An Unmanned Underwater Vehicle Positioning Method and System in a Shallow Sea Non-uniform Medium

Abstract

The present invention relates to a positioning method and system for an unmanned underwater vehicle in a shallow sea non-uniform medium. The method includes: obtaining samples of the sound velocity of the shallow sea environment in the shallow sea area to be positioned; obtaining the sound pressure of the unmanned underwater vehicle signal; constructing a non-uniform medium sound propagation model containing random variables in the sound velocity according to the samples of the sound velocity; simplifying the random variables through Karhunen-Loève expansion to obtain a simplified non-uniform medium sound propagation model; obtaining the numerical solution of the Green's function through the simplified non-uniform medium sound propagation model; substituting the numerical solution of the Green's function and the sound pressure of the unmanned underwater vehicle signal into the iterative Bayesian focusing algorithm for calculation to obtain the Bayesian posterior mean, that is, the estimated position of the unmanned underwater vehicle. Compared with the prior art, the present invention can achieve high-precision positioning of the unmanned underwater vehicle under random complex interference in the shallow sea.

Description

Technical Field

[0001] The present invention relates to the field of shallow - sea positioning, and particularly to a positioning method and system for an unmanned underwater vehicle in a shallow - sea non - uniform medium. Background Art

[0002] Near - shore surveillance has always been one of the important research fields of hydroacoustics. Since the 21st century, with the increasing maturity of unmanned and intelligent technologies, various unmanned autonomous platforms have been put into use. Underwater, UUV (Unmanned Underwater Vehicle) can adapt to a variety of application scenarios, and many models have been developed according to its functional tasks. In the early days, UUVs were mainly applied in civilian fields, performing tasks such as deep - sea exploration, cable laying, and shipwreck salvage. As this technology has gradually matured, its huge value in the military field has attracted the attention of navies around the world. In 1957, the Applied Physics Laboratory of the University of Washington in the United States developed an underwater vehicle used to study submarine wakes and acoustic wave diffusion, which is considered the world's first military UUV. After more than 60 years of development, the uses of military UUVs have expanded to intelligence surveillance and reconnaissance, anti - mine warfare, payload delivery, anti - submarine warfare, maritime search and rescue, communication relay, military deception and interference, etc., becoming a force that cannot be ignored in the field of marine military.

[0003] The problem of detecting high - speed small non - cooperative underwater targets belongs to the cross - disciplinary fields of hydrodynamics, hydroacoustics, information theory, and array signal processing, concentrating on the difficulties of "non - Gaussian background noise, non - linear sound - source model, non - stationary sound - source signal, and incomplete information measured by finite array elements". Due to the adverse factors of low signal - to - noise ratio, few observation samples, strong non - stationarity of the signal, and non - cooperative targets of the radiation noise source signal of high - speed UUVs, the feature extraction and detection and recognition of underwater small target signals with UUVs as the target objects have become a practical need and a potential technology hot - spot. Summary of the Invention

[0004] The purpose of the present invention is to overcome the above - mentioned defects existing in the prior art and provide a positioning method for an unmanned underwater vehicle in a shallow - sea non - uniform medium.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A positioning method for an unmanned underwater vehicle in a shallow - sea non - uniform medium includes the following steps:

[0007] S1. Obtain samples of the shallow - sea environmental sound speed in the shallow - sea area to be positioned;

[0008] S2. Obtain the sound pressure of the unmanned underwater vehicle signal;

[0009] S3. Construct a sound propagation model for a non-uniform medium with a random variable in the sound speed based on the samples of the sound speed;

[0010] S4. Simplify the random variable through Karhunen-Loève expansion to obtain a simplified sound propagation model for the non-uniform medium;

[0011] S5. Obtain the numerical solution of the Green's function through the simplified sound propagation model for the non-uniform medium;

[0012] S6. Substitute the numerical solution of the Green's function and the sound pressure of the unmanned underwater vehicle signal into the iterative Bayesian focusing algorithm for calculation to obtain the Bayesian posterior mean, that is, the estimated position of the unmanned underwater vehicle.

[0013] Further, the expression of the sound propagation model for the non-uniform medium with a random variable is:

[0014]

[0015] where r s and r represent positions, v(r, ξ r ) represents the sound speed at position r, ξ r represents a random variable, t represents time, δ represents the Dirac function, Ω(t) represents the sound source signal function in the time domain, represents the Laplace operator, and y(r, t) represents the sound pressure at position r at time t.

