Underwater passive electric field positioning method and system

Abstract

The invention discloses an underwater passive electric field positioning method and system. The method comprises the following steps: acquiring original electric field signals of an underwater target from different directions; performing band-pass filtering processing on the original electric field signals acquired in different directions; performing feature extraction processing on the filtered signal through fast Fourier transform, and extracting a potential amplitude at a target feature frequency as a corresponding feature value of the electrode array; a passive electric field positioning minimization objective function is constructed based on a least square method and acquired corresponding characteristic values of an electrode array, and the problems that target positioning is carried out in a target potential water area with a large area, so that the calculated amount is huge, and the real-time positioning and tracking requirements cannot be met are solved. A target position normalization spectrum function obtained through a least square method is used as a fitness function of an IDBO algorithm to carry out target positioning optimization, and the positioning speed is improved under the condition that the target positioning accuracy is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of underwater detection technology, and in particular to an underwater passive electric field positioning method and system. Background Art

[0002] Underwater vehicles are important platforms for performing various underwater tasks. Currently, underwater vehicles are usually equipped with sonar equipment to detect targets. However, there is great instability when acoustic signals propagate in complex marine environments such as seabed features, water depth, and thermocline. At the same time, as the acoustic stealth performance of targets such as ships continues to improve, it is difficult to meet the actual needs of underwater target detection, identification, and positioning through acoustic detection technology. The shaft-frequency electric field signals generated by ships, submarines, etc. have the advantages of stable channels, fast propagation speed, strong resistance to environmental noise, and simple detection equipment. They are an effective signal source for underwater target positioning.

[0003] In practical applications, it is usually necessary to conduct three-dimensional (target two-dimensional coordinates, target orientation) or even four-dimensional (target three-dimensional coordinates, target orientation) searches of potential target waters with a large area in order to achieve accurate positioning. The computational workload is huge 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 the present invention is to provide an underwater passive electric field positioning method and system to overcome the problem of poor timeliness of electric field positioning of underwater targets in the prior art.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is as follows: An underwater passive electric field positioning method comprises the following steps: S1, collects the original electric field signals of underwater targets from different directions; S2: bandpass filtering the original electric field signals collected at different directions; S3: Perform feature extraction processing on the filtered signal through fast Fourier transform, and extract the potential amplitude at the target characteristic frequency as the corresponding characteristic value of the electrode array; S4: Based on the least squares method and the corresponding eigenvalues ​​of the electrode array, the passive electric field positioning minimization objective function is constructed; S5, taking the minimized objective function as the fitness function, searching the target potential waters by improving the dung beetle optimization algorithm to obtain the estimated position and orientation of the target.

[0006] Preferably, the original signals of the multiple receiving electrodes arranged in the array are processed by a FIR filter. Filter the signals separately to obtain the filtered signals : .

[0007] Preferably, an electric field detection array is used to collect original electric field signals of underwater targets from different directions.

[0008] Preferably, the filtered signal is processed by fast Fourier transform to extract the potential amplitude at the target characteristic frequency. The specific steps are as follows: Perform fast Fourier transform on the filtered signals of each channel to obtain the spectrum of the signal; Extract the potential amplitude corresponding to the target characteristic frequency of each channel , is the actual position of the target.

[0009] Preferably, for the actual location of the target and search location , the error between the measured and theoretical potential disturbance values ​​on each receiving electrode is expressed as:

[0010] In the formula, For electrode The theoretical potential disturbance value on is calculated by the theoretical model; For electrode The measured potential disturbance value on .

[0011] The normalized spectrum function of the target position obtained by the least squares method is expressed as: .

[0012] Preferably, the normalized spectrum function of the target position obtained by the least squares method is used as the fitness function, and the optimal target estimated position is: .

