A hydrophone array multi-sound source positioning method based on frost ice optimization algorithm

Abstract

The application discloses a hydrophone array multi-sound source positioning method based on a frost and ice optimization algorithm and belongs to the field of sound source positioning. The method constructs a linear hydrophone array, sets an underwater sound source positioning scene, collects sound source signals and processes the sound source signals to obtain received signals; the received signals are processed by using a MUSIC algorithm, a spatial spectrum is generated to preliminarily estimate the azimuth and the pitch angle and other information of the sound source, the spatial spectrum is normalized, a gravity region and a repulsion region are divided, and a gravity-repulsion type artificial potential field is constructed; a frost and ice RIME optimization algorithm is adopted, an independent sub-population is initialized for each sound source, the position of an individual is updated in combination with the artificial potential field and a frost piercing mechanism in an iterative updating process, and through iterative search, a particle with the highest fitness is selected as a final estimated position. The application overcomes the limitations of a traditional optimization algorithm, significantly improves the precision and reliability of sound source positioning, and is especially suitable for a multi-sound source positioning scene in a complex underwater acoustic environment.

Description

Technical Field

[0001] This invention belongs to the field of sound source localization, specifically a multi-sound source localization method for hydrophone arrays based on the frost-ice optimization algorithm. Background Technology

[0002] With the continuous development of underwater acoustic signal processing technology and hydrophone array technology, the application of hydrophone arrays in underwater detection fields such as oceans and lakes is becoming increasingly widespread, playing an irreplaceable role, especially in key tasks such as noise detection and sound source localization. In marine resource exploration, hydrophone arrays can accurately capture and locate weak acoustic signals released from oil and gas resources, providing a reliable basis for resource development. In environmental monitoring, they can monitor marine biological activity and background noise changes in real time, contributing to the protection and assessment of the marine ecological environment. In national defense and security, the localization of underwater target sound sources is a crucial technical support for ensuring national maritime security. However, the underwater acoustic environment is complex and variable, with numerous interference factors such as biological and mechanical noise, posing a severe challenge to sound source localization algorithms based on hydrophone arrays. Against this backdrop, improving the robustness, accuracy, and convergence efficiency of algorithms has become a key research focus in the field of underwater sound source localization.

[0003] Traditional particle swarm optimization (PSO) algorithms are widely used in sound source localization problems, but they also have many limitations: their global search capability is weak, and particles are prone to getting trapped in local optima during the search process, making it difficult to escape local extreme regions and thus failing to obtain the global optimal solution; in addition, the algorithm lacks an adaptive parameter adjustment mechanism, and key parameters such as inertia weight remain fixed throughout the optimization process, resulting in slow convergence speed in the early stages and a tendency to miss the optimal solution in the later stages; as the iterations are updated, the population diversity gradually decreases, which can easily lead to premature convergence and reduce the quality of the final solution.

[0004] The Frost Ice RIME optimization algorithm, a newly proposed intelligent optimization method based on natural physical phenomena, is inspired by the growth and diffusion process of frost ice. By constructing a special "hard frost ice" puncture search mechanism, it possesses a certain degree of global search and local fine-grained search capabilities, which can compensate for the shortcomings of traditional algorithms to some extent. However, in the application scenario of underwater sound source localization, where interference is significant, the Frost Ice RIME optimization algorithm still has shortcomings: its initial search direction has strong randomness, making it difficult to quickly focus on the effective search area in complex environments, increasing search blindness and time costs.

[0005] Therefore, it is necessary to propose a multi-source localization method for hydrophone arrays based on the frost-ice optimization algorithm to solve the above-mentioned technical problems in the existing technology. Summary of the Invention

[0006] The purpose of this invention is to provide a multi-source localization method for hydrophone arrays based on the frost-ice optimization algorithm, so as to improve the robustness, accuracy and convergence efficiency of the hydrophone array-based sound source localization algorithm.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A method for multi-source localization of hydrophone arrays based on frost-ice optimization algorithm includes the following steps:

[0009] Step 1: Set up the underwater sound source localization scenario, establish a linear hydrophone array and reference coordinate system, and collect sound source signals. Perform data preprocessing on the collected sound source signals to obtain the received signals.

