An underwater passive electric field positioning method and system
By employing an underwater passive electric field positioning method, utilizing an electric field detection array and an improved dung beetle optimization algorithm, the problem of slow target positioning speed and low accuracy of underwater vehicles in complex marine environments was solved, achieving fast and accurate underwater target positioning.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- XI AN JIAOTONG UNIV
- Filing Date
- 2024-12-23
- Publication Date
- 2026-07-21
AI Technical Summary
Existing underwater vehicles struggle to achieve rapid and accurate target positioning in complex marine environments using acoustic detection technology. This is especially true in large bodies of water where the computational workload is enormous, making it impossible to meet the demands for real-time positioning and tracking.
The underwater passive electric field positioning method is adopted. The signal is collected by an electric field detection array, and bandpass filtering and fast Fourier transform are performed to extract the potential amplitude of the target's characteristic frequency. The target positioning is performed by combining the least squares method and the improved dung beetle optimization algorithm. The positioning accuracy and speed are improved by using an equiripple FIR filter and the Harris Eagle algorithm.
While ensuring positioning accuracy, the speed and accuracy of underwater target positioning have been significantly improved. The optimization algorithm of the fitness function accelerates the calculation process and enhances the positioning capability of underwater vehicles in large water areas.
Smart Images
Figure CN119757892B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of underwater detection technology, specifically to an underwater passive electric field positioning method and system. Background Technology
[0002] Underwater vehicles are crucial platforms for performing various underwater missions. Currently, they typically carry sonar equipment for target detection. However, acoustic signals exhibit significant instability when propagating through complex marine environments such as seabed features, water depth, and thermoclines. Furthermore, with the continuous improvement of acoustic stealth capabilities of targets like ships, acoustic detection technology is no longer sufficient to meet the practical needs of underwater target detection, identification, and localization. Axial-frequency electric field signals generated by ships and submarines offer advantages such as channel stability, high propagation speed, strong resistance to environmental noise, and simple detection equipment, making them an effective signal source for underwater target localization.
[0003] In practical applications, it is usually necessary to perform three-dimensional (two-dimensional coordinates of the target and the target orientation) or even four-dimensional (three-dimensional coordinates of the target and the target orientation) searches on large potential water areas to achieve accurate positioning. This requires a huge amount of computation and the positioning speed is slow, which cannot meet the timeliness requirements of real-time positioning and tracking. Summary of the Invention
[0004] The purpose of this invention is to provide an underwater passive electric field positioning method and system to overcome the problem of poor timeliness in underwater target electric field positioning in existing technologies.
[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:
[0006] An underwater passive electric field localization method includes the following steps:
[0007] S1, collects raw electric field signals of underwater targets from different directions;
[0008] S2: Bandpass filtering is performed on the raw electric field signals collected from different directions;
[0009] S3: The filtered signal is processed by fast Fourier transform to extract the potential amplitude at the target characteristic frequency as the corresponding characteristic value of the electrode array.
[0010] S4: Construct a passive electric field localization minimization objective function based on the least squares method and the corresponding eigenvalues of the electrode array;
[0011] S5 uses the minimization objective function as the fitness function and searches for potential target waters using an improved dung beetle optimization algorithm to obtain the target's estimated location and orientation.
[0012] Preferably, the raw signals from the multiple receiving electrodes arranged in the array are filtered by an FIR filter. The filtered signals are obtained by performing filtering separately. :
[0013] .
[0014] Preferably, an electric field detection array is used to collect the original electric field signal of the underwater target from different directions.
[0015] Preferably, the process of processing the filtered signal using Fast Fourier Transform to extract the potential amplitude at the target characteristic frequency is as follows:
[0016] The spectrum of the signal is obtained by performing a Fast Fourier Transform on the filtered signals of each channel.
[0017] Extract the potential amplitude corresponding to the target characteristic frequency of each channel. , The actual location of the target.
[0018] Preferably, for the actual location of the target and search location The error between the measured and theoretical potential perturbation values at each receiving electrode is expressed as:
[0019]
[0020] In the formula, Electrode The theoretical potential perturbation value is obtained through theoretical model calculation; Electrode The measured potential disturbance value.