[0013] An underwater passive electric field positioning system includes an electric field signal acquisition module, a filtering processing module, a feature extraction module, an objective function module and a positioning module; The electric field signal acquisition module collects the original electric field signals of underwater targets from different directions; The filtering processing module performs bandpass filtering on the original electric field signals collected from different directions; The feature extraction module performs feature extraction processing on the filtered signal through fast Fourier transform, and extracts the potential amplitude at the target characteristic frequency as the corresponding characteristic value of the electrode array; The objective function module constructs the passive electric field positioning minimization objective function based on the least squares method and the corresponding eigenvalues ​​of the electrode array; The positioning module takes the minimization objective function as the fitness function, searches for potential target waters through the improved dung beetle optimization algorithm, and obtains the estimated position and orientation of the target.

[0014] Preferably, the electric field signal acquisition module specifically uses an electric field detection array to collect original electric field signals of underwater targets from different directions.

[0015] Preferably, the filtered signal is processed by fast Fourier transform to extract the potential amplitude at the target characteristic frequency. The specific steps are as follows: Perform fast Fourier transform on the filtered signals of each channel to obtain the spectrum of the signal; Extract the potential amplitude corresponding to the target characteristic frequency of each channel , is the actual position of the target.

[0016] Preferably, for the actual location of the target and search location , the error between the measured and theoretical potential disturbance values ​​on each receiving electrode is expressed as:

[0017] In the formula, For electrode The theoretical potential disturbance value on is calculated by the theoretical model; For electrode The measured potential disturbance value on .

[0018] The normalized spectrum function of the target position obtained by the least squares method is expressed as: .

[0019] Compared with the prior art, the present invention has the following beneficial technical effects: The invention discloses an underwater passive electric field positioning method, which comprises the following steps: collecting original electric field signals of underwater targets from different directions; performing bandpass filtering on the original electric field signals collected from different directions; performing feature extraction on the filtered signals through fast Fourier transform, extracting the electric potential amplitude at the characteristic frequency of the target as the corresponding characteristic value of the electrode array; constructing a passive electric field positioning minimization objective function based on the least square method and obtaining the corresponding characteristic value of the electrode array, and aiming at the problem that the calculation amount is huge when performing target positioning in a potential water area of ​​a target with a large area, and the need for real-time positioning and tracking cannot be met, the target position normalized spectral function obtained by the least square method is used as the fitness function of the IDBO algorithm to optimize the target positioning, and the positioning speed is improved under the condition of ensuring the accuracy of the target positioning.

[0020] The present invention filters the original electric field signal through an equiripple FIR bandpass filter, so that the noise signal conforms to the Gaussian distribution, and the electric field signal meets the prerequisite of using the shaft frequency electric field signal for positioning, while making the signal purer and facilitating the extraction of the potential amplitude at the target characteristic frequency.

[0021] The passive electric field positioning algorithm based on LS in the present invention has higher positioning accuracy than array signal processing algorithms such as MUSIC. In view of the problems that the original DBO algorithm uses random numbers to generate the initial population, has low ergodicity and poor population diversity, the algorithm has better coverage and exploration and can accelerate the convergence speed of the algorithm.

[0022] In view of the problem that the rolling behavior of the dung beetle in the original DBO algorithm only depends on the global worst value and lacks connection with individuals in other populations, which easily leads to low global search ability and slow convergence speed, the Harris Eagle algorithm is introduced to replace the rolling behavior to improve the global optimization ability of the algorithm; in view of the problem that in the original DBO algorithm, if the position of the dung beetle exceeds the search boundary after iteration, the position is processed according to the boundary value, which makes the subsequent search invalid, the strategy of randomly initializing the position of the dung beetle when the position after iteration exceeds the search boundary is adopted, so as to enhance the individual search. Effectiveness: In order to address the problem that in the late iteration of the original DBO algorithm, the egg-laying dung beetles and small dung beetles quickly gather near the current optimal position, causing its search to stagnate, a perturbation strategy of Chebyshev chaotic mapping is adopted. That is, when the population falls into a local optimum and cannot jump out, the positions of the egg-laying dung beetles and small dung beetles are perturbed based on Chebyshev chaotic mapping to make them jump out of the local optimum, and at the same time "surround" the local optimum to avoid the current local optimum being the global optimum and being lost due to disturbance, thereby further enhancing the global search capability of the population.

