Underwater target shaft frequency magnetic field detection method and system based on inductive sensor array

Abstract

The invention discloses an underwater target axis frequency magnetic field detection method based on an inductive sensor array. The method comprises the following steps: setting a first sensor array and a second sensor array to capture a first signal and a second signal; performing preprocessing and noise and interference line spectrum suppression on the first signal and the second signal to obtain a detection signal, a reference signal and a target signal; inputting the target signal into an intermittent chaotic oscillator system to obtain a time domain diagram output by the intermittent chaotic oscillator system, judging a target line spectrum in the target signal according to the time domain diagram, and obtaining an internal driving frequency of an intermittent chaotic oscillator in the state; and performing Hilbert envelope analysis to obtain frequency spectrum information of the target signal, and analyzing line spectrum information of the target signal. According to the technical scheme of the invention, the interference existing in the detection signal can be suppressed; the signal-to-noise ratio of the target signal is increased by more than 20dB under the condition of extremely low signal-to-noise ratio; and detection of long-distance shaft frequency magnetic field signals can be realized, and high accuracy and low false alarm rate are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of weak signal amplification and underwater target detection, and in particular to a method and system for underwater target shaft frequency magnetic field detection based on an inductive sensor array. Background Art

[0002] The shaft frequency magnetic field is an extremely low frequency alternating magnetic field generated by the periodic change of the contact resistance of the hull corrosion circuit caused by the rotation of the propeller of the underwater target, which modulates the corrosion current and the protection current. The period of the shaft frequency magnetic field is highly correlated with the propeller rotation frequency of the underwater target. It contains rich ship characteristics in both the time domain and the frequency domain, and can provide an effective and reliable basis for the detection, identification, positioning and tracking of moving underwater targets. The shaft frequency magnetic field is an extremely low frequency magnetic field, and the attenuation effect of seawater on it during the propagation process is weak, so the shaft frequency magnetic field can be detected at a long distance. However, the magnetic field environment of the marine environment is complex, especially in the complex magnetic field environment near the coast, the shaft frequency signal is often submerged in the noise, and the signal-to-noise ratio is extremely low.

[0003] At present, shaft frequency magnetic field detection mainly relies on linear algorithms, including Fourier transform, bandpass filtering, Welch power spectrum analysis and wavelet transform, etc., which cannot achieve accurate detection under low signal-to-noise ratio conditions. In response to such problems, the existing technologies have proposed sliding power spectrum estimation, phase-locked amplification technology and correlation detection, etc. For example, the method of using wavelet modulus maximum to denoise the shaft speed electric field has improved the performance of the detection algorithm; however, the linear algorithm is not robust to non-Gaussian noise. In response to such situations, decomposition methods such as empirical mode decomposition are used to effectively screen the intrinsic mode function and use high-order cumulants to suppress Gaussian noise and then extract the shaft frequency electromagnetic field signal, but the detection effect is limited in an environment where the signal-to-noise ratio is lower than -5dB. In addition, the existing schemes propose methods such as random resonance, adaptive filtering, and modal decomposition. However, the existing nonlinear detection methods have more or less problems such as modal aliasing, poor resolution, and fuzzy judgment criteria.

[0004] Therefore, in view of the defects of the above-mentioned known solutions, in order to achieve high-precision detection of the shaft-frequency magnetic field of underwater targets, it is urgent to find a method and system for detecting the shaft-frequency magnetic field of underwater targets based on an inductive sensor array to solve the above problems. The present invention uses the node structure design of the inductive sensor array, the original signal collected after the deployment position optimization as the target signal, and performs target signal preprocessing, noise and interference line spectrum suppression, intermittent chaotic oscillator system detection, and Hilbert envelope spectrum analysis, especially in the noise and interference line spectrum suppression link, by performing multi-window spectrum subtraction and whitening filtering on different node signals, the interference in the detection signal is suppressed; and the signal-to-noise ratio of the target signal is increased by more than 20dB under low signal-to-noise ratio conditions; finally, the success rate of shaft-frequency magnetic field signal detection is achieved and the number of false alarms is reduced. Summary of the invention