[0021] The target position normalized spectral function obtained by the least squares method is expressed as:
[0022] .
[0023] Preferably, the normalized spectral function of the target position obtained by the least squares method is used as the fitness function, then the optimal target estimated position is:
[0024] .
[0025] An underwater passive electric field positioning system includes an electric field signal acquisition module, a filtering module, a feature extraction module, an objective function module, and a positioning module;
[0026] The electric field signal acquisition module collects raw electric field signals of underwater targets from different directions;
[0027] The filtering module performs bandpass filtering on the raw electric field signals collected from different directions;
[0028] The feature extraction module performs feature extraction on the filtered signal using fast Fourier transform, extracting the potential amplitude at the target characteristic frequency as the corresponding feature value of the electrode array.
[0029] The objective function module constructs a passive electric field localization minimization objective function based on the least squares method and the acquisition of the corresponding eigenvalues of the electrode array;
[0030] The localization module uses the minimization objective function as the fitness function and searches for the target's potential water area by improving the dung beetle optimization algorithm to obtain the target's estimated location and orientation.
[0031] Preferably, the electric field signal acquisition module specifically uses an electric field detection array to acquire the original electric field signal of the underwater target from different directions.
[0032] Preferably, the process of processing the filtered signal using Fast Fourier Transform to extract the potential amplitude at the target characteristic frequency is as follows:
[0033] The spectrum of the signal is obtained by performing a Fast Fourier Transform on the filtered signals of each channel.
[0034] Extract the potential amplitude corresponding to the target characteristic frequency of each channel. , The actual location of the target.
[0035] Preferably, for the actual location of the target and search location The error between the measured and theoretical potential perturbation values at each receiving electrode is expressed as:
[0036]
[0037] In the formula, Electrode The theoretical potential perturbation value is obtained through theoretical model calculation; Electrode The measured potential disturbance value.
[0038] The target position normalized spectral function obtained by the least squares method is expressed as:
[0039] .
[0040] Compared with the prior art, the present invention has the following beneficial technical effects:
[0041] This invention discloses an underwater passive electric field localization method. The method involves acquiring raw electric field signals of an underwater target from different azimuths; performing bandpass filtering on the acquired raw electric field signals; extracting features from the filtered signals using Fast Fourier Transform (FFT) to obtain the potential amplitude at the target's characteristic frequency as the corresponding feature value of the electrode array; and constructing a passive electric field localization minimization objective function based on the least squares method and the acquired feature values of the electrode array. Addressing the issue of massive computational complexity in target localization within large potential water areas, which cannot meet real-time localization and tracking requirements, this invention uses the target position normalized spectral function obtained through the least squares method as the fitness function of the IDBO algorithm for target localization optimization, thereby improving localization speed while maintaining target localization accuracy.
[0042] This invention filters the original electric field signal using an equirippled FIR bandpass filter, making the noise signal conform to a Gaussian distribution. This ensures that the electric field signal meets the prerequisite for positioning using the axial frequency electric field signal, while also making the signal cleaner and facilitating the extraction of the potential amplitude at the target's characteristic frequency.
[0043] This invention presents a passive electric field localization algorithm based on LS, which has higher localization accuracy compared to array signal processing algorithms such as MUSIC. It addresses the problems of low traversal and poor population diversity in the original DBO algorithm, which uses random numbers to generate the initial population. This invention has better coverage and exploratory capabilities, and can accelerate the convergence speed of the algorithm.
[0044] To address the issue that the original DBO algorithm's rolling ball behavior in dung beetles relies solely on the global worst-case scenario and lacks connection with other individuals in the population, leading to poor global search capability and slow convergence, the Harris Eagle algorithm is introduced to replace the rolling ball behavior, improving the algorithm's global optimization ability. Furthermore, to address the problem in the original DBO algorithm where the dung beetle's position exceeds the search boundary after iteration, rendering subsequent searches invalid, a strategy is adopted to randomly reinitialize the dung beetle's position when it exceeds the search boundary, thereby enhancing individual search capabilities. Effectiveness: To address the issue that the original DBO algorithm stagnates in the later stages of iteration due to the rapid aggregation of egg-laying dung beetles and baby dung beetles near the current optimal position, a perturbation strategy based on the Chebyshev chaotic mapping is adopted. That is, when the population is trapped in a local optimum and cannot escape, the positions of individual egg-laying dung beetles and baby dung beetles are perturbed based on the Chebyshev chaotic mapping to help them escape the local optimum. At the same time, the local optimum is "surrounded" to avoid the situation where the current local optimum is the global optimum and is lost due to perturbation, thus further enhancing the global search capability of the population.