[0023] In summary, compared with the existing underwater passive electric field positioning algorithm, this method is based on LS and IDBO optimization algorithms for target positioning, with higher positioning accuracy and faster speed. Compared with the existing methods, this method improves the original DBO algorithm with targeted multi-strategy based on the actual application characteristics of underwater passive electric field positioning, and uses the normalized spectral function obtained by the least squares method as the fitness function, which is more accurate and efficient in the passive electric field positioning of underwater vehicles in large waters. This method has good application prospects in the passive electric field positioning and tracking of underwater targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 4 is a flow chart of the underwater passive electric field positioning method in an embodiment of the present invention.

[0025] Figure 2 This is a flow chart of the IDBO algorithm in an embodiment of the present invention.

[0026] Figure 3 Schematic diagram of population two-dimensional position initialization based on Sobol sequence in an embodiment of the present invention.

[0027] Figure 4Schematic diagram of a two-dimensional position based on Chebyshev chaotic mapping in an embodiment of the present invention.

[0028] Figure 5 Schematic diagram of a target positioning simulation detection scenario in an embodiment of the present invention.

[0029] Figure 6 Graphs showing the original distribution of electrode noise and the distribution after filtering in an embodiment of the present invention.

[0030] Figure 7 It is the time domain and frequency domain diagram of the original signal of the electrode sensor in the embodiment of the present invention.

[0031] Figure 8 It is the time domain and frequency domain diagram of the electrode sensor filtering signal in the embodiment of the present invention.

[0032] Fig. 9 is a comparison diagram of DBO and IDBO positioning results in an embodiment of the present invention, DETAILED DESCRIPTION In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0034] like Figure 1 As shown, the present invention provides an underwater passive electric field positioning method, which specifically includes the following steps: S1, using the electric field detection array to collect the original electric field signals of underwater targets from different directions; S2: performing bandpass filtering on the original electric field signal collected by the electric field detection array through an equiripple FIR bandpass filter; S3: Perform feature extraction processing on the filtered signal through fast Fourier transform (FFT), and extract the potential amplitude at the target characteristic frequency as the corresponding characteristic value of the electrode array; S4: Based on the least squares method and the corresponding eigenvalues ​​of the electrode array, the passive electric field positioning minimization objective function is constructed; S5, taking the minimized objective function as the fitness function, searching the target potential waters by improving the dung beetle optimization algorithm to obtain the estimated position and orientation of the target.

[0035] In a specific implementation manner of the present application, the specific steps of performing bandpass filtering on the original electric field signal of each receiving electrode by using an equiripple FIR bandpass filter as described in S2 are as follows: (2-1) Determine the values ​​of the parameters of the FIR filter: the first stopband frequency , the first passband frequency , the second passband frequency , the second stopband frequency , the first stopband attenuation rate , the second stopband attenuation rate , passband ripple , density coefficient ; (2-2) The original signals of multiple receiving electrodes arranged in the array are filtered through the FIR filter. Filter the signals separately to obtain the filtered signals :

[0036] Furthermore, the specific steps of processing the filtered signal by fast Fourier transform (FFT) described in S3 to extract the potential amplitude at the target characteristic frequency are as follows: (3-1) Perform fast Fourier transform on the filtered signals of each channel to obtain the signal spectrum; (3-2) Extract the potential amplitude corresponding to the target characteristic frequency of each channel , is the actual position of the target.

[0037] Furthermore, the specific steps of constructing the passive electric field positioning minimization objective function based on the least squares method (LS) described in S4 are as follows: (4-1) For the actual position of the target and search location , the error between the measured and theoretical potential disturbance values ​​on each receiving electrode is expressed as:

[0038] In the formula, For electrode The theoretical potential disturbance value on is calculated by the theoretical model; For electrode The measured potential disturbance value on .

