0040] A beamforming module is configured to perform frequency-domain beamforming on the received signal of each array element to obtain preliminary azimuth information.

[0041] A pulse compression module is configured to perform matched filtering operation on the output of the frequency-domain beamforming by using the transmitted reference signal, complete pulse compression of the linear frequency modulation signal, and obtain preliminary imaging results.

[0042] A two-dimensional point spread function calculation module is configured to calculate a distance-angle two-dimensional point spread function corresponding to the preliminary imaging results.

[0043] An imaging module is configured to perform deconvolution operation on the preliminary imaging results and the distance-angle two-dimensional point spread function by using a two-dimensional R-L algorithm to obtain final two-dimensional high-resolution imaging results.

[0044] Compared with the prior art, the application has the following advantages:

[0045] 1. The application provides a distance-angle two-dimensional point spread function corresponding to preliminary imaging results obtained by frequency-domain beamforming and pulse compression of a wideband signal.

[0046] 2. The two-dimensional high-resolution low-sidelobe sonar image can be obtained by performing deconvolution operation on the preliminary imaging results and the two-dimensional point spread function by using the two-dimensional R-L algorithm.

[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 method. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 Fig. 1 shows a schematic diagram of a uniform linear array;

[0049] Figure 2 Fig. 2 shows a flowchart based on the uniform linear array and the linear frequency modulation signal;

[0050] Figure 3 Fig. 3 shows preliminary imaging results obtained by frequency-domain beamforming and pulse compression of the array received signal;

[0051] Figure 4 Fig. 4 shows imaging results obtained by performing two-dimensional deconvolution on the preliminary imaging results by using the method provided by the application. DETAILED DESCRIPTION

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

[0053] This application provides a broadband deconvolutional two-dimensional high-resolution sonar imaging method and system, which directly derives 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 obtains a high-resolution, low-sidelobe sonar image by performing two-dimensional deconvolution operation on the conventional imaging result.

[0054] Example 1

[0055] Broadband deconvolutional two-dimensional high-resolution sonar imaging methods include:

[0056] 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 transmitted signal parameters;

[0057] The real signals received by the array elements are converted into analytic signals, assuming... The signals received by the I and Q channels of the array element are respectively and , The analyzed signal received by the array element is:

[0058]

[0059] in, For the first Each sampling time The parsed signal received by the array element , 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, the received signals of each array element are subjected to Discrete Fourier Transform (DFT) to obtain frequency-domain narrowband data. Narrowband beamforming is then performed on the narrowband signals corresponding to each frequency point. Finally, the results of beamforming at each frequency point are subjected to Inverse Fourier Transform (IFT) to obtain the output of the time-domain beamforming. The array involved in this invention is a uniform linear array, and the narrowband beamforming method used is conventional beamforming. The DFT of the received signals of each array element is as follows:

[0062]

[0063] in, The number of time-domain sampling points for performing the Discrete Fourier Transform. The number of points corresponding to the frequency domain. For the first The reflection coefficients of each target. The received signals of each array element are written in vector form:

[0064]

[0065] where the superscript T denotes vector transpose.

[0066] The conventional beamforming weight vector corresponding to each angle of the uniform linear array is as follows:

[0067]

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

[0069]

[0070] where the superscript H denotes conjugate transpose.

[0071] Performing inverse Fourier transform on the time-domain result is obtained:

[0072]

[0073] Step three: perform matched filtering operation on the output of the frequency-domain beamforming using the transmitted reference signal, complete pulse compression of the linear frequency modulation signal, and obtain the preliminary imaging result.

[0074] Suppose that the reference signal transmitted by the active sonar is a linear frequency modulation signal:

[0075]

[0076] where, is the center frequency, is the modulation slope, is the signal pulse width. Perform time-domain sampling on at a sampling frequency to obtain the discrete reference signal The specific operation of pulse compression is:

[0077]

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

[0079] Step four: calculate the distance-angle two-dimensional point spread function corresponding to the preliminary imaging result.