[0045] In summary, compared to existing underwater passive electric field localization algorithms, this method, based on LS and IDBO optimization algorithms, achieves higher positioning accuracy and faster speed. Compared to existing methods, this method makes targeted multi-strategy improvements to the original DBO algorithm based on the practical application characteristics of underwater passive electric field localization, and uses the normalized spectral function obtained through the least squares method as the fitness function. This makes it more accurate and efficient in passive electric field localization of underwater vehicles in large water areas. Therefore, this method has good application prospects in underwater target passive electric field localization and tracking. Attached Figure Description
[0046] Figure 1 This is a flowchart of the underwater passive electric field positioning method in an embodiment of the present invention.
[0047] Figure 2 This is a flowchart of the IDBO algorithm in an embodiment of the present invention.
[0048] Figure 3 This is a schematic diagram of two-dimensional position initialization of the population based on Sobol sequences in an embodiment of the present invention.
[0049] Figure 4 This is a schematic diagram of two-dimensional position based on Chebyshev chaotic mapping in an embodiment of the present invention.
[0050] Figure 5 This is a schematic diagram of a target localization simulation detection scenario in an embodiment of the present invention.
[0051] Figure 6 The images show the original and filtered distributions of electrode noise in an embodiment of the present invention.
[0052] Figure 7 The diagram shows the time and frequency domain representations of the original signal from the electrode sensor in this embodiment of the invention.
[0053] Figure 8 This is a time-domain and frequency-domain diagram of the electrode sensor filter signal in an embodiment of the present invention.
[0054] Figure 9 This is a comparison chart of DBO and IDBO positioning results in an embodiment of the present invention. Detailed Implementation
[0055] To enable those skilled in the art to better understand the present invention, 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.
[0056] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0057] like Figure 1 As shown, the present invention provides an underwater passive electric field positioning method, which specifically includes the following steps:
[0058] S1 uses an electric field detection array to collect raw electric field signals of underwater targets from different directions;
[0059] S2: The raw electric field signal acquired by the electric field detection array is bandpass filtered by an equiripple FIR bandpass filter;
[0060] S3: The filtered signal is processed by Fast Fourier Transform (FFT) to extract the potential amplitude at the target characteristic frequency as the corresponding characteristic value of the electrode array.
[0061] S4: Construct a passive electric field localization minimization objective function based on the least squares method and the corresponding eigenvalues of the electrode array;
[0062] S5 uses the minimization objective function as the fitness function and searches for potential target waters using an improved dung beetle optimization algorithm to obtain the target's estimated location and orientation.
[0063] In a specific embodiment of this application, the specific steps of bandpass filtering the original electric field signals of each receiving electrode using an equiripple FIR bandpass filter as described in S2 are as follows:
[0064] (2-1) Determine the values of each parameter of the FIR filter: First stopband frequency First passband frequency Second passband frequency Second stopband frequency First stopband attenuation rate Second stopband attenuation rate ripples Density coefficient ;
[0065] (2-2) The original signals of the multiple receiving electrodes set in the array are filtered by an FIR filter. The filtered signals are obtained by performing filtering separately. :
[0066]
[0067] Furthermore, the specific steps described in S3 for processing the filtered signal using Fast Fourier Transform (FFT) to extract the potential amplitude at the target characteristic frequency are as follows:
[0068] (3-1) Perform a Fast Fourier Transform on the filtered signals of each channel to obtain the signal spectrum;
[0069] (3-2) Extract the potential amplitude corresponding to the target characteristic frequency of each channel. , The actual location of the target.
[0070] Furthermore, the specific steps for constructing the passive electric field localization minimization objective function based on the least squares (LS) method, as described in S4, are as follows:
[0071] (4-1) Regarding the actual location of the target and search location The error between the measured and theoretical potential perturbation values at each receiving electrode is expressed as:
[0072]
[0073] In the formula, Electrode The theoretical potential perturbation value is obtained through theoretical model calculation; Electrode The measured potential disturbance value.
