A robust distance estimation method and system based on normal mode-constrained modes in dual-channel audio in Arctic ice regions

Abstract

This invention provides a tolerance-based distance estimation method and system for dual-channel acoustic imaging in Arctic ice regions based on normal mode-constrained modes. The method includes: using a normal mode model to simulate and predict the frequency domain sound pressure, phase velocity, group velocity, and mode covariance matrix of the experimental sea area in the Arctic ice region; using Fast Fourier Transform (FFT) to transform the time-domain signal received by the vertical array in the ice region into frequency-domain data; obtaining the sound velocity and depth corresponding to the upper boundary, channel axis, and lower boundary of the Beaufort waveguide based on the dual-channel sound velocity profile of the experimental sea area; obtaining the order index of the Beaufort waveguide modes based on the intersection of the phase velocity dispersion curve with the sound velocity at the upper boundary and channel axis of the Beaufort waveguide; performing mode separation using a matched filtering mode filtering method to extract the modes confined in the Beaufort waveguide; and constructing a tolerance-based distance estimation operator for ice and seabed based on the extracted modes in the Beaufort waveguide to achieve tolerance-based estimation of underwater target distances.

Description

Technical Field

[0001] This invention belongs to the fields of underwater acoustic engineering, marine engineering, and sonar technology, and specifically relates to a tolerance distance estimation method and system based on normal mode-constrained modes in dual-channel audio in the Arctic ice region. Background Technology

[0002] Environmentally tolerant underwater target range estimation is one of the hot and challenging issues in underwater acoustic signal processing research. Conventional matched-field localization suffers from strong environmental sensitivity. When the sea ice / sea surface, sound velocity structure, sea depth, and seabed sediment parameters required for the copied field model are insufficiently obtained, resulting in a mismatch with the actual waveguide environment, it can lead to fuzzy target range and depth estimations or even incorrect localization. In the Arctic Ocean, the frequent interaction between sound waves and sea ice interfaces causes scattering, absorption, and dispersion of acoustic signals. The complexity and diversity of sea ice physical and acoustic parameters significantly amplify the fuzziness in range estimation caused by sea ice parameter mismatches using matched-field methods, limiting the application of model-based processing methods such as matched-field methods in the Arctic Ocean.

[0003] Currently, scholars at home and abroad have mainly conducted research on the mechanisms of propagation characteristics and dispersion characteristics under dual-channel audio in the Arctic, but there are no publicly published articles or patents on tolerance distance estimation methods under dual-channel audio. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing matching field techniques in the high environmental sensitivity of distance estimation in the Arctic Ocean, so as to solve the problem of distance estimation in the Arctic ice region with tolerance.

[0005] To achieve the above objectives, this invention proposes a tolerance distance estimation method based on normal mode-constrained modes in dual-channel audio in Arctic ice regions. This method includes:

[0006] Step 1: Using the normal mode model, simulate and predict the frequency domain sound pressure, phase velocity, group velocity, and modal covariance matrix of the experimental sea area in the Arctic ice zone;

[0007] Step 2: Use Fast Fourier Transform to transform the time-domain signal received by the vertical array in the ice-covered sea area into frequency-domain data;

[0008] Step 3: Based on the dual-channel sound velocity profile of the test sea area, obtain the sound velocity and depth corresponding to the upper boundary, channel axis and lower boundary of the Beaufort waveguide;

[0009] Step 4: Based on the intersection of the phase velocity dispersion curve with the upper boundary of the Beaufort waveguide and the sound velocity of the channel axis, obtain the order index of the Beaufort waveguide modes;

[0010] Step 5: Use matched filtering mode filtering method to perform mode separation and extract the modes confined in the Beaufort waveguide;

[0011] Step 6: Based on the extracted modes in the Beaufort waveguide, construct a tolerance-based distance estimation operator for ice and seabed to achieve tolerance-based estimation of underwater target distances.