[0010] Step 2: Define the azimuth search grid and pitch angle search grid Calculate the covariance matrix of the received signal and perform eigenvalue decomposition to obtain eigenvectors and eigenvalues. Sort the eigenvalues ​​in ascending order, extract the eigenvectors corresponding to the small eigenvalues ​​to form the noise subspace, and then calculate the MUSIC spatial spectrum.

[0011] Step 3: Normalize the MUSIC spatial spectrum matrix and set a peak determination threshold to construct a gravity-repulsion type artificial potential energy field.

[0012] Step 4: Set the parameters of the frost and ice optimization algorithm, and initialize an independent subpopulation for each sound source, including variables in three dimensions: azimuth, elevation, and distance.

[0013] Step 5: Calculate the fitness of each individual in each sound source subpopulation and find the optimal individual in each subpopulation; during the iterative update process, update the individual position based on the artificial potential field and frost puncture mechanism, while applying boundary constraints, accepting better solutions and updating the fitness.

[0014] Step 6: After each iteration, calculate the total error of the current iteration and the average error to analyze the convergence of the error. When the iteration convergence condition is met, recalculate the fitness of all particles for each sound source and select the particle with the highest fitness as the final estimated position.

[0015] Compared with the prior art, the present invention has the following advantages:

[0016] (1) By first using the MUSIC algorithm, which has orthogonality analysis of the signal subspace and noise subspace, this invention can effectively estimate the approximate direction of the sound source, providing reliable initial information for subsequent searches. This avoids the blind and inefficient search process caused by the lack of prior estimation of the sound source direction in traditional algorithms.

[0017] (2) The introduction of an attraction-repulsion type artificial potential energy field further enhances the algorithm's search capability. By normalizing the spatial spectrum obtained from the MUSIC algorithm and dividing it into attraction and repulsion regions, particles can be guided more effectively during the search process. When a particle approaches a possible sound source location, it is attracted by gravity; while when it is in an unfavorable region, it is repelled by repulsion. This mechanism enables particles to converge to the sound source location more quickly, greatly improving the efficiency of localization.

[0018] (3) The frost puncture mechanism in the frost-ice RIME optimization algorithm solves the problem that the traditional PSO algorithm is prone to getting trapped in local optima. When the normalized fitness of a particle meets certain conditions, the particle is allowed to jump to the optimal individual, thereby increasing the diversity of particles and giving the algorithm a greater chance to escape local optima and find the global optimal solution, further improving the accuracy of positioning. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below.

[0020] Figure 1 This is a flowchart of a multi-source localization method for hydrophone arrays based on frost-ice optimization algorithm in this invention;

[0021] Figure 2 This is a diagram showing the optimized sound source localization trajectory for frost and ice in this invention.

[0022] Figure 3 This is a graph showing the change in average error during the iteration process of this invention. Detailed Implementation

[0023] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0024] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Example

[0026] like Figure 1 As shown in the figure, this embodiment describes a multi-source localization method for hydrophone arrays based on a frost-ice optimization algorithm. The method includes the following steps:

[0027] Step 1: In the set underwater sound source localization scenario, take the position of the first hydrophone as the reference origin, establish a coordinate system based on the reference origin, and arrange the remaining hydrophones in the coordinate system at equal intervals to form a linear hydrophone array. Use the linear hydrophone array to collect the sound source signal, and then perform data preprocessing on the sound source signal to obtain the received signal.

[0028] First, determine the element spacing d and the number of hydrophones N. sensors Based on the determined element spacing d and the number of hydrophones N sensors Construct a linear hydrophone array and a reference coordinate system. The position of the first hydrophone is used as the origin of the coordinate system, and subsequent hydrophones are arranged at equal intervals d. The position of the nth hydrophone is then defined as x. n = (n-1)d, n = 1, 2, ..., N sensors .

