Hierarchical optimization search underwater acoustic Doppler estimation method and system

Abstract

The invention discloses a hierarchical optimization search underwater acoustic Doppler estimation method and system, and the method comprises the steps: S1, a receiving end receives an OFDM signal frame, carries out the preliminary frequency offset scanning, and obtains a preliminary Doppler frequency offset value f corresponding to the maximum value of a correlation index and a coarse Doppler estimation interval [f-1, f + 1]; s2, randomly generating a group of initial particles in the coarse Doppler estimation interval [f-1, f + 1], each initial particle representing a candidate frequency offset value; and S3, calculating the amplitude of the mutual ambiguity function of each initial particle as a fitness function for continuous iteration until a convergence condition is met, and obtaining a fine Doppler frequency offset f1. The problem of high calculation complexity can be effectively solved under the condition that Doppler estimation precision is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of underwater acoustic communication, and in particular to a hierarchical optimization search underwater acoustic Doppler estimation method and system. Background Art

[0002] In underwater acoustic communications, the Doppler effect affects signal propagation, causing frequency shifts that affect communication quality. Therefore, accurately estimating Doppler shift is crucial for compensating for signal distortion and improving the stability and reliability of communication systems. Traditional Doppler estimation algorithms, including block Doppler estimation, cross-ambiguity function Doppler estimation, and minimum mean square error Doppler estimation, can estimate Doppler shift to a certain extent. However, their accuracy and efficiency remain to be improved in complex ocean environments.

[0003] Among them, the Doppler estimation based on the block Doppler estimation algorithm only needs to use two identical correlators at the receiving end to estimate the average Doppler factor within the data block, but its accuracy is limited by the length of the transmitted data frame.

[0004] As a commonly used Doppler estimation algorithm, the mutual ambiguity function Doppler estimation algorithm determines the Doppler frequency offset by calculating the mutual ambiguity function between the received signal and the reference signal.

[0005] However, when performing Doppler frequency offset estimation, the cross-ambiguity function Doppler estimation algorithm requires searching within a relatively large range to find the maximum value of the cross-ambiguity function. As the search range expands, the computational complexity increases dramatically. Especially when high-precision estimation is required, the reduction in the search interval multiplies the number of searches within the same search range, significantly increasing computational resource consumption and limiting its application in underwater acoustic communication systems with high real-time requirements.

[0006] Furthermore, the performance of the cross-ambiguity function Doppler estimation algorithm is also affected when dealing with multipath interference and noise, making it prone to misjudgment and inaccurate estimation. Therefore, a new Doppler estimation algorithm that can effectively solve the problem of excessive search times in the cross-ambiguity function algorithm is urgently needed to improve the real-time performance and reliability of underwater acoustic communication systems. Summary of the Invention

[0007] In order to solve these problems, the present invention proposes a hierarchical optimization search underwater acoustic Doppler estimation method and system, which can effectively solve the problem of high computational complexity while ensuring the accuracy of Doppler estimation.

[0008] According to a first aspect of the present invention, a hierarchical optimization search underwater acoustic Doppler estimation method is proposed, comprising the following steps:

[0009] S1, the receiving end receives the OFDM signal frame and performs a preliminary frequency offset scan to obtain the preliminary Doppler frequency offset value f and the coarse Doppler estimation interval [f-1, f+1] corresponding to the maximum value of the correlation index;

[0010] S2, randomly generating a group of initial particles in the coarse Doppler estimation interval [f-1, f+1], each initial particle representing a candidate frequency offset value;

[0011] S3: Calculate the mutual ambiguity function amplitude for each initial particle as a fitness function and iterate until convergence conditions are met to obtain a fine Doppler frequency offset f1. This search is performed by first performing a coarse Doppler estimation followed by hierarchical optimization, effectively reducing computational complexity and avoiding the high computational complexity associated with traditional grid-by-grid search methods.

[0012] Preferably, performing a preliminary frequency offset scan in S1 specifically includes: using a local reference signal to perform a preliminary frequency offset scan within a preset frequency offset range using a time-domain sliding correlator group with a step size of 1 Hz. A sliding window traversal search is performed to calculate the amplitude of the mutual ambiguity function under different frequency offset assumptions, and a rough estimate and its confidence interval are determined based on the peak detection principle. A subsequent fine search is performed within the rough estimate and its confidence interval, thereby reducing computational complexity. Therefore, this method is more suitable for mobile underwater information transmission scenarios with high real-time requirements.