[0012] Preferably, the frequency domain sound pressure level in step 1 for:

[0013]

[0014] in, Indicates the distance from the sound source, in meters. Indicates the reception depth, in meters. Discrete angular frequencies; Represents the imaginary unit; Represents the modal depth function; represent The horizontal wavenumber of the first mode; Represents a modal index; This represents the total modal order; Represents the depth of the target; The density of seawater at the target depth;

[0015] First mode phase velocity Group speed for:

[0016]

[0017]

[0018] in, To simulate angular frequency;

[0019] Modal covariance matrix for:

[0020]

[0021]

[0022] Among them, superscript H This indicates the conjugate transpose. Representing different vertical arrays The depth of each array element; This represents the modal depth function of the Nth element. is the modal depth function matrix.

[0023] Preferably, the frequency domain data in step 2 for:

[0024]

[0025]

[0026] in, This represents the complex sound pressure vector of the vertical receiving array. Indicates the number of elements in the vertical array; Represents the modal amplitude function vector; superscript H This indicates the conjugate transpose.

[0027] Preferably, step 3 includes:

[0028] Based on the sound speed profile of the test sea area, the upper boundary has a positive gradient of shallow sound speed and a negative gradient of deep sound speed, with the sound speed maxima at the boundary. Thus, the upper boundary depth of the Beaufort waveguide is obtained. and the corresponding speed of sound , channel axis depth and speed of sound and lower boundary depth and the corresponding speed of sound .

[0029] Preferably, step 4 includes:

[0030] Based on the dispersion curve of phase velocity and and The intersection points yield the mode order index confined in the Beaufort waveguide. and :

[0031]

[0032] .

[0033] Preferably, the modes extracted in step 5 are confined to the Beaufort waveguide. for:

[0034]

[0035] in, , The modal depth function matrix, This refers to the frequency domain data from step 2.

[0036] Preferably, the distance estimation operator for ice layer and seabed parameters constructed in step 6 is tolerant. for:

[0037]

[0038] in, and It is a mode order index restricted in Beaufort waveguides. The mode index for the constraint extracted from the data received from the vertical array in step 5 is: The modality;

[0039] When the search distance matches the actual target distance, A peak will appear, and the target distance estimate can be obtained by searching for the peak.

[0040] On the other hand, this invention proposes a robust distance estimation system based on normal mode-constrained modes in dual-channel audio in the Arctic ice region, implemented using the above method. The system includes:

[0041] The normal mode model calculation module is used to simulate and predict the frequency domain sound pressure, phase velocity, group velocity, and modal covariance matrix of the experimental sea area in the Arctic ice zone using the normal mode model.

[0042] The Fourier transform module is used to transform the time-domain signal received by the vertical array in the ice-covered sea area into frequency-domain data using fast Fourier transform.

[0043] The sound velocity profile calculation module is used to obtain the sound velocity and depth corresponding to the upper boundary, channel axis and lower boundary of the Beaufort waveguide based on the dual-channel sound velocity profile of the test sea area.

[0044] The order index acquisition module is used to obtain the order index of the Beaufort waveguide mode based on the intersection of the phase velocity dispersion curve with the upper boundary of the Beaufort waveguide and the sound velocity of the channel axis.

[0045] The mode separation module is used to perform mode separation using matched filtering mode filtering methods to extract modes confined in the Beaufort waveguide;

[0046] The distance estimation module is used to construct a tolerance-based distance estimation operator for ice and seabed based on the extracted modes in the Beaufort waveguide, thereby achieving tolerance-based estimation of underwater target distances.

[0047] Compared with existing technologies, the advantages of this invention are:

[0048] This invention, based on the dispersion characteristics of a dual-channel waveguide, extracts the initial order index of the normal modes confined within the Beaufort waveguide. Through mode separation, it performs mode separation on the measured data of the vertical array. A range estimation operator is constructed based on the modes confined within the Beaufort waveguide, overcoming the environmental mismatch problem caused by uncertainties in sea ice and seabed parameters, and achieving ice-tolerant range estimation. Compared with traditional matched field and matched mode methods, it has lower computational cost, higher robustness and environmental tolerance, avoids the environmental mismatch problem caused by uncertainties in sea ice and seabed parameters, and is easily applicable to practical sonar platforms. Attached Figure Description

[0049] Figure 1 The flowchart shown is a permissive distance estimation method based on normal mode confinement in dual-channel audio in the Arctic ice region according to the present invention.