[0029] The sound source signal is sampled multiple times, and the number of snapshots N is set. snapshots =200, to improve signal reliability and stability. For each sound source k, first determine its azimuth angle. and pitch angle Convert to radians, then calculate the cosine components of the spatial direction:

[0030]

[0031] in, Let x represent the cosine component of the direction of sound source k propagation along the x-axis. Let represent the cosine component of the propagation direction of sound source k along the y-axis. This represents the cosine component of the propagation direction of sound source k along the z-axis.

[0032] Simultaneously calculate the delay of the signal propagating from the sound source to the nth hydrophone.

[0033]

[0034] Where, x n It is the position of the nth hydrophone, and c is the speed of sound. The position x of the nth hydrophone n The direction of sound source k corresponds to the cosine component of the propagation direction.

[0035] This generates an analog signal.

[0036]

[0037] in, Let f be the random initial phase and f be the frequency of the signal.

[0038] By superimposing the signals from all sound sources, we obtain the sum s of all sound source signals received by the nth hydrophone. n (t):

[0039]

[0040] Where, N sources Indicates the total number of sound sources;

[0041] Then add Gaussian white noise n n (t) obtains the final received signal r n (t)=s n (t)+n n (t).

[0042] Step 2: Define the azimuth search grid and pitch angle search grid Calculate the covariance matrix of the received signal and perform eigenvalue decomposition to obtain eigenvectors and eigenvalues. Sort the eigenvalues ​​in ascending order and extract the eigenvectors corresponding to the smaller eigenvalues ​​to form the noise subspace. Then calculate the MUSIC spatial spectrum.

[0043] This embodiment defines an azimuth search grid. The range is from 0° to 360°, with a step size of 5°, where i = 1, 2, ..., 72; define the pitch angle search grid. The range is from -30° to 30°, with a step size of 2°, where j = 1, 2, ..., 30.

[0044] According to the final received signal r n (t), calculate the covariance matrix R xx :

[0045]

[0046] in, is the signal vector received by all hydrophones at the t-th sampling time. Where N snapshots The number of times the signal is sampled. They represent the 1st, 2nd, ..., Nth times at time t, respectively. snapshots The received signal of the hydrophone, r H (t) denotes the conjugate transpose of r(t);

[0047] For the covariance matrix R xx Perform eigenvalue decomposition:

[0048] R xx =EΛE H

[0049] Where E is the eigenvector matrix and Λ is the eigenvalue diagonal matrix.

[0050] After sorting the eigenvalues ​​in ascending order, take the last N. sensors The noise subspace E is constructed from the eigenvectors corresponding to M eigenvalues ​​(small eigenvalues, i.e., relatively small eigenvalues). n M represents the number of sound sources.

[0051] For each point in the search grid Calculate the direction vector

[0052]

[0053] in, The coordinates of each hydrophone in the coordinate system are represented by f, where f is the frequency of the signal and c is the speed of sound.

[0054] Then, the MUSIC spatial spectrum is calculated, and spectral peaks are used to estimate the direction of the sound source. The calculation formula is as follows:

[0055]

[0056] in, Represents direction vector The conjugate transpose of . Represents the noise subspace E n The conjugate transpose of .

[0057] Step 3: Normalize the MUSIC spatial spectrum matrix and set a peak determination threshold to construct a gravity-repulsion type artificial potential energy field.

[0058] MUSIC spatial spectrum Normalization is performed to obtain the normalized spectrum. The expression is as follows:

[0059]

[0060] in, This represents the calculated MUSIC spatial spectrum. The maximum value among absolute values.

[0061] A spectral peak determination threshold T = 0.6 was set, and the search space was divided into gravitational and repulsive regions based on the normalized spectrum value, thereby constructing a gravitational-repulsive artificial potential energy field. When this region is defined as a gravitational region, it may contain sound sources and attract particles in the subsequent optimization algorithm, guiding them towards areas where sound sources might exist. This provides clear directional guidance for the particle search, enabling them to more effectively locate sound sources during the search process. When this occurs, the area is divided into a repulsive region, which will exert a repulsive effect on the particles and guide them to move toward the possible sound source location.