[0074] (4-2) The target position normalized spectral function obtained by the least squares method is expressed as:
[0075]
[0076] As described in S5, minimizing the objective function is used as the fitness function, and the improved dung beetle optimization algorithm is used to search for potential target water areas. Figure 2 As shown, the specific steps for outputting the estimated target position and orientation are as follows:
[0077] (5-1) Using the normalized spectral function of the target position obtained by the least squares method as the fitness function, the optimal target estimated position is:
[0078]
[0079] (5-2) Setting relevant parameters for the IDBO algorithm: population size Number of rolling dung beetles Number of egg-laying dung beetles Number of dung beetles Number of dung beetles stealing Dimensionality of the positioning optimization problem Locating the boundary of the optimization problem Population iteration count ;
[0080] (5-2) Based on Sequence random numbers Initialize the population location, such as Figure 3 As shown:
[0081] (5-3) Calculate the fitness value of all dung beetles;
[0082] (5-4) Update the position of the dung beetle:
[0083]
[0084] In the formula, For a random dung beetle in the first... The position at the next iteration; The average position of all rolling dung beetles; , , , , All belong to random numbers, Providing diverse trends for the search enables it to explore the feature space of different regions. The closer the value is to 1, the stronger the randomness of the search strategy.
[0085] (5-5) When the dung beetle encounters an obstacle and cannot move forward, it adjusts its direction by dancing. In DBO, the dancing behavior of the dung beetle is simulated using a tangent function to obtain a new route:
[0086]
[0087] In the formula, For the deflection angle, when When the tangent of the deflection angle is 0, the position of the rolling dung beetle is not updated in this iteration;
[0088] (5-6) Perform boundary checks on the position of the dung beetle. If it exceeds the boundary, update its position randomly.
[0089]
[0090] In the formula, For size A random vector;
[0091] (5-7) Calculate the fitness value of the dung beetle and update the local optimum. and the global optimal solution ;
[0092] (5-8) Determine if the system is trapped in a local optimum. If so, update the positions of the egg-laying dung beetle and the baby dung beetle based on the Chebyshev chaotic mapping, such as... Figure 4 As shown:
[0093]
[0094] If not, then update normally as follows;
[0095] (5-9) Update the location of the egg-laying dung beetle:
[0096]
[0097]
[0098] In the formula, This marks the lower boundary of the spawning area. This is the upper boundary of the spawning area; This is the current local optimum position; To optimize the lower bound of the problem; To optimize the upper bound of the problem; , This represents the maximum number of iterations. For the first The breeding ball was in the first The position at the next iteration; For two independent sizes A random vector;
[0099] (5-10) Update the location of the dung beetle:
[0100]
[0101]
[0102] In the formula, For the first Only one dung beetle in the first The position at the next iteration; These are random numbers that follow a normal distribution. Belonging to random variables; This marks the lower boundary of the spawning area. This is the upper boundary of the spawning area; This is the current local optimum position;
[0103] (5-11) Update the location of the dung beetle:
[0104]
[0105] In the formula, For the first Only stealing dung beetles on the first The position at the next iteration; It is a constant; Let be a normally distributed integer of size . A random vector;
[0106] (5-12) Perform boundary checks on the locations of egg-laying dung beetles, baby dung beetles, and thieving dung beetles. If they exceed the boundary, update their locations randomly.
[0107]
[0108] (5-13) Calculate the fitness values of the egg-laying dung beetle, the small dung beetle, and the thieving dung beetle, and update the global optimal solution;
[0109] (5-14) Repeat (5-4) to (5-13) until the number of iterations is reached. .