[0050] Figure 2 The image shows the measured dual-channel sound velocity structure in an ice-covered sea area.

[0051] Figure 3 The diagram shows the vertical array placement.

[0052] Figure 4 The figure shows the depth function distribution of the 2nd, 10th, 60th and 80th normal modes;

[0053] Figure 5 The figure shows the dispersion curves of phase velocity and group velocity;

[0054] Figure 6 The figure shows the modal covariance matrix of the vertical array;

[0055] Figure 7 The figure shows the intersection of the phase velocity and the characteristic sound velocity of the Beaufort waveguide;

[0056] Figure 8 The figures show the measured propagation loss of the vertical array and the propagation loss calculated using the Beaufort waveguide confined modes.

[0057] Figure 9 The distance estimate for 6.4 km is shown below;

[0058] Figure 10 The figure shows a comparison curve of the estimated distance of 4-8.5km and the actual distance measured by GPS. Detailed Implementation

[0059] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings.

[0060] This invention proposes a tolerant distance estimation method and system based on normal mode constraint modes in dual-channel Arctic ice regions to solve the environmental mismatch problem of distance estimation caused by uncertainties in sea ice and seabed in Arctic ice regions. It can be used for tolerant estimation of underwater target distances in dual-channel sea areas such as the Chukchi Plateau and the Beaufort Sea in the Arctic.

[0061] The applicable scope of this method / system is as follows: the sound velocity structure is a dual-channel sound velocity structure, the sea ice concentration range is 0~1, the sea depth range is 300~6000m; the horizontal distance between the target sound source and the vertical linear array ranges from 0 to 200km; and the depth range between the target sound source and the vertical array ranges from 50 to 300m.

[0062] Example 1

[0063] like Figure 1As shown, Embodiment 1 of the present invention proposes a tolerance distance estimation method based on normal mode-constrained modes in dual-channel audio in Arctic ice regions, comprising:

[0064] Step 1) Using a normal mode model, simulate and predict the normal mode depth function, phase velocity and group velocity dispersion curves, and modal covariance matrix of the experimental sea area; and the distance to the sound source. meters, receiving depth The frequency domain sound pressure level of a meter can be expressed as

[0065]

[0066] in, Represents data received in the frequency domain; Discrete angular frequencies; Represents the imaginary unit; Represents the modal depth function; represent The horizontal wavenumber of the first mode; Represents a modal index; This represents the total modal order; Represents the depth of the target; This represents the density of seawater at the target depth. First mode phase velocity Group speed It can be represented as:

[0067]

[0068]

[0069] Modal covariance matrix It can be represented as:

[0070]

[0071]

[0072] in, Representing different vertical arrays The depth of each array element; the depth of the vertical array needs to cover the typical depth range of 40~200m for Beaufort waveguides. This represents the modal depth function of the Nth element. is the modal depth function matrix.

[0073] Step 2) Through Fast Fourier Transform, the frequency domain received data of the vertical array can be represented as:

[0074]

[0075]

[0076] in, This represents the complex sound pressure vector of the vertical receiving array. Indicates the number of elements in the vertical array; Represents the modal amplitude function vector; superscript H This indicates the conjugate transpose.

[0077] Step 3) Based on the sound speed structure of the test sea area, the upper boundary has a positive gradient of shallow sound speed and a negative gradient of deep sound speed, with the boundary being the maximum sound speed. Thus, the upper boundary depth of the Beaufort waveguide is obtained. and speed of sound , channel axis depth and speed of sound and lower boundary depth and speed of sound ;

[0078] Step 4) Based on the dispersion curve of the phase velocity and and The intersection points yield the mode order index confined in the Beaufort waveguide. and :

[0079]

[0080]

[0081] Step 5) Use the matched filter mode separation method to extract the amplitudes of the normal modes that can be resolved from the experimental data, expressed as:

[0082]

[0083] in, This represents the estimation result of the modal amplitude function; the modal depth function matrix. Calculations were performed using a normal mode wave model.