[0062] Step 4: Set the parameters of the frost optimization algorithm and initialize an independent subpopulation for each sound source, including variables in three dimensions: azimuth, elevation, and distance.

[0063] Depend on Figure 2 As shown, the population size N and the maximum number of iterations N in the frost optimization algorithm are set. iter =200, position update ratio coefficient β = 0.5, frost puncture intensity parameter E = 0.1, artificial potential field gravitational coefficient α potential =1 and repulsion coefficient β potential =1;

[0064] Initialize a set containing N for each sound source k pop An independent subpopulation of particles, each particle's position determined by its azimuth angle. Pitch angle and distance r ik express.

[0065] Step 5: Calculate the fitness of each individual in each sound source subpopulation and find the optimal individual in each subpopulation; during the iterative update process, update the individual position based on the artificial potential field and frost puncture mechanism, while imposing boundary constraints, accepting better solutions and updating the fitness.

[0066] Design the fitness function F with optimal distance. ik The function is represented as follows:

[0067]

[0068] in, and r ik Let represent the azimuth, elevation, and distance of each particle in the independent subpopulation initialized for each sound source k, respectively. This represents the true azimuth angle of the k-th sound source. r represents the true pitch angle of the k-th sound source. k This represents the actual distance to the k-th sound source. The fitness function F... ik Select the particle with the highest current fitness as the optimal individual. The fitness function evaluates how close the particle's position is to the actual sound source position. The closer the distance, the higher the fitness, indicating that the particle's position is more likely to be the actual sound source position.

[0069] Calculate cosine factor Controls the direction and magnitude of particle position updates; where N iter This represents the maximum number of iterations for the Frost Ice Optimization Algorithm.

[0070] Based on the normalized spectral value at the particle's location Calculate the potential field guiding value U force And update particle positions

[0071]

[0072] Where, p ik Let represent the position of the i-th particle in the population corresponding to the k-th sound source, β represent the position update ratio coefficient, and r1 represent a vector with random properties. The particle explores more extensively in the search space to avoid the algorithm getting stuck in local optima.

[0073] Apply boundary constraints to the updated positions. Calculate the fitness of the updated particles. if The updated position is then accepted; simultaneously, to avoid particles getting trapped in local optima, a frost puncture mechanism is applied. For each particle i, a random number r3 is generated, and the normalized fitness is calculated:

[0074]

[0075] when At this time, particles are allowed to jump towards the optimal individual, explore new search spaces, and update particle positions. Boundary constraints and fitness assessments were performed. Specifically, 10... -5 To prevent the denominator from being 0, which would cause the formula to be incorrect.

[0076] Step 6: After each iteration, calculate the total error of the current iteration and the average error to analyze the convergence of the error. When the convergence condition is met, recalculate the fitness of all particles for each sound source and select the particle with the highest fitness as the final estimated position.

[0077] After each iteration, calculate the total error E of the current iteration. t :

[0078]

[0079] in, Let be the optimal individual position of the k-th sound source. This represents the true azimuth angle of the k-th sound source. r represents the true pitch angle of the k-th sound source. k N represents the actual distance to the k-th sound source. sources This indicates the total number of sound sources.

[0080] Then calculate the average error. And record it for analysis of error convergence.

[0081] Depend on Figure 3 As shown, when the iterative convergence condition is met, for each sound source k, the fitness F of all particles is recalculated. ik The particle with the highest fitness is selected as the final estimated position.