[0039] (4-2) The normalized spectrum function of the target position obtained by the least squares method is expressed as:

[0040] As described in S5, the minimization objective function is used as the fitness function, and the target potential water area is searched by improving the dung beetle optimization algorithm, such as Figure 2 As shown, the specific steps for outputting the estimated position and orientation of the target are as follows: (5-1) The normalized spectral function of the target position obtained by the least squares method is used as the fitness function, and the optimal target estimated position is:

[0041] (5-2) Setting IDBO algorithm related parameters: population size , the number of rolling dung beetles , the number of dung beetles laying eggs , the number of small dung beetles , stealing dung beetle populations , the dimension of the positioning optimization problem , locate the optimization problem boundary , population iteration number ; (5-2) Based on Sequence random number Initialize the population position, such as Figure 3 As shown: (5-3) Calculate the fitness values ​​of all dung beetles; (5-4) Update the position of the rolling dung beetle:

[0042] In the formula, For a random ball-rolling dung beetle in The position at the iteration; is the average position of all ball-rolling dung beetles; , , , , All belong to A random number, Providing diverse trends to the search so that it can explore different regions of the feature space, The closer it is to 1, the more random the search strategy is; (5-5) When the ball-rolling dung beetle encounters an obstacle and cannot move forward, it adjusts its direction by dancing. In DBO, the tangent function is used to simulate the dancing behavior of the dung beetle and obtain a new route:

[0043] In the formula, is the deflection angle, when When , the tangent value of the deflection angle is 0, and the position of the rolling dung beetle is not updated in this iteration; (5-6) Check the boundary of the rolling dung beetle's position. If it exceeds the boundary, update the position randomly to:

[0044] In the formula, The size is A random vector of (5-7) Calculate the fitness value of the rolling dung beetle and update the local optimal solution and the global optimal solution ; (5-8) Determine whether it is trapped in the local optimum. If so, update the positions of the egg-laying dung beetles and the small dung beetles based on the Chebyshev chaotic map, such as Figure 4 As shown:

[0045] If not, update normally as follows; (5-9) Updated the location of dung beetle eggs:

[0046]

[0047] In the formula, It is the lower boundary of the spawning area; It is the upper boundary of the spawning area; is the current local optimal position; is the lower bound of the optimization problem; is the upper bound of the optimization problem; , is the maximum number of iterations; For the The breeding ball is in The position at the iteration; For two independent sizes A random vector of (5-10) Updated the location of the dung beetle:

[0048]

[0049] In the formula, For the A dung beetle in the The position at the iteration; is a random number that follows a normal distribution; For A random variable of It is the lower boundary of the spawning area; It is the upper boundary of the spawning area; is the current local optimal position; (5-11) Updated the location of the stealing dung beetle:

[0050] In the formula, For the Only the thieving dung beetle is The position at the iteration; is a constant; is a normal distribution with a size of A random vector of (5-12) Check the boundaries of the positions of egg-laying dung beetles, small dung beetles, and thieving dung beetles. If they are beyond the boundaries, the positions are updated randomly to:

[0051] (5-13) Calculate the fitness values ​​of egg-laying dung beetles, small dung beetles, and stealing dung beetles, and update the global optimal solution; (5-14) Repeat (5-4) to (5-13) until the number of iterations reaches .

[0052] Example In order to verify the advantages of the underwater passive electric field positioning method based on the IDBO algorithm adopted by the present invention, as Figure 5 As shown in FIG. 1 , this method uses an electrode detection array to collect 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 original distribution and filtered distribution of electrode noise are shown in the figure. The target positioning experiment is carried out using the LS-based IDBO algorithm to verify the performance of the proposed target positioning method. Figure 7 The time domain and frequency domain diagrams of the original signal of the electrode sensor shown; Figure 8 The figure on the left in the middle shows the target positioning result obtained by the original DBO algorithm. Its running time is 72.2298s, and the average ReRMSE of the positioning result is 0.1170; Figure 8The figure on the right shows the target positioning result obtained by the IDBO algorithm. Its running time is 72.2716s, and the average ReRMSE of the positioning result is 0.0861. Its positioning error is reduced by about 26.41% compared with the original DBO algorithm. Fig. 9 As shown in the figure, the results show that the underwater passive electric field positioning method based on the IDBO algorithm has higher accuracy.