[0016] Further, the expression of the random variable in the simplified sound propagation model for the non-uniform medium is:

[0017]

[0018] where represents the sample mean of the sound speed in the non-uniform medium sound field, λ n and f n (r) represent the eigenvalue and eigenfunction respectively, is the random variable of the simplified sound speed and is considered a Gaussian random sound field, represents a series of uncorrelated random variables and is an independent standard Gaussian random variable.

[0019] Further, the samples of the sound speed in the shallow sea environment are obtained through sensors arranged in the shallow sea.

[0020] An unmanned underwater vehicle positioning system in a shallow sea non-uniform medium, including a processor and a memory, the processor calls a program in the memory to execute the following steps:

[0021] S1. Obtain samples of the sound speed in the shallow sea environment of the shallow sea area to be positioned;

[0022] S2. Obtain the sound pressure of the unmanned underwater vehicle signal;

[0023] S3. Construct a sound propagation model of a non-uniform medium with a random variable in the sound speed according to the samples of the sound speed;

[0024] S4. Simplify the random variable through Karhunen-Loève expansion to obtain a simplified sound propagation model of a non-uniform medium;

[0025] S5. Obtain the numerical solution of the Green's function through the simplified sound propagation model of a non-uniform medium;

[0026] S6. Substitute the numerical solution of the Green's function and the sound pressure of the unmanned underwater vehicle signal into the iterative Bayesian focusing algorithm for calculation to obtain the Bayesian posterior mean, that is, the estimated position of the unmanned underwater vehicle.

[0027] Furthermore, the expression of the sound propagation model of a non-uniform medium with a random variable is:

[0028]

[0029] where r s and r represent positions, v(r, ξ r ) represents the sound speed at position r, ξ r represents a random variable, t represents time, δ represents the Dirac function, Ω(t) represents the sound source signal function in the time domain, represents the Laplace operator, and y(r, t) represents the sound pressure at position r at time t.

[0030] Furthermore, the expression of the random variable in the simplified sound propagation model of a non-uniform medium is:

[0031]

[0032] where represents the sample mean of the sound speed in the non-uniform medium sound field, λ n and f n (r) represent the eigenvalue and eigenfunction respectively, is the random variable of the simplified sound speed and is considered a Gaussian random sound field, represents a series of uncorrelated random variables and is an independent standard Gaussian random variable.

[0033] Furthermore, the samples of the sound speed in the shallow sea environment are obtained through sensors arranged in the shallow sea.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention simulates the sound propagation process based on a non-uniform sound propagation medium model, which is very close to the actual sound propagation process. At the same time, the non-uniform propagation model is simplified by Karhunen-Loève expansion, greatly reducing the computational complexity. In addition, the iterative Bayesian focusing algorithm used in the present invention obtains sparse sound source coefficients through the joint estimation of the aperture function and the prior during the iterative process, thereby achieving high-precision positioning of the unmanned underwater vehicle under random complex interference in the shallow sea. Description of the Drawings

[0036] Figure 1 It is a schematic flow chart of the present invention.

[0037] Figure 2 It is a schematic flow chart of the iterative Bayesian focusing algorithm. Detailed Embodiment

[0038] The present invention will be described in detail below with reference to the drawings and specific embodiments. This embodiment is implemented on the premise of the technical solution of the present invention, and the detailed implementation manners and specific operation processes are given, but the protection scope of the present invention is not limited to the following embodiments.

[0039] As Figure 1 shown, this embodiment provides a method for positioning an unmanned underwater vehicle in a shallow sea non-uniform medium, including the following steps:

[0040] Step S1: Obtain samples of the sound speed of the shallow sea environment in the shallow sea area to be positioned;

[0041] Step S2: Obtain the sound pressure of the unmanned underwater vehicle signal;

[0042] Step S3: Construct a non-uniform medium sound propagation model containing random variables in the sound speed according to the samples of the sound speed;

[0043] Step S4: Simplify the random variables through Karhunen-Loève expansion to obtain a simplified non-uniform medium sound propagation model;

[0044] Step S5: Obtain the numerical solution of the Green's function through the simplified non-uniform medium sound propagation model.