[0084] Step 6) From the amplitude of the normal mode We select modes restricted to the Beaufort waveguide to construct distance estimation operators that are tolerant to ice and seabed parameters:

[0085]

[0086] in, and It is the mode order index restricted in the Beaufort waveguide. When the search distance matches the actual target distance, A peak will appear. The target distance estimate can be obtained by searching for the peak.

[0087] Example 2

[0088] Figure 2 This exhibit showcases the dual-channel sound velocity structure measured in the Canadian Basin during a Chinese Arctic scientific expedition. The test location was at a depth of 792m, where submarine mountains were present. The sea ice concentration was 70%, with an average thickness of 2m and uneven surface roughness. Figure 3 The deployment position of the vertical array during the experiment is shown, with 15 array elements, an element spacing of 10m, and a water depth coverage of 40-180m. The target frequency is 700Hz. The tolerance range estimation was performed using the following steps:

[0089] Step 1: Using the Kraken model of normal modes, Figure 2 The acoustic structure and depth information of the test sea area shown are used as model inputs to simulate and predict the normal mode depth function of ice-free, flat-bottomed sea areas. Horizontal wavenumber Phase velocity Group speed and modal covariance matrix The distributions of the 2nd, 10th, 60th, and 80th modal depth functions obtained at 700Hz are as follows: Figure 4 As shown, these correspond to (a), (b), (c), and (d) respectively. It can be seen that the 10th mode is completely confined within the Beaufort waveguide and does not interact with sea ice or the seabed. Figure 5 The figure shows the dispersion curves of phase velocity and group velocity. Figure 6 The diagram shows the modal covariance matrix. It can be seen that the vertical matrix used in the experiment can resolve [variables / variances]. The modality.

[0090] Step 2: Convert the time-domain data of each depth element of the vertical receiving array. , Sampling rate The data is 10kHz in frequency and 12s in length. A Fourier transform is performed to convert it into frequency domain data. Select the 700Hz frequency band to generate the sound pressure frequency domain matrix of the measurement field. .

[0091] Step 3: According to Figure 2 The experimental sea area acoustic structure is shown, and the upper and lower boundaries and acoustic channel axis of the Beaufort waveguide are obtained, as follows. Figure 2 As shown by the red dot in the image. It is 1447.52 m / s. It is 1441.6 m / s. It is 114m. and The values ​​are 29m and 224m.

[0092] Step 4: According to Figure 7 The 700Hz phase velocity curve shown and (Blue dashed line) and The intersection of the (red wires), i.e., the modes between 1441.6 and 1447.52 m / s, yields the mode order index confined in the Beaufort waveguide, where... , ;

[0093] Step 5: Calculate the modal depth function obtained from the KRAKEN model. Constructing a matrix By employing a matched-filter mode separation method, the amplitudes of the normal modes that can be resolved from the measured data of the vertical array are extracted. .

[0094] Step 6: From the amplitude of the normal mode Select the mode restricted to the Beaufort waveguide and set... and Construct a distance estimation operator with tolerance for ice layer and seabed parameters:

[0095]

[0096] When the search distance matches the actual distance to the target sound source There exists a local maximum, and the distance estimate can be obtained through peak search. Figure 8 The upper figure shows the measured propagation loss of the vertical array, and the lower figure shows the propagation loss calculated by the Beaufort waveguide mode. It can be seen that the Beaufort waveguide accounts for a larger proportion of the energy in the acoustic convergence region. Figure 9 The distance is 6.4km. curve, Figure 10 The figure shows a comparison between the tolerance distance estimation of 4-8.5 km and the GPS measurement results. The maximum estimation error is less than 2.8%, which proves the tolerance of the proposed method to the environmental mismatch of sea ice and seabed parameters.