Claims

1. An underwater passive electric field positioning method, characterized in that: The following steps are involved: S1, collects the original electric field signals of underwater targets from different directions; S2: bandpass filtering the original electric field signals collected at different directions; S3: Perform feature extraction processing on the filtered signal through fast Fourier transform, and extract the potential amplitude at the target characteristic frequency as the corresponding characteristic value of the electrode array; S4: Based on the least squares method and the corresponding eigenvalues ​​of the electrode array, the passive electric field positioning minimization objective function is constructed; S5, taking the minimized objective function as the fitness function, searching the target potential waters by improving the dung beetle optimization algorithm to obtain the estimated position and orientation of the target.

2. The underwater passive electric field positioning method according to claim 1, characterized in that: The original signals of multiple receiving electrodes set in the array are filtered by FIR filter. Filter the signals separately to obtain the filtered signals : 。 3. The underwater passive electric field positioning method according to claim 1, characterized in that: The electric field detection array is used to collect the original electric field signals of underwater targets from different directions.

4. The underwater passive electric field positioning method according to claim 1, characterized in that: The filtered signal is processed by fast Fourier transform to extract the potential amplitude at the target characteristic frequency. The specific steps are as follows: Perform fast Fourier transform on the filtered signals of each channel to obtain the spectrum of the signal; Extract the potential amplitude corresponding to the target characteristic frequency of each channel , is the actual position of the target.

5. The underwater passive electric field positioning method according to claim 1, characterized in that: For the actual location of the target and search location , the error between the measured and theoretical potential disturbance values ​​on each receiving electrode is expressed as: In the formula, For electrode The theoretical potential disturbance value on is calculated by the theoretical model; For electrode The measured potential disturbance value on ; The normalized spectrum function of the target position obtained by the least squares method is expressed as: 。 6. The underwater passive electric field positioning method according to claim 1, characterized in that: The normalized spectral function of the target position obtained by the least squares method is used as the fitness function, and the optimal target estimated position is: 。 7. An underwater passive electric field positioning system, characterized in that: It includes electric field signal acquisition module, filtering processing module, feature extraction module, target function module and positioning module; The electric field signal acquisition module collects the original electric field signals of underwater targets from different directions; The filtering processing module performs bandpass filtering on the original electric field signals collected from different directions; The feature extraction module performs feature extraction processing on the filtered signal through fast Fourier transform, and extracts the potential amplitude at the target characteristic frequency as the corresponding characteristic value of the electrode array; The objective function module constructs the passive electric field positioning minimization objective function based on the least squares method and the corresponding eigenvalues ​​of the electrode array; The positioning module takes the minimization objective function as the fitness function, searches for potential target waters through the improved dung beetle optimization algorithm, and obtains the estimated position and orientation of the target.

8. The underwater passive electric field positioning system according to claim 7, 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.

9. The underwater passive electric field positioning system according to claim 7, characterized in that: The filtered signal is processed by fast Fourier transform to extract the potential amplitude at the target characteristic frequency. The specific steps are as follows: Perform fast Fourier transform on the filtered signals of each channel to obtain the spectrum of the signal; Extract the potential amplitude corresponding to the target characteristic frequency of each channel , is the actual position of the target.

10. The underwater passive electric field positioning system according to claim 7, characterized in that: For the actual location of the target and search location , the error between the measured and theoretical potential disturbance values ​​on each receiving electrode is expressed as: In the formula, For electrode The theoretical potential disturbance value on is calculated by the theoretical model; For electrode The measured potential disturbance value on ; The normalized spectrum function of the target position obtained by the least squares method is expressed as: 。

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