[0080] Calculate the distance-angle two-dimensional point spread function corresponding to the preliminary imaging result , write in the form of two-dimensional convolution ,in This is a two-dimensional high-resolution azimuth-range imaging result containing target azimuth and range information. For the corresponding two-dimensional point spread function, This is the time-domain result of the frequency-domain beam response after inverse Fourier transform.

[0081] For linear frequency modulation signals :

[0082]

[0083] yes Discrete sampled signal after pulse compression.

[0084] For a uniform linear array:

[0085]

[0086]

[0087] Therefore, the distance-angle two-dimensional point diffusion function is:

[0088]

[0089] Step 5: Apply the 2D RL algorithm to perform deconvolution operation on the preliminary imaging results and the 2D point spread function in Step 4 to obtain the final 2D high-resolution imaging results.

[0090] The preliminary imaging results are deconvolved using a two-dimensional RL algorithm. RL is a method based on Bayes' theorem; therefore, the variables involved in the operation need to be non-negative real numbers between 0 and 1. First, the... Taking the modulus value, and assuming that the echoes from each target are incoherent, we take the following approximation:

[0091]

[0092]

[0093] right and High-resolution results in the azimuth and range directions were obtained by performing deconvolution operations using a two-dimensional RL algorithm. .

[0094] Example 2

[0095] Reference Figure 2 The flowchart shown illustrates the imaging method proposed in this embodiment, which includes the following steps:

[0096] Step 1: Reference Figure 1The schematic diagram of the even horizontal linear array shown sets the array parameters and the related parameters of the sound source signal:

[0097] 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 , , , , , , , , . The reference signal emitted by the active sonar is a linear frequency modulation signal with a center frequency , a bandwidth , and a pulse width . The signal-to-noise ratio is -10 dB, and the underwater sound speed is 1500 m / s.

[0098] Step two: construct the reference signal and the received signal of each element , , is the number of elements.

[0099]

[0100] The received signal of each element is:

[0101]

[0102] wherein is the number of targets, which is , is the reflection coefficient of the th target, is the time delay of the echo signal of the th target to the reference element, is the difference between the time delay of the echo signal of the th target to the th element and the time delay of the echo signal of the th target to the reference element. The continuous signal is sampled at a sampling rate to obtain a discrete signal, and let , , be the number of time domain sampling points.

[0103] Step three: perform frequency domain beamforming on the array received signal. For consideration of the amount of calculation, the long signal received by the array is segmented first, and the segmentation length is preferably a power of 2. Then, the discrete Fourier transform is performed on each segment of the signal to obtain a frequency domain signal, and the frequency domain signals of the elements are written in the form of a vector:

[0104]

[0105]

[0106] Weighted vectors for each frequency point corresponding to a uniform linear array:

[0107]

[0108] in, For the corresponding frequencies, the beam patterns obtained by narrowband conventional beamforming at each frequency point are as follows:

[0109]

[0110]

[0111] right The inverse Fourier transform yields the time-domain results of frequency-domain beamforming. .

[0112] Step 4: Results of frequency domain beamforming using a reference signal Perform pulse compression:

[0113]

[0114] Matched filter The conjugate of the reference signal after inversion is taken. This is the convolution operator. Squaring the modulus yields the preliminary imaging results of traditional broadband signal imaging methods based on frequency domain beamforming and pulse compression, such as... Figure 3 As shown.

[0115] Step 5: It can be decomposed into the form of a convolution of a distance-angle two-dimensional point spread function and a target position distribution function. Therefore, the distance-angle two-dimensional point spread function is calculated. .