[0097] Example 3

[0098] This invention also provides a robust distance estimation system based on normal mode-constrained modes in dual-channel audio in Arctic ice regions, implemented using the above method. The system includes:

[0099] The normal mode model calculation module is used to simulate and predict the normal mode depth function, phase velocity and group velocity dispersion curves, and modal covariance matrix of the experimental sea area using the normal mode model.

[0100] The Fourier transform module is used to transform the time-domain signal received by the vertical array in the ice-covered sea area to the frequency domain to obtain the frequency domain data of the array received signal.

[0101] The sound velocity profile calculation module is used to obtain the sound velocity and depth corresponding to the upper boundary, channel axis and lower boundary of the Beaufort waveguide through the dual-channel sound velocity profile of the test sea area;

[0102] The order index acquisition module is used to obtain the order index of the Beaufort waveguide mode based on the intersection of the phase velocity dispersion curve with the upper boundary of the Beaufort waveguide and the sound velocity of the channel axis.

[0103] The mode separation module is used to perform mode separation using matched filtering mode filtering methods to extract modes confined in the Beaufort waveguide;

[0104] The distance estimation module is used to construct a tolerance-based distance estimation algorithm for ice and seabed based on the extracted modes in the Beaufort waveguide, thereby achieving tolerance-based estimation of underwater target distances.

[0105] It is worth noting that in the embodiments of the above system, the modules included are divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional module are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0106] Example 4

[0107] The present invention may also provide a computer device, comprising: at least one processor, a memory, at least one network interface, and a user interface. The various components of the device are coupled together via a bus system. It is understood that the bus system is used to enable communication between these components. In addition to a data bus, the bus system also includes a power bus, a control bus, and a status signal bus.

[0108] The user interface can include a display, keyboard, or clicking device. Examples include a mouse, trackball, touchpad, or touchscreen.

[0109] It is understood that the memory in the embodiments disclosed in this invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be 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 DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memories described herein are intended to include, but are not limited to, these and any other suitable types of memory.

[0110] In some implementations, the memory stores elements such as executable modules or data structures, or subsets thereof, or extended sets thereof: operating systems and applications.

[0111] The operating system includes various system programs, such as the framework layer, core library layer, and driver layer, used to implement various basic business functions and handle hardware-based tasks. The application programs include various applications, such as media players and browsers, used to implement various application functions. Programs implementing the methods of the embodiments of this disclosure can be included in the application programs.

[0112] In the above embodiments, the processor can also invoke programs or instructions stored in memory, specifically programs or instructions stored in an application program, for the following purposes:

[0113] Follow the steps described above.

[0114] The above methods can be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above methods can be completed by integrated logic circuits in the processor's hardware or by software instructions. 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 devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the disclosed methods, steps, and logic block diagrams. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the disclosed methods can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above methods.

[0115] It is understood that the embodiments described in this invention can be implemented in 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 this invention, or combinations thereof.

[0116] For software implementation, the technology of this invention can be achieved by executing the functional modules (e.g., procedures, functions, etc.) of this invention. The software code can be stored in memory and executed by a processor. The memory can be implemented in the processor or externally.

[0117] Example 5

[0118] The present invention may also provide a non-volatile storage medium for storing a computer program. When the computer program is executed by a processor, it can implement the steps in the above method embodiments.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the embodiments, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of the present invention do not depart from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for estimating the tolerant distance of a dual-channel underwater target in the Arctic ice region based on the normal mode limited mode, the method comprising: Step 1: using a normal mode model to simulate the frequency domain sound pressure, phase velocity, group velocity and mode covariance matrix of the test sea area in the Arctic ice region; Step 2: using fast Fourier transform to transform the time domain signal received by the vertical array in the ice region sea area into frequency domain data; Step 3: obtaining the sound speed and depth corresponding to the upper boundary, channel axis and lower boundary of the Baffin waveguide according to the dual-channel sound speed profile of the test sea area; Step 4: obtaining the order index of the Baffin waveguide mode according to the intersection of the phase velocity dispersion curve and the sound speed of the upper boundary and channel axis of the Baffin waveguide; Step 5: using a matched filter mode filtering method to separate the modes and extract the modes limited in the Baffin waveguide; Step 6: based on the extracted modes in the Baffin waveguide, constructing an ice layer and seabed tolerant distance estimation algorithm to realize the tolerant estimation of the distance of the underwater target.