[0013] Preferably, the initial position of the initial particle in S2 is obtained by random generation, and the velocity update formula of the initial particle is as follows:

[0014]

[0015] The updating formula of the initial particle position is as follows:

[0016]

[0017] Where V id represents the velocity vector of the i-th particle in the d-th dimension, X represents the position vector of the i-th particle in the d-th dimension, the superscripts k and k+1 represent the k-th and k+1-th iterations respectively, w is the inertia weight, c1 is the individual learning factor, c2 is the social learning factor, r is a random number in the interval [0,1], pbest id is the optimal position of the individual particle, gbest d is the position of the particle with the optimal fitness function value in the population. By treating the Doppler frequency shift as the particle's position variable and selecting the correlation function value as the fitness function, a refined estimation model based on swarm intelligence is constructed and continuously iterated. While ensuring estimation accuracy, this significantly reduces computational complexity and avoids the high computational complexity of traditional grid-by-grid search methods.

[0018] Preferably, c1 is set to follow a linear decrease, and the update interval is [c 1min, c 1max ] and the update formula is:

[0019]

[0020] Set c2 to follow linear increment, and update interval is [c 2min, c 2max ] and the update formula is:

[0021]

[0022] Set w update interval [w min, w max ], the update formula is:

[0023]

[0024] Where k represents the kth iteration, k max Indicates the preset maximum number of iterations, con_speed is the current iteration convergence speed, and max_con_speed is the maximum convergence speed. Based on the current number of iterations and convergence status, various parameters are adaptively adjusted to accelerate convergence and improve the efficiency of Doppler estimation.

[0025] Preferably, the OFDM signal frame in S1 is an OFDM data frame with two identical OFDM symbols inserted before the frame. By constructing a specifically structured OFDM signal frame for Doppler estimation, no additional pilot signal insertion is required. Compared with cyclic prefix-based methods, this method does not require complex mathematical operations and large amounts of storage space. It can more accurately estimate frequency offset in a multipath environment, is less affected by signal distortion and interference caused by multipath propagation, and can effectively achieve fast, accurate, and synchronized communication.

[0026] Preferably, the convergence condition specifically includes: the difference between the optimal frequency offset value of the iteration and the global optimal frequency offset value is less than or equal to a threshold, and the minimum number of iterations or the maximum number of iterations is reached. By limiting the convergence condition, the obtained Doppler estimation result significantly reduces the number of searches while ensuring the accuracy of the result and the performance of the method.

[0027] Preferably, the threshold is 0.01 Hz, the minimum number of iterations is 6 iterations, and the maximum number of iterations is 50 iterations.

[0028] According to a second aspect of the present invention, a hierarchical optimization search underwater acoustic Doppler estimation system is proposed, which implements the method described in any one of the first aspects and includes the following modules:

[0029] Frequency offset scanning module: The receiving end receives the OFDM signal frame and performs a preliminary frequency offset scan to obtain the preliminary Doppler frequency offset value f and the coarse Doppler estimation interval [f-1, f+1] corresponding to the maximum value of the correlation index;

[0030] Particle generation module: randomly generates a group of initial particles in the coarse Doppler estimation interval [f-1, f+1], each initial particle represents a candidate frequency offset value;

[0031] Optimization search module: Calculate the mutual ambiguity function amplitude of each initial particle as the fitness function and iterate continuously until the convergence condition is met to obtain the fine Doppler frequency offset f1.

[0032] According to a third aspect of the present invention, a computer program product is provided, on which a computer program is stored. When the computer program is executed by a processor, the method according to any one of the first aspects is implemented.

[0033] According to a fourth aspect of the present invention, a computing system is provided, comprising a processor and a memory, wherein the processor is configured to execute the method according to any one of the first aspects.

[0034] Compared with the prior art, the present invention is beneficial in that:

[0035] (1) Accurately estimate the Doppler frequency shift of the signal to make corresponding compensation, ensure the correct demodulation of the signal, and improve the effectiveness and robustness of underwater information transmission.