[0045] Step S6: Substitute the numerical solution of the Green's function and the sound pressure of the unmanned underwater vehicle signal into the iterative Bayesian focusing algorithm for calculation to obtain the Bayesian posterior mean, that is, the estimated position of the unmanned underwater vehicle.

[0046] The following is the specific expansion of the positioning method:

[0047] Step S1 is the acquisition of samples of the sound speed of the shallow sea environment in the shallow sea area. Sensors are arranged in the shallow sea area to be positioned to measure the samples of the seawater sound speed.

[0048] Step S2 is to obtain the signal sound pressure of the AUV. An underwater microphone array and a data analysis center are arranged in the shallow sea area to be located. The underwater microphone array is equipped with sensors that can measure the sound pressure of the signal, and each element transmits the data back to the data analysis center. The signal collected by the underwater microphone array is a time-domain signal, which needs to be converted to the frequency domain for calculation in the positioning step. The classical Hanning window function is selected to truncate the time-domain signal, and then the truncated time-domain signal is converted to a frequency-domain signal through the fast Fourier transform. Each segment is a snapshot. Thus, the observation data p of the underwater microphone can be obtained.

[0049] Step S3 is to construct a non-uniform medium acoustic propagation model with random variables according to the sample of the shallow sea environmental sound speed. It includes:

[0050] Step S31: Construct the wave equation of the sound pressure in the non-uniform medium:

[0051]

[0052] where the non-uniform medium sound field is generated by a sound source located at r s , r s is a position in the region Γ, v(r, ξ r ) represents the sound speed at the position r and is a function with a random variable ξ r . y(r, t) represents the sound pressure at the position r at time t. δ represents the Dirac function, and Ω(t) represents the sound source signal function in the time domain.

[0053] Step S32: Apply the Fourier transform to both sides of the wave equation in Step S31, and the Helmholtz equation of the sound field in the frequency domain can be obtained:

[0054]

[0055] where k(r, ξ r ) is the wave number, and k(r, ξ r ) = (2πf) / [v(r, ξ r )], and Ω(f) is the Fourier transform of Ω(t).

[0056] Steps S4 and S5 are to simplify the random variables through the Karhunen-Loève expansion to simplify the non-uniform medium acoustic propagation model and obtain the numerical solution H of the Green's function. Specifically:

[0057] First, the sound speed of the non-uniform medium sound field can be expressed as:

[0058]

[0059] where represents the sample mean of the sound speed in a non-uniform medium sound field, λ n (λ 1 ≥ λ 2 ≥...) and f n (r) represent the eigenvalues and eigenfunctions of the covariance function C(r 1 , r 2 ), respectively, represents a series of uncorrelated random variables and is an independent standard Gaussian random variable, ξ r is considered a Gaussian random sound field.

[0060] Then, using the Karhunen-Loève expansion, a simplified form of the random sound field can be obtained:

[0061]

[0062] Since most of the eigenvalues in the non-uniform random sound field are very small, this means that the influence of small eigenvalues in the non-uniform medium sound field is relatively weak. By setting the global mean square error in the Karhunen-Loève expansion to remove the terms with small eigenvalues, the global mean square error is calculated as:

[0063]

[0064] where σ 2 (r) represents the local variance of the random field. The number of terms N in the formula can be obtained by making the global mean square error less than a given threshold.

[0065] Finally, substituting the simplified back into the Helmholtz equation in step S32, the numerical solution H of the Green's function can be obtained.

[0066] Step S6 is to substitute the observed data p of the hydrophone and the numerical solution H of the Green's function into the iterative Bayesian focusing algorithm for calculation to obtain the posterior mean of the source coefficient c, from which the estimated position of the target signal can be obtained. The specific process is as Figure 2 shown:

[0067] Step S61: Design an initial aperture function where

[0068] The prior distribution of the source coefficient can be obtained from where

[0069] Step S62: Set k = 0. From and the Bayesian framework, α 2 and the posterior mean where β 2is the noise variance;

[0070] Step S63. Repeat steps S64 to S67 until convergence;

[0071] Step S64. Dok = k + 1

[0072] Step S65. Estimate the relative intensity as Set where 0 < ε 1 << 1;

[0073] Step S66. Update the aperture function to and construct a diagonal matrix whose j-th diagonal element is

[0074] Step S67. Obtain a new prior distribution from the updated aperture function and relative intensity, and thus calculate a new posterior mean

[0075] Step S68. Convergence criterion: When is less than a given threshold ε, 0 < ε < 1, stop the iteration;

[0076] Step S69. Use the posterior mean as an estimate of the target signal position.