[0082] This embodiment presents a multi-source localization method for hydrophone arrays based on the frost-ice optimization algorithm. First, a linear hydrophone array is constructed, and an underwater sound source localization scenario is set. Sound source signals are collected and processed to obtain the received signals. Then, the MUSIC algorithm is used to process the received signals, generating a spatial spectrum to initially estimate information such as the azimuth and elevation angles of the sound sources. Next, the spatial spectrum is normalized, and based on spectral peak determination thresholds, attractive and repulsive regions are divided to construct an attractive-repulsive artificial potential energy field. The frost-ice RIME optimization algorithm is then employed to initialize an independent subpopulation for each sound source. During iterative updates, the individual positions are updated by combining the artificial potential energy field and the frost penetration mechanism. Through continuous iterative searching until the maximum number of iterations is reached, the final sound source location estimation parameters are output. This method effectively overcomes the problem of traditional PSO algorithms easily getting trapped in local optima, significantly improving the accuracy and reliability of sound source localization, and is particularly suitable for multi-source localization scenarios in complex underwater acoustic environments.

[0083] The embodiments of the present invention are only used to illustrate the technical solutions of the present invention and are not intended to limit it. For those skilled in the art, it will be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for multi-source localization of hydrophone arrays based on frost-ice optimization algorithm, characterized in that, Includes the following steps: Step 1: Set up the underwater sound source localization scenario, establish a linear hydrophone array and reference coordinate system, and collect sound source signals. Perform data preprocessing on the collected sound source signals to obtain the received signals. Step 2: Define the azimuth search grid and pitch angle search grid The covariance matrix of the received signal is calculated and its eigenvalues ​​are decomposed to obtain eigenvectors and eigenvalues. The eigenvalues ​​are sorted in ascending order, and the eigenvectors corresponding to the small eigenvalues ​​are extracted to form the noise subspace. Then the MUSIC spatial spectrum is calculated. Step 3: Normalize the MUSIC spatial spectrum matrix and set a peak determination threshold to construct a gravity-repulsion type artificial potential energy field. Step 4: Set the parameters of the frost and ice optimization algorithm, and initialize an independent subpopulation for each sound source, including variables in three dimensions: azimuth, elevation, and distance. Step 5: Calculate the fitness of each individual in each sound source subpopulation and find the optimal individual in each subpopulation; during the iterative update process, update the individual position based on the artificial potential field and frost puncture mechanism, while applying boundary constraints, accepting better solutions and updating the fitness. The specific process of step 5 is as follows: First, design the fitness function with optimal distance. The function is represented as follows: ； in, , and Each sound source represents The azimuth, pitch, and range of each particle in the initialized independent subpopulation. Indicates the first The true azimuth angle of the sound source. Indicates the first The true pitch angle of the sound source. Indicates the first The actual distance to each sound source; through the fitness function. Select the particle with the highest current fitness as the optimal individual. ; Then calculate the cosine factor. ,in, This represents the maximum number of iterations for the frost optimization algorithm. Based on the normalized spectral value at the particle's location Calculate the potential field guidance value And update particle positions : ； in, Indicates the first In the population corresponding to the sound source, the first The position of each particle. This represents the position update ratio coefficient. Represents a vector with random properties; The gravitational coefficient of the artificial potential energy field. It is the repulsion coefficient; Calculate the fitness of the updated particles. ,if If so, the updated position is accepted; simultaneously, the frost puncture mechanism is applied for each particle. Generate random numbers And calculate the normalized fitness: ； when At that time, particles are allowed to jump to the optimal individual, updating the particle position. And perform boundary constraints and fitness assessment; Step 6: After each iteration, calculate the total error of the current iteration and the average error to analyze the convergence of the error. When the iteration convergence condition is met, recalculate the fitness of all particles for each sound source and select the particle with the highest fitness as the final estimated position.

2. The method for multi-source localization of hydrophone arrays based on frost-ice optimization algorithm according to claim 1, characterized in that, The specific process of step 1 is as follows: First, determine the spacing between array elements. and number of hydrophones A linear hydrophone array and a reference coordinate system were constructed, with the position of the first hydrophone as the origin of the coordinate system, and subsequent hydrophones arranged according to the element spacing. Arranged at equal intervals in sequence, then the... The location of the hydrophone is as follows ,in ; Set the number of snapshots The sound source signal is sampled multiple times, and then the collected sound source signal is preprocessed to obtain the received signal.