[0036] (2) Through the collaborative optimization of coarse-fine two-level estimation, the traditional two-dimensional Doppler-delay search problem is transformed into a high-dimensional parameter space optimization problem. Through the particle hierarchical optimization mechanism, the computational complexity of Doppler estimation is reduced. A time-varying parameter control strategy is used to adaptively optimize the velocity update equation, and the convergence characteristics of the particle swarm algorithm are used to achieve adaptive contraction of the search space. While ensuring the estimation accuracy, the computational complexity is significantly reduced, avoiding the high computational complexity of the traditional grid-by-grid search method. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated into and constitute a part of this specification. The accompanying drawings illustrate the embodiments and, together with the description, serve to explain the principles of the present invention. Other embodiments and many of the expected advantages of the embodiments will be readily apparent as they become better understood by reference to the following detailed description. The elements of the drawings are not necessarily to scale with respect to each other. Like reference numerals designate corresponding similar parts.

[0038] Figure 1 This is a flow chart of a hierarchical optimization search underwater acoustic Doppler estimation method proposed by the present invention;

[0039] Figure 2 Schematic diagram of the simulated channel impulse response corresponding to the simulated underwater acoustic channel simulated by the present invention;

[0040] Figure 3 A schematic diagram of the structure of an OFDM signal frame constructed in the present invention;

[0041] Figure 4 A comparison chart of the search times of the mutual ambiguity function Doppler estimation and the estimation method of the present invention;

[0042] Figure 5 The bit error rate comparison chart of three Doppler estimation methods;

[0043] Figure 6 This is the MSE comparison chart of three Doppler estimation methods;

[0044] Figure 7 This is a structural diagram of a hierarchical optimization search underwater acoustic Doppler estimation system proposed by the present invention;

[0045] Figure 8 A schematic diagram of the computer system structure of an electronic device suitable for implementing the embodiments of the present application is shown. DETAILED DESCRIPTION

[0046] The present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the relevant invention are shown in the accompanying drawings.

[0047] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0048] like Figure 1 As shown, according to the first aspect of the present invention, a hierarchical optimization search underwater acoustic Doppler estimation method is proposed, comprising the following steps:

[0049] S1, the receiving end receives the OFDM signal frame and performs a preliminary frequency offset scan to obtain the preliminary Doppler frequency offset value f and the coarse Doppler estimation interval [f-1, f+1] corresponding to the maximum value of the correlation index;

[0050] S2, randomly generating a group of initial particles in the coarse Doppler estimation interval [f-1, f+1], each initial particle representing a candidate frequency offset value;

[0051] S3, calculating the mutual ambiguity function amplitude of each initial particle as a fitness function and continuously iterating until a convergence condition is met to obtain a fine Doppler frequency offset f1.

[0052] As an example, an underwater acoustic OFDM communication implementation is used for detailed description. The performance of the proposed method is verified using a time-frequency-difference OFDM underwater acoustic communication system. To verify the effectiveness of the proposed method in the underwater acoustic OFDM communication system, a simulation using the BELLHOP toolbox was performed to simulate an underwater acoustic channel.

[0053] The simulated underwater acoustic channel parameters are set as follows: water depth 25m, horizontal distance between transmitter and receiver 1000m, depth of transmitter and receiver underwater acoustic transducers are both 10m, sound velocity range is 1540-1543m / s, water medium is uniform, sea surface and seabed are flat, and the simulated channel impulse response is obtained as follows: Figure 2 shown.

[0054] In this experiment, the simulation parameters for the underwater acoustic OFDM communication system were set as follows: center frequency 25,000 Hz, operating bandwidth 8,000 Hz, system sampling rate 96,000 Hz, number of OFDM subcarriers 340, number of FFT points 4,096, OFDM symbol length 42.6 ms, cyclic prefix length 10.6 ms, OFDM data frame length 2, and DQPSK modulation for bit information. Channel coding used a 50% convolutional code, followed by arithmetic interleaving with an interleaving depth of 2 to further suppress burst errors.

[0055] The frame format constructed in this experiment is as follows Figure 3 As shown, a 17.7ms linear frequency sweep signal is used as the transmitted synchronization signal, followed by a blank signal as a guard interval. Two identical OFDM symbols are inserted before the OFDM data frame as training sequences for Doppler frequency offset estimation, used for mutual ambiguity function Doppler estimation and the Doppler estimation method proposed in this paper. The two LFM signals before and after the OFDM data frame are used for block Doppler estimation.

[0056] The preliminary frequency offset scan in S1 specifically includes: using a local reference signal and performing preliminary frequency offset scan by a time domain sliding correlator group within a preset frequency offset range with a step size of 1 Hz.

[0057] The Doppler frequency deviation is usually from several Hz to more than ten Hz. The preset frequency deviation range set in the present invention is [-25, -5], where the difference between positive and negative values ​​lies in the direction of movement.