[0116] Step Six: Perform deconvolution on the preliminary imaging results using a two-dimensional RL algorithm. Since the RL algorithm is based on Bayes' theorem, it requires that the variables involved in the operation be non-negative real numbers between 0 and 1. Therefore, it is necessary to... Taking the square of the modulus and assuming that the echo signals from each target are incoherent, we obtain the following approximation:

[0117]

[0118]

[0119] The specific iterative formula for the two-dimensional RL algorithm is as follows:

[0120]

[0121]

[0122] wherein, , and satisfy the relationship , represents the result of the iteration. The number of iterations selected in this example is 12, and the final two-dimensional high-resolution low-sidelobe imaging result is shown in FIG. 6. The two-dimensional image contrast of FIG. 6 and FIG. 7 can show that, in the case of low signal-to-noise ratio, the conventional imaging result can be processed by the method proposed in the present application to obtain narrower main lobe width and lower sidelobe level in both azimuth and range directions. Figure 4 Figure 3 Figure 4

[0123] Embodiment 3

[0124] The present application also provides a wideband deconvolution two-dimensional high-resolution sonar imaging system, which is realized based on the above method, and the system comprises:

[0125] a band-pass filtering module, configured to 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 transmission signal parameter;

[0126] a beam forming module, configured to perform frequency domain beam forming on the received signal of each array element to obtain preliminary azimuth information;

[0127] a pulse compression module, configured to perform matched filtering operation on the output of the frequency domain beam forming by using the transmitted reference signal, complete pulse compression of the linear frequency modulation signal, and obtain a preliminary imaging result;

[0128] a two-dimensional point spread function calculation module, configured to calculate the distance-angle two-dimensional point spread function corresponding to the preliminary imaging result;

[0129] an imaging module, configured to perform deconvolution operation on the preliminary imaging result and the distance-angle two-dimensional point spread function by using the two-dimensional R-L algorithm to obtain a final two-dimensional high-resolution imaging result.

[0130] The present application can also provide a computer device, comprising at least one processor, a memory, at least one network interface and a user interface. The various components in the device are coupled together through a bus system. It can be understood that the bus system is used to realize the connection and communication between the components. In addition to the data bus, the bus system also includes a power bus, a control bus and a status signal bus.

[0131] ​​​​The user interface can include a display, a keyboard, or a pointing device, for example, a mouse, a trackball, a touchpad, or a touchscreen.

[0132] It can be understood that the memory in the embodiments disclosed in the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. The non-volatile memory can be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically EPROM (EEPROM), or a flash memory. The volatile memory can be a Random Access Memory (RAM) used as an external cache. By way of example, but not limitation, many forms of RAM can be used, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory described herein is intended to include, without being limited to, these and any other suitable types of memory.

[0133] In some embodiments, the memory stores elements, executable modules, or data structures, or a subset thereof, or an extended set thereof, such as an operating system and an application program.

[0134] The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, for implementing various basic services and processing hardware-based tasks. The application program includes various application programs, such as a media player (Media Player), a browser (Browser), and the like, for implementing various application services. The program for implementing the method of the embodiments of the present disclosure can be included in the application program.

[0135] In the above-described embodiments, the processor can be configured to perform the following operations by invoking the program or instruction stored in the memory, in particular, the program or instruction stored in the application program:

[0136] performing the steps of the method.

[0137] The method can be applied to or implemented by a processor. The processor can be an integrated circuit chip having a processing capability for signals. In implementation, the steps of the method can be completed by integrated logic circuits or instructions in the form of software in the processor. The processor can 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 device, a discrete gate or transistor logic device, a discrete hardware component. The disclosed methods, steps and logic block diagrams can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the disclosed method can be directly embodied in a hardware code processor for execution, or a combination of hardware and software modules in the code processor for execution. The software module can be located in 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. The storage medium is located in the memory, and the processor reads the information in the memory and combines the hardware to complete the steps of the method.

[0138] It can be understood that the embodiments described in the present application can be realized by hardware, software, firmware, middleware, microcode or a combination thereof. For hardware implementation, the processing unit can be realized 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 executing functions described in the present application or a combination thereof.

[0139] For software implementation, the functions of the present application can be implemented by executing the functional modules (such as processes, functions, etc.) of the present application. The software code can be stored in the memory and executed by the processor. The memory can be implemented in the processor or outside the processor.