2. The polar-ice-region two-channel down normal-mode-based permissive-distance estimation method of claim 1, wherein, The step 1 frequency domain sound pressure Is: ； where, denotes the distance to the sound source in meters, denotes the receiving depth in meters is the discrete angular frequency; denotes the imaginary unit; denotes the modal depth function; denotes is the horizontal wave number of the m-th mode; denotes the mode index; is the total number of modes; denotes the depth at which the target is located; denotes the seawater density at the target depth; modal phase velocity and group velocity is: ； ； wherein is the angular frequency of the simulation; Modal covariance matrix is: ； ； where the superscript H denotes the conjugate transpose, represents different vertical arrays of M elements; denotes the modal depth function of the Nth element, is the modal depth function matrix.

3. The polar-ice-region two-channel down normal-mode-based tolerant-distance estimation method of claim 2, wherein, The frequency domain data of step 2 Is: ； ； wherein represents the vertical array complex pressure vector, represents the number of vertical array elements; represents the modal amplitude function vector; the superscript H represents the conjugate transpose.

4. The polar-ice-region two-channel down normal-mode-based permissive-distance estimation method of claim 3, wherein, The step 3 comprises: According to the sound velocity profile of the test sea area, the upper boundary has a positive gradient of shallow sound velocity and a negative gradient of deep sound velocity, and the boundary has a maximum sound velocity , so as to obtain the depth of the upper boundary of the Baffin Bay waveguide and the corresponding sound velocity , the depth of the sound channel axis and the sound velocity , and the depth of the lower boundary and the corresponding sound velocity .

5. The polar-ice-region two-channel down normal-mode-based tolerant-distance estimation method of claim 4, wherein, The step 4 comprises: According to the dispersion curve of phase velocity With And The intersection of And : ； 。 6. The polar-ice-region two-channel down normal-mode-based permissive-distance estimation method of claim 5, wherein, The step 5 extraction limits the modes in the waveguide is: ； wherein, , is the modal depth function matrix, is the frequency domain data of step 2.

7. The polar-ice-region two-channel down normal-mode-based permissive-distance estimation method of claim 6, wherein, The step 6 constructs an ice layer and seafloor parameter tolerant distance estimator Is: ； wherein and is the mode order index of the mode confined in the waveguide, is the mode order index of the mode confined in the waveguide, is the mode order index of the mode confined in the waveguide, When the searched distance and the target true distance are consistent, A peak will appear, and the target distance estimate can be obtained by searching the peak.

8. A polar ice zone dual-channel downgoing normal mode based tolerant range estimation system, implemented based on the method of any one of claims 1-7, characterized in that, The system comprises: a normal mode model calculation module for using a normal mode model to simulate the frequency domain sound pressure, phase velocity, group velocity and mode covariance matrix of the test sea area in the Arctic ice region; a Fourier transform module for using fast Fourier transform to transform the time domain signal received by the vertical array in the ice region sea area into frequency domain data; a sound speed profile calculation module for obtaining the sound speed and depth corresponding to the upper boundary, channel axis and lower boundary of the Baffin waveguide according to the dual-channel sound speed profile of the test sea area; an order index acquisition module for obtaining the order index of the Baffin waveguide mode according to the intersection of the phase velocity dispersion curve and the sound speed of the upper boundary and channel axis of the Baffin waveguide; a mode separation module for using a matched filter mode filtering method to separate the modes and extract the modes limited in the Baffin waveguide; and a distance estimation module for constructing an ice layer and seabed tolerant distance estimation algorithm based on the extracted modes in the Baffin waveguide to realize the tolerant estimation of the distance of the underwater target.

9. A computer device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program comprises instructions that, when executed by the processor, cause the processor to perform the method of any one of claims 1-8. The processor executes the computer program to realize the steps of the method of any one of claims 1-7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the method of any one of claims 1-7.

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