[0058] Record the frequency offset value corresponding to the peak of the mutual ambiguity function, select the corresponding Doppler frequency offset f with the largest correlation index, and expand it 1 Hz to both sides to form a coarse Doppler estimation interval [f-1, f+1].

[0059] On this basis, a fine estimation model based on swarm intelligence is constructed: the coarse Doppler estimation interval [f-1, f+1] is mapped to the solution space, each candidate frequency offset parameter corresponds to a particle position vector, and the amplitude of the mutual ambiguity function is used as the fitness evaluation index. The intelligent search of the parameter space is realized through the particle velocity update equation and the individual / swarm optimal solution guidance mechanism.

[0060] The fitness function defined in the hierarchical optimization search underwater acoustic Doppler estimation method and system proposed in the present invention is:

[0061]

[0062] P best represents fitness, f d represents the Doppler frequency shift, y(t) represents the received signal, and s(t) is the local signal.

[0063] The initial position of the particle swarm is obtained by random generation, and its velocity update follows the following formula:

[0064]

[0065] The position update follows the following formula:

[0066]

[0067] V id represents the velocity vector of the i-th particle in the d-th dimension, and X represents the position vector of the i-th particle in the d-th dimension. The superscripts k and k+1 represent the k-th and k+1-th iterations, respectively. w is the inertia weight, c1 is the individual learning factor, c2 is the social learning factor, and r is a random number in the interval [0,1]. pbest id is the optimal position of the individual particle, gbest d is the position of the particle with the best fitness function value in the population, that is, the global optimal position.

[0068] Based on the guidance of the global optimal position and the individual optimal position, the search direction and step size of each particle are dynamically adjusted. The individual optimal position is the position where each particle has the highest fitness function value during the iteration process, and the global optimal position is the position of the particle with the highest fitness function among all the individual optimal positions of all particles.

[0069] The search direction is determined by the difference between the individual optimal position and the global optimal position. Particles move closer to the global optimal position, adjusting the search direction. The search step size is determined by the speed, which is controlled by the inertia weight w, the individual learning factor c1, and the social learning factor c2.

[0070] The search range is narrowed after each iteration until the following two convergence conditions are met:

[0071] 1) The difference between the optimal frequency offset value of the current iteration and the global optimal frequency offset value is less than or equal to 0.01 Hz;

[0072] 2) Reach the minimum number of iterations (6 iterations) or the maximum number of iterations (50 iterations).

[0073] Preferably, the initial position of the initial particle in S2 is obtained by random generation, and the velocity update formula of the initial particle is as follows:

[0074]

[0075] The updating formula of the initial particle position is as follows:

[0076]

[0077] Where V id represents the velocity vector of the i-th particle in the d-th dimension, X represents the position vector of the i-th particle in the d-th dimension, the superscripts k and k+1 represent the k-th and k+1-th iterations respectively, w is the inertia weight, c1 is the individual learning factor, c2 is the social learning factor, r is a random number in the interval [0,1], pbest id is the optimal position of the individual particle, gbest d is the position of the particle with the optimal fitness function value in the population. By treating the Doppler frequency shift as the particle's position variable and selecting the correlation function value as the fitness function, a refined estimation model based on swarm intelligence is constructed and continuously iterated. While ensuring estimation accuracy, this significantly reduces computational complexity and avoids the high computational complexity of traditional grid-by-grid search methods.

[0078] The change of particle speed is controlled by three parameters. c1 controls the particle to move to its individual optimal position, which indicates the degree of dependence of the particle on its own experience. In the algorithm proposed in this invention, c1 follows a linear decrease. The update interval of c1 is set to [c 1min, ,c 1max ] and the update follows the following formula:

[0079]

[0080] c2 controls the movement of particles to their global optimal position, indicating the degree of dependence of particles on group experience. c2 follows a linear increase, which helps the algorithm to have better convergence in the later stage of the search. The c2 update interval is set to [c 2min, c 2max ]Update follows the formula:

[0081]

[0082] w represents the influence of the velocity of the previous generation of particles on the velocity of the current generation of particles. A larger w value is beneficial to global search and helps particles escape from the local optimum, but a larger w value will slow down the convergence speed. The algorithm proposed in this invention adaptively adjusts w according to the current number of iterations and convergence status to accelerate convergence and improve algorithm efficiency. The w update interval [w min, w max ], the update formula is as follows:

[0083]

[0084] k represents the kth iteration, k max Indicates the preset maximum number of iterations, con_speed is the current iteration convergence speed, and max_con_speed is the maximum convergence speed.