[0140] The application further provides a nonvolatile storage medium for storing the computer program. When the computer program is executed by a processor, each step in the above method embodiment can be implemented.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application is described in detail with reference to the embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A broadband deconvolution two-dimensional high-resolution sonar imaging method, comprising: Step 1: converting the received array signal from a real signal to an analytic signal, and performing band-pass filtering on the received signal according to a transmitted signal parameter; Step 2: performing frequency domain beamforming on each array element received signal to obtain preliminary azimuth information; Step 3: performing matched filtering operation on the output of the frequency domain beamforming using a transmitted reference signal to complete pulse compression of the linear frequency modulation signal, and obtaining a preliminary imaging result; Step 4: calculating a distance-angle two-dimensional point spread function corresponding to the preliminary imaging result; Step 5: applying a two-dimensional R-L algorithm to perform deconvolution operation on the preliminary imaging result and the distance-angle two-dimensional point spread function to obtain a final two-dimensional high-resolution imaging result; the step 4 comprises: Distance-angle two-dimensional point spread function is: ； wherein, is the angle corresponding to each angle of the uniform linear array; is a linear frequency modulated signal is the discrete signal after pulse compression; ； where j denotes the complex unit; is the center frequency; is the modulation slope; is the signal pulse width; t is the continuous time; is the time-domain result after inverse Fourier transform of the frequency-domain beam response: ； ； wherein, is the number of array elements; is the element spacing of the uniform linear array; is the sound speed; is the frequency corresponding to each angle of the uniform linear array; is the sampling frequency; is the number of time domain sampling points for performing the discrete Fourier transform, is the number of points in the frequency domain.

2. The broadband deconvolved two-dimensional high resolution sonar imaging method of claim 1, wherein, the step 1 comprises: The analytical signal received by the array element is: ； in, For the first Each sampling time The parsed signal received by the array element ; and They are respectively The signals received by the I and Q channels of the array element.

3. The broadband deconvolved two-dimensional high resolution sonar imaging method of claim 2, wherein, the step 2 comprises: performing a discrete Fourier transform on the received signals to obtain frequency domain narrowband data : ； wherein is the reflection coefficient for the th target; The narrowband signals corresponding to each frequency point are subjected to narrowband beamforming, and the output result is is: ； where the superscript H denotes the conjugate transpose; for the received signal of each array element, the superscript T denotes the vector transpose; denotes the conventional beamforming weight vector corresponding to each angle of the uniform linear array; ； right Performing the inverse Fourier transform yields the time-domain result: 。 4. The broadband deconvolved two-dimensional high resolution sonar imaging method of claim 3, wherein, the step 3 comprises: a specific operation of the pulse compression is: ； wherein, is a post-reversal conjugate of the reference signal, is a convolution operator, is a reference signal emitted by the active sonar, at a sampling frequency is sampled in time domain to obtain a discrete reference signal; Reference signal for active sonar transmission: 。 5. A broadband deconvoluted two-dimensional high resolution sonar imaging system, implemented based on the method of any one of claims 1-4, characterized in that, the system comprises: a band-pass filtering module configured to convert the received array signal from a real signal to an analytic signal, and perform band-pass filtering on the received signal according to a transmitted signal parameter; a beamforming module configured to perform frequency domain beamforming on each array element received signal to obtain preliminary azimuth information; a pulse compression module configured to perform matched filtering operation on the output of the frequency domain beamforming using a transmitted reference signal to complete pulse compression of the linear frequency modulation signal, and obtain a preliminary imaging result; a two-dimensional point spread function calculation module configured to calculate a distance-angle two-dimensional point spread function corresponding to the preliminary imaging result; and an imaging module configured to apply a two-dimensional R-L algorithm to perform deconvolution operation on the preliminary imaging result and the distance-angle two-dimensional point spread function to obtain a final two-dimensional high-resolution imaging result.

Images