[0110] Example
[0111] To verify the advantages of the underwater passive electric field localization method based on the IDBO algorithm used in this invention, such as... Figure 5 As shown, this method utilizes an electrode detection array to acquire the original electric field signal of a simulated underwater target, and performs bandpass filtering and feature extraction on the original electric field signal, such as... Figure 6 The diagram shows the original and filtered distributions of electrode noise. Target localization experiments were conducted using the LS-based IDBO algorithm to verify the performance of the proposed target localization method. Figure 7 The diagram shows the time and frequency domain plots of the original signal from the electrode sensor. Figure 8 The figure on the left shows the target localization result obtained by the original DBO algorithm, which takes 72.2298 seconds to run and has an average ReRMSE of 0.1170. Figure 8 The figure on the right shows the target localization result obtained by the IDBO algorithm. Its running time is 72.2716 seconds, and the average ReRMSE of the localization result is 0.0861. Compared with the original DBO algorithm, its localization error is reduced by approximately 26.41%. The results are as follows... Figure 9 As shown, the results indicate that the underwater passive electric field localization method based on the IDBO algorithm has higher accuracy.
Claims
1. A method for underwater passive electric field positioning, characterized in that, Includes the following steps: S1, collects raw electric field signals of underwater targets from different directions; S2: Bandpass filtering is performed on the raw electric field signals collected from different directions; S3: The filtered signal is processed by fast Fourier transform to extract the potential amplitude at the target characteristic frequency as the corresponding characteristic value of the electrode array. S4: Construct the target position normalization spectrum function for passive electric field positioning based on the least squares method and the corresponding eigenvalues of the electrode array; S5, using the target location normalized spectral function as the fitness function, the target potential water area is searched by the improved dung beetle optimization algorithm to obtain the target estimated location and orientation; The specific steps for processing the filtered signal using Fast Fourier Transform to extract the potential amplitude at the target characteristic frequency are as follows: The spectrum of the signal is obtained by performing a Fast Fourier Transform on the filtered signals of each channel. Extract the measured potential perturbation values corresponding to the target characteristic frequencies of each channel. , The actual location of the target; For the actual location of the target and search location The error between the measured and theoretical potential perturbation values at each receiving electrode is expressed as: In the formula, Electrode The theoretical potential perturbation value is obtained through theoretical model calculation; Electrode The measured potential disturbance value on the surface; The target position normalized spectral function obtained by the least squares method is expressed as: ； Using the normalized spectral function of the target position obtained by the least squares method as the fitness function, the optimal target estimated position is: 。 2. The underwater passive electric field positioning method according to claim 1, characterized in that, The raw signals from the multiple receiving electrodes of the array are filtered by an FIR filter. The filtered signals are obtained by performing filtering separately. : 。 3. The underwater passive electric field positioning method according to claim 1, characterized in that, An electric field detection array was used to collect raw electric field signals of underwater targets from different directions.
4. An underwater passive electric field positioning system, characterized in that, It includes an electric field signal acquisition module, a filtering module, a feature extraction module, an objective function module, and a positioning module; The electric field signal acquisition module collects raw electric field signals of underwater targets from different directions; The filtering module performs bandpass filtering on the raw electric field signals collected from different directions; The feature extraction module performs feature extraction on the filtered signal using fast Fourier transform, extracting the potential amplitude at the target characteristic frequency as the corresponding feature value of the electrode array. The objective function module constructs a target position normalized spectral function for passive electric field localization based on the least squares method and the acquisition of the corresponding eigenvalues of the electrode array. The localization module uses the target location normalized spectral function as the fitness function and searches for the target's potential water area by improving the dung beetle optimization algorithm to obtain the target's estimated location and orientation. The specific steps for processing the filtered signal using Fast Fourier Transform to extract the potential amplitude at the target characteristic frequency are as follows: The spectrum of the signal is obtained by performing a Fast Fourier Transform on the filtered signals of each channel. Extract the measured potential perturbation values corresponding to the target characteristic frequencies of each channel. , The actual location of the target; For the actual location of the target and search location The error between the measured and theoretical potential perturbation values at each receiving electrode is expressed as: In the formula, Electrode The theoretical potential perturbation value is obtained through theoretical model calculation; Electrode The measured potential disturbance value on the surface; The target position normalized spectral function obtained by the least squares method is expressed as: ； Using the normalized spectral function of the target position obtained by the least squares method as the fitness function, the optimal target estimated position is: 。 5. The underwater passive electric field positioning system according to claim 4, characterized in that, The electric field signal acquisition module specifically uses an electric field detection array to collect the original electric field signals of underwater targets from different directions.