[0085] according to Figure 4 and Figure 5 The analysis results show that, under the premise of ensuring the bit error rate performance, the hierarchical optimization search underwater acoustic Doppler estimation method and system proposed in the present invention can significantly reduce the number of searches. Compared with the traditional mutual ambiguity function Doppler estimation algorithm and block Doppler estimation algorithm, the hierarchical optimization search underwater acoustic Doppler estimation algorithm and system proposed in the present invention reduces the computational complexity while ensuring the estimation performance, and is therefore more suitable for mobile underwater information transmission scenarios with high real-time requirements. Figure 6 It can be seen that the Doppler estimation algorithm and system proposed in the present invention achieve an estimation performance similar to that of the mutual ambiguity function Doppler estimation algorithm, and are superior to the block Doppler estimation algorithm.

[0086] According to the second aspect of the present invention, Figure 7 As shown in the figure, a hierarchical optimization search underwater acoustic Doppler estimation system is proposed, which includes the following modules:

[0087] Frequency offset scanning module 701: The receiving end receives the OFDM signal frame and performs preliminary frequency offset scanning to obtain the preliminary Doppler frequency offset value f and the coarse Doppler estimation interval [f-1, f+1] corresponding to the maximum value of the correlation index;

[0088] Particle generation module 702: randomly generates a group of initial particles in the coarse Doppler estimation interval [f-1, f+1], each initial particle represents a candidate frequency offset value;

[0089] Optimization search module 703: Calculate the mutual ambiguity function amplitude of each initial particle as the fitness function and iterate continuously until the convergence condition is met to obtain the fine Doppler frequency offset f1.

[0090] Reference below Figure 8 , which shows a structural diagram of a computer system 800 suitable for implementing an electronic device of an embodiment of the present application. Figure 8 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0091] like Figure 8 As shown, the computer system 800 includes a central processing unit (CPU) 801, which performs various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage unit 809 into a random access memory (RAM) 804. Various programs and data required for the operation of the system 800 are also stored in the RAM 804. The CPU 801, ROM 802, ROM 803, and RAM 804 are connected to each other via a bus 805. An input / output (I / O) interface 806 is also connected to the bus 805.

[0092] The following components are connected to the I / O interface 806: an input section 807 including a keyboard, a mouse, and the like; an output section 808 including a liquid crystal display (LCD) and speakers; a storage section 809 including a hard disk and the like; and a communication section 810 including a network interface card such as a LAN card or a modem. The communication section 810 performs communication processing via a network such as the Internet. A drive 811 is also connected to the I / O interface 806 as needed. A removable medium 812, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 811 as needed, so that a computer program read therefrom can be installed in the storage section 809 as needed.

[0093] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart is implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program is downloaded and installed from the network via the communication section 810, and / or installed from the removable medium 812. When the computer program is executed by the central processing unit (CPU) 801, the above-mentioned functions defined in the method of the present application are performed.

[0094] It should be noted that the computer-readable storage medium of the present application is a computer-readable signal medium or a computer-readable storage medium or any combination of the two. Computer-readable storage media are, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or devices, or any combination of the above. More specific examples of computer-readable storage media include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium is any tangible medium that contains or stores a program that is used by or in conjunction with an instruction execution system, device, or device. In the present application, a computer-readable signal medium includes a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal takes a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium is any computer-readable storage medium other than a computer-readable storage medium that transmits, propagates, or transports a program for use by or in connection with an instruction execution system, apparatus, or device. The program code embodied on the computer-readable storage medium is transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber cable, RF, or any suitable combination thereof.

[0095] Computer program code for performing the operations of the present application is written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code is executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer is connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or is connected to an external computer (e.g., through the Internet using an Internet service provider).

[0096] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram represents a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession are actually executed substantially in parallel, and they are sometimes also executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart, are implemented using a dedicated hardware-based system that performs the specified function or operation, or are implemented using a combination of dedicated hardware and computer instructions.

[0097] The modules involved in the embodiments described in this application are implemented by software and hardware.