[0077] In this step, through the joint estimation of the aperture function and the prior, the sound source distribution is gradually focused on the region of interest during the iteration process, promoting the sparsity of the sound source coefficients, and finally estimating the position of the sound source.

[0078] The preferred specific embodiments of the present invention have been described in detail above. It should be understood that those of ordinary skill in the art can make many modifications and variations based on the concept of the present invention without creative efforts. Therefore, all technical solutions that can be obtained by those skilled in the art in the technical field according to the concept of the present invention through logical analysis, reasoning, or limited experiments on the basis of the prior art should fall within the protection scope determined by the claims.

Claims

1. An unmanned underwater vehicle positioning method in a shallow sea non-uniform medium, characterized in that, it includes the following steps: S1. Obtain samples of the sound speed of the shallow sea environment in the shallow sea area to be positioned; S2. Obtain the sound pressure of the unmanned underwater vehicle signal; S3. Construct a non-uniform medium sound propagation model containing random variables in the sound speed according to the sound speed samples; S4. Simplify the random variables through Karhunen-Loève expansion to obtain a simplified non-uniform medium sound propagation model; S5. Obtain the numerical solution of the Green's function through the simplified non-uniform medium sound propagation model; S6. Substitute the numerical solution of the Green's function and the sound pressure of the unmanned underwater vehicle signal into the iterative Bayesian focusing algorithm for calculation to obtain the Bayesian posterior mean, that is, the estimated position of the unmanned underwater vehicle; wherein, the expression of the non-uniform medium sound propagation model containing random variables is: where r s and r represent positions, v(r, ξ r ) represents the speed of sound at position r, ξ r represents a random variable, t represents time, δ represents the Dirac function, Ω(t) represents the sound source signal function in the time domain, represents the Laplace operator, and y(r, t) represents the sound pressure at position r at time t; the expression of the random variable in the simplified non-uniform medium sound propagation model is: Among them, represents the sample mean of the sound speed in the inhomogeneous medium sound field, λ n and f n (r) represent the eigenvalue and the eigenfunction respectively, is the random variable of the simplified sound speed and is considered a Gaussian random sound field, represents a series of uncorrelated random variables and is an independent standard Gaussian random variable.

2. The unmanned underwater vehicle positioning method in a shallow sea non-uniform medium according to claim 1, characterized in that, the samples of the sound speed of the shallow sea environment are obtained through sensors arranged in the shallow sea.

3. An unmanned underwater vehicle positioning system in a shallow sea non-uniform medium, characterized in that, it includes a processor and a memory, and the processor calls the program in the memory to execute the following steps: S1. Obtain samples of the sound speed of the shallow sea environment in the shallow sea area to be positioned; S2. Obtain the sound pressure of the unmanned underwater vehicle signal; S3. Construct a non-uniform medium sound propagation model containing random variables in the sound speed according to the sound speed samples; S4. Simplify the random variables through Karhunen-Loève expansion to obtain a simplified non-uniform medium sound propagation model; S5. Obtain the numerical solution of the Green's function through the simplified non-uniform medium sound propagation model; S6. Substitute the numerical solution of the Green's function and the sound pressure of the unmanned underwater vehicle signal into the iterative Bayesian focusing algorithm for calculation to obtain the Bayesian posterior mean, that is, the estimated position of the unmanned underwater vehicle; the expression of the non-uniform medium sound propagation model containing random variables is: where r s and r represent positions, v(r, ξ r ) represents the sound speed at position r, ξ r represents a random variable, t represents time, δ represents the Dirac function, Ω(t) represents the sound source signal function in the time domain, represents the Laplace operator, and y(r, t) represents the sound pressure at position r at time t; the expression of the random variable in the simplified non-uniform medium sound propagation model is: Among them, represents the sample mean of the sound speed in the inhomogeneous medium sound field, λ n and f n (r) represent the eigenvalue and the eigenfunction respectively, is the random variable of the simplified sound speed and is considered a Gaussian random sound field, represents a series of uncorrelated random variables and is an independent standard Gaussian random variable.

4. The unmanned underwater vehicle positioning system in a shallow sea non-uniform medium according to claim 3, characterized in that, the samples of the sound speed of the shallow sea environment are obtained through sensors arranged in the shallow sea.

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