[0005] In view of the defects and improvement needs of the prior art, the present invention provides a method for detecting the shaft frequency magnetic field of an underwater target based on an inductive sensor array, which is characterized by comprising:

[0006] S1, setting a first sensor array and a second sensor array in an underwater target water area to capture a first signal and a second signal;

[0007] S2, after preprocessing the first signal and the second signal, suppressing the noise and interference line spectra in the first signal and the second signal to obtain a detection signal, a reference signal, a differential signal, and a target signal;

[0008] S3, inputting the target signal into the intermittent chaotic oscillator system to obtain a time domain diagram of the output of the intermittent chaotic oscillator system, and judging the target line spectrum in the target signal according to the time domain diagram and obtaining the internal driving frequency of the intermittent chaotic oscillator in this state; and

[0009] S4, using Hilbert envelope to analyze the time domain diagram of the intermittent chaotic oscillator system output and obtain its spectrum information, and combining the internal driving frequency of the intermittent chaotic oscillator in this state to analyze the line spectrum information of the target signal.

[0010] Preferably, the first sensor array and the second sensor array constitute a first detection node and a second detection node, each of which includes three inductive sensors, and the inductive sensors respectively constitute the X, Y, and Z axes of the Cartesian coordinate system of the detection node with the center of gravity of the detection node as the origin, and the inductive sensors are fixed in a fixed bracket, and an inertial navigation device is installed on the fixed bracket.

[0011] Preferably, the step of preprocessing the first signal and the second signal comprises:

[0012] S201, removing outliers in the first signal and the second signal;

[0013] S202, removing the DC amount in the first signal and the second signal;

[0014] S203, converting the first signal and the second signal in the carrier coordinate system into signal expressions in a geographic coordinate system; and

[0015] S204: Band-pass filter the first signal and the second signal in the geographic coordinate system to complete preprocessing.

[0016] Preferably, the method for removing outliers of the first signal and the second signal in step S201 is:

[0017] Calculate the first quartile Q1 and the third quartile Q3 in the signal;

[0018] Calculate the interquartile range (IQR) of the signal and set lower and upper thresholds for outliers; and replace outliers with the previous non-outlier value.

[0019] Preferably, the step S203 specifically includes:

[0020] Read the yaw angle φ output by the inertial navigation system bn , pitch angle θ bn and the roll angle ψ bn ;

[0021] Rotate φ around the Z axis of the carrier coordinate system bn The angle gets the transition coordinate system ψ;

[0022] Rotate θ around the Y axis of the transition coordinate system ψ bn The angle obtains the transition coordinate system θ; and

[0023] Rotate ψ around the X axis of the transition coordinate system Θ bn The angle is used to obtain a geographic coordinate system, thereby acquiring the first signal and the second signal in the geographic coordinate system.

[0024] Preferably, the bandpass filtering step in step S204 is as follows:

[0025] Set the passband range of the bandpass filter to [0.130] Hz;

[0026] The bandpass filter is a second-order Butterworth bandpass filter, which is used to filter out power frequency electromagnetic field noise and geomagnetic field noise in the first signal and the second signal, wherein the transfer function H(s) of the second-order Butterworth bandpass filter is expressed as follows:

[0027]

[0028] Among them, ω0 is the center circle frequency and Q is the quality factor.

[0029] Preferably, the step of suppressing noise and interference line spectra in the first signal and the second signal in step S2 comprises:

[0030] S205, windowing the first signal and the second signal using a Hamming window;

[0031] S206, performing Fourier transform on each window of the windowed signal to extract the frequency spectrum and phase spectrum of the first signal and the second signal;

[0032] S207, differentiating the frequency spectra of the windowed signals of the first signal and the second signal, and identifying a detection signal, a reference signal, and a differential signal according to the differential result; and

[0033] S208: Acquire an interference line spectrum suppression signal using the phase spectrum of each window of the detection signal and the differential signal spectrum.

[0034] Preferably, the Hamming window splitting formula in step S205 is as follows:

[0035]

[0036] Where w(n) is the Hamming window function, N is the number of windows, and a0 is the Hamming window coefficient.