[0098] As another aspect, the present application also provides a computer-readable storage medium, which is included in the electronic device described in the above embodiment; it also exists independently and is not assembled into the electronic device. The above computer-readable storage medium carries one or more programs. When the above one or more programs are executed by the electronic device, the electronic device: S1, the receiving end receives the OFDM signal frame and performs a preliminary frequency deviation scan to obtain the preliminary Doppler frequency deviation value f and the coarse Doppler estimation interval [f-1, f+1] corresponding to the maximum value of the correlation index; S2, randomly generates a group of initial particles in the coarse Doppler estimation interval [f-1, f+1], each initial particle represents a candidate frequency deviation value; S3, calculates the mutual ambiguity function amplitude of each initial particle as a fitness function and continuously iterates until the convergence condition is met to obtain the fine Doppler frequency deviation f1.

[0099] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned inventive concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A hierarchical optimization search underwater acoustic Doppler estimation method, characterized in that: The following steps are involved: S1, the receiving end receives the OFDM signal frame and performs a preliminary frequency offset scan to obtain the preliminary Doppler frequency offset value f and the coarse Doppler estimation interval [f-1, f+1] corresponding to the maximum value of the correlation index; S2, randomly generating a group of initial particles in the coarse Doppler estimation interval [f-1, f+1], each initial particle representing a candidate frequency offset value; S3, calculating the mutual ambiguity function amplitude of each initial particle as a fitness function and continuously iterating until a convergence condition is met to obtain a fine Doppler frequency offset f1.

2. The hierarchical optimization search underwater acoustic Doppler estimation method according to claim 1, characterized in that: The preliminary frequency offset scan in S1 specifically includes: using a local reference signal and performing preliminary frequency offset scan by a time domain sliding correlator group within a preset frequency offset range with a step size of 1 Hz.

3. The hierarchical optimization search underwater acoustic Doppler estimation method according to claim 1, characterized in that: The initial position of the initial particle in S2 is obtained by random generation, and the velocity update formula of the initial particle is as follows: The updating formula of the initial particle position is as follows: Where V id represents the velocity vector of the i-th particle in the d-th dimension, X represents the position vector of the i-th particle in the d-th dimension, the superscripts k and k+1 represent the k-th and k+1-th iterations respectively, w is the inertia weight, c1 is the individual learning factor, c2 is the social learning factor, r is a random number in the interval [0,1], pbest id is the optimal position of the individual particle, gbest d is the position of the particle with the best fitness function value in the population.

4. The hierarchical optimization search underwater acoustic Doppler estimation method according to claim 1, characterized in that: Set c1 to follow linear decrease, and the update interval is [c 1min, c 1max ] and the update formula is: Set c2 to follow linear increment, and update interval is [c 2min, c 2max ] and the update formula is: Set w update interval [w min, w max ], the update formula is: Where k represents the kth iteration, k max Indicates the preset maximum number of iterations, con_speed is the current iteration convergence speed, and max_con_speed is the maximum convergence speed.

5. The hierarchical optimization search underwater acoustic Doppler estimation method according to claim 1, characterized in that: The OFDM signal frame in S1 is an OFDM data frame with two identical OFDM symbols inserted before the frame.

6. The hierarchical optimization search underwater acoustic Doppler estimation method according to claim 1, characterized in that: The convergence condition specifically includes: the difference between the optimal frequency offset value of the current iteration and the global optimal frequency offset value is less than or equal to a threshold, and the minimum number of iterations or the maximum number of iterations is reached.

7. The hierarchical optimization search underwater acoustic Doppler estimation method according to claim 6, characterized in that: The threshold is 0.01 Hz, the minimum number of iterations is 6 iterations, and the maximum number of iterations is 50 iterations.

8. A hierarchical optimization search underwater acoustic Doppler estimation system, characterized in that: The method according to any one of claims 1 to 7 comprises the following modules: Frequency offset scanning module: The receiving end receives the OFDM signal frame and performs a preliminary frequency offset scan to obtain the preliminary Doppler frequency offset value f and the coarse Doppler estimation interval [f-1, f+1] corresponding to the maximum value of the correlation index; Particle generation module: randomly generates a group of initial particles in the coarse Doppler estimation interval [f-1, f+1], each initial particle represents a candidate frequency offset value; Optimization search module: Calculate the mutual ambiguity function amplitude of each initial particle as the fitness function and iterate continuously until the convergence condition is met to obtain the fine Doppler frequency offset f1.

9. A computer program product having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method according to any one of claims 1 to 7.

10. A computing system comprising a processor and a memory, wherein the processor is configured to execute the method according to any one of claims 1 to 7.

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