3. The method for multi-source localization of hydrophone arrays based on frost-ice optimization algorithm according to claim 1, characterized in that, In step 1, the data preprocessing process is as follows: For each sound source First, determine its azimuth. and pitch angle Convert to radians, then calculate the cosine components of the spatial direction: ； in, Indicates the sound source The cosine component of the propagation direction along the x-axis. Indicates the sound source The cosine component of the propagation direction along the y-axis. Indicates the sound source The cosine component of the propagation direction along the z-axis; Simultaneously calculate the signal propagation from the sound source to the... The delay of a hydrophone : ； in, It is the first The location of the hydrophone It's the speed of sound. It is the first Hydrophone position The sound source corresponding to the direction The cosine component of the propagation direction; This generates an analog signal. : ； in, For random initial phase, The frequency of the signal; The signals from all sound sources are superimposed to obtain the first... The sum of all sound source signals received by each hydrophone : ； in, Indicates the total number of sound sources; Then add Gaussian white noise. Obtain the final received signal .

4. The method for multi-source localization of hydrophone arrays based on frost-ice optimization algorithm according to claim 1, characterized in that, In step 2, the contents of the azimuth search grid and the elevation search grid are defined as follows: Define azimuth search grid The range is To 360°, the step size is ,in ; Define the pitch angle search grid The range is arrive Step size is ,in .

5. The method for multi-source localization of hydrophone arrays based on frost-ice optimization algorithm according to claim 1, characterized in that, In step 2, the specific process of calculating the covariance matrix and performing eigenvalue decomposition is as follows: Calculate the covariance matrix based on the received signal. The calculation formula is as follows: ； in, The number of times the signal is sampled. , , … They represent in The first, second, ..., first The hydrophone receives the signal. express The conjugate transpose of; Then, the covariance matrix... The eigenvalue decomposition is calculated using the following formula: ； in, The eigenvector matrix, It is a diagonal matrix of eigenvalues.

6. The method for multi-source localization of hydrophone arrays based on frost-ice optimization algorithm according to claim 1, characterized in that, In step 2, the specific process for calculating the MUSIC spatial spectrum is as follows: For each point in the search grid Calculate the direction vector : ； in, , , … This indicates the position coordinates of each hydrophone in the coordinate system. For the frequency of the signal, Speed ​​of sound; Then calculate the MUSIC spatial spectrum. To estimate the direction of a sound source by finding spectral peaks, the calculation formula is as follows: ； in, express The conjugate transpose of; Representing the noise subspace The conjugate transpose of .

7. The method for multi-source localization of hydrophone arrays based on frost-ice optimization algorithm according to claim 1, characterized in that, The specific process of step 3 is as follows: The MUSIC spatial spectrum is normalized to obtain the normalized spectrum. The expression is as follows: ； in, This represents the calculated MUSIC spatial spectrum. The maximum value among absolute values; Set the peak determination threshold ,when When, this region is defined as the gravitational region; when When this region is defined as a repulsive region, an artificial potential energy field of gravitational-repulsive type is constructed.

8. The method for multi-source localization of hydrophone arrays based on frost-ice optimization algorithm according to claim 1, characterized in that, The specific process of step 4 is as follows: Set the population size N and the maximum number of iterations for the frost optimization algorithm. Location update ratio coefficient Frost puncture strength parameters Gravitational coefficient of artificial potential energy field and repulsion coefficient ; For each sound source Initialize a containing An independent subpopulation of particles, each particle's position determined by its azimuth angle. Pitch angle and distance express.

9. The method for multi-source localization of hydrophone arrays based on frost-ice optimization algorithm according to claim 1, characterized in that, The specific process of step 6 is as follows: After each iteration, calculate the total error of the current iteration. : ； in, For the first The optimal individual position of each sound source Indicates the first The true azimuth angle of the sound source. Indicates the first The true pitch angle of the sound source. Indicates the first The actual distance of the sound source Indicates the total number of sound sources; Then calculate the average error. When the iterative convergence condition is met, for each sound source Recalculate the fitness of all particles. The particle with the highest fitness is selected as the final estimated position. .

Images