[0037] Preferably, the method of extracting the spectrum and phase spectrum by discrete Fourier transform in step S206 is as follows:

[0038] The frequency domain sequence after discrete Fourier transform is as follows:

[0039]

[0040] Wherein x(n) is a discrete time domain sequence of the first signal or the second signal, X(k) is a frequency domain sequence after discrete Fourier transform, N is the length of the sequence, k represents a discrete frequency index, and n is a discrete time index;

[0041] The discrete-time Fourier transform method to extract the phase spectrum is as follows:

[0042]

[0043] Where Im represents the imaginary part of the complex number, and Re represents the real part of the complex number.

[0044] Preferably, in step S207, the frequency spectra of the detection signal and the reference signal after windowing are differentiated, and the expression is as follows:

[0045]

[0046] where x k (n) is the window data after windowing, w(n) is the Hamming window function, x k1 (ω) is the spectrum data of each window of the detection signal, x k2 (ω) is the spectrum data of each window of the reference signal, Y k (ω) is the spectrum of the k-th window after subtraction, which is recorded as the k-th window difference signal in the difference signal.

[0047] Preferably, the specific steps of step S208 are as follows:

[0048] The spectrum data of each window of the differential signal is averaged, and the calculation formula is as follows:

[0049]

[0050] Where, M is the number of windows;

[0051] The method of obtaining the interference line spectrum suppression signal according to the phase spectrum of each window of the detection signal and the spectrum of the differential signal is as follows:

[0052]

[0053] Wherein, y(n) is the interference line spectrum suppression signal, and N is the sequence length.

[0054] Preferably, the step of suppressing the noise and interference line spectra in the first signal and the second signal in step S2 further includes whitening filtering and variational mode decomposition, and the steps are as follows:

[0055] S209, inputting the interference line spectrum suppression signal into a whitening filter to obtain a whitening filtered signal, wherein the expression of the whitening filter is:

[0056]

[0057] Where P is the model order, a k is the coefficient of the autoregressive model, z is the complex variable in the Z transformation;

[0058] S210, performing variational modal decomposition on the whitened filtered signal, and updating and decomposing the mode and frequency of the whitened filtered signal, wherein the objective function expression is:

[0059]

[0060] Where K is the number of decomposed modes, u k is the kth mode, f k is the frequency corresponding to the kth mode,

[0061] α is the penalty factor, the number of decomposed modes K is set to 8, and the penalty factor α is set to 9500;

[0062] S211, after the objective function reaches a preset convergence condition, extracting each modal component of the whitening filter signal and the frequency corresponding to each modal component; and

[0063] S212, selecting a modal component containing a target signal from each of the modes to obtain a denoised target signal.

[0064] Preferably, the step S3 further includes:

[0065] S31, determining the driving force of the intermittent chaotic oscillator system, wherein the intermittent chaotic model is:

[0066] y″+ky′-ay+by 3 =γcos(ωt)+s(t)

[0067] Where k is the damping coefficient, -ax+bx 3 is the nonlinear dynamic term, γ is the internal driving force, y is the output of intermittent chaos, s(t) is the target signal, and ω is the internal driving frequency; and

[0068] S32, changing the solution step size of the intermittent chaotic oscillator system and the size of the internal driving force, detecting the target signal, wherein when the target signal contains a frequency difference with the internal driving frequency that is less than 0.08ω, intermittent chaos will occur in the system.

[0069] Preferably, the step S4 specifically includes:

[0070] S41: Performing Hilbert envelope analysis on the time domain diagram of the intermittent chaotic oscillator system output, wherein the Hilbert transform expression is as follows:

[0071]

[0072] The Hilbert envelope expression is as follows:

[0073]

[0074] S42: Performing Fourier transform on the Hilbert envelope;

[0075] S43: Analyze the detection result of the target signal according to the spectrum diagram, wherein when a line spectrum appears in the spectrum of the Hilbert envelope within 0.08ω, calculate the energy ratio of the line spectrum relative to the line spectrum of other frequencies, and if the energy ratio exceeds 10dB, determine that the intermittent chaotic oscillator system detects the target signal.

[0076] The present invention also discloses an underwater target shaft frequency magnetic field detection system based on an inductive sensor array, which is characterized by comprising:

[0077] A signal acquisition unit, used to acquire shaft frequency magnetic field signals of underwater targets, including a first signal and a second signal;

[0078] A signal processing unit, configured to process the first signal and the second signal according to the method shown in step S2 above;

[0079] A signal detection unit, used to detect the target signal according to the method shown in step S3 above; and

[0080] The signal analysis unit is used to analyze the target signal according to the method shown in step S4 above.

[0081] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects:

[0082] (1) It can suppress the interference in the detection signal;

[0083] (2) Improve the signal-to-noise ratio of the target signal by more than 20 dB under extremely low signal-to-noise ratio conditions;

[0084] (3) It can detect long-distance axial frequency magnetic field signals with high accuracy and low false alarm rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 The figure is a flow chart of a method for detecting shaft frequency magnetic field of an underwater target based on an inductive sensor array according to an embodiment of the present invention;

[0086] Figure 2 The figure is a flow chart of signal processing in a method for detecting shaft frequency magnetic field of an underwater target based on an inductive sensor array according to an embodiment of the present invention;

[0087] Figure 3 It is a flow chart of signal detection in a method for detecting shaft frequency magnetic field of an underwater target based on an inductive sensor array according to an embodiment of the present invention;

[0088] Figure 4 It is a flow chart of signal analysis in a method for detecting shaft frequency magnetic field of an underwater target based on an inductive sensor array according to an embodiment of the present invention;

[0089] Figure 5 The figure shows a module diagram of an underwater target shaft frequency magnetic field detection system based on an inductive sensor array according to an embodiment of the present invention;

[0090] Figure 6 Shown is a diagram of a fixing bracket for fixing the inductive sensor array used in the experiment;

[0091] Figure 7 The figure shows the time domain diagram and spectrum diagram of the target signal when the target signal contains the shaft frequency signal in the experiment;

[0092] Figure 8 Shown are the time domain diagram of the target signal and the spectrum diagram of the target signal after the target signal in the experiment is processed in the embodiment disclosed by the present invention;

[0093] Fig. 9The figure shows the time domain diagram of the intermittent chaotic system output and the linear amplitude spectrum of the Hilbert envelope when the target signal is input into the intermittent chaotic oscillator system and analyzed by the Hilbert envelope in the experiment;

[0094] Fig.10 Shown is a table of detection errors in the experiment; and

[0095] Fig.11 Shown is the statistical table of false alarm rate in the experiment; DETAILED DESCRIPTION

[0096] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0097] In the present invention, the terms "first", "second", etc. (if any) in the present invention and the accompanying drawings are used to distinguish similar objects but not necessarily to describe a specific order or sequence.

[0098] Figure 1 The figure shows a flow chart of a method for detecting shaft-frequency magnetic field of underwater targets based on an inductive sensor array according to an embodiment of the present invention. In shaft-frequency magnetic field detection, the background noise is mainly composed of environmental magnetic field noise such as geological noise, daily noise and industrial frequency electromagnetic field noise of the geomagnetic field. Compared with environmental noise, shaft-frequency magnetic field signals are often weaker. Figure 1 As shown, the underwater target shaft frequency magnetic field detection method disclosed in the present invention comprises the following steps:

[0099] Step S1: A first sensor array and a second sensor array are set in the underwater target water area to capture the first signal and the second signal. Specifically, two detection points are set in the water area where the shaft frequency magnetic field is to be captured, and each detection point is arranged with an inductive sensor array, one of which is the first sensor array and the other is the second sensor array. The shaft frequency magnetic field signal captured by the first sensor array is called the first signal, and the shaft frequency magnetic field signal captured by the second sensor array is the second signal, wherein the first signal and the second signal are both original signals containing the target magnetic field signal and the noise signal. Usually the original signal is recorded as x(t), the target magnetic field signal is recorded as s(t), the noise signal is recorded as n(t), t is the observation time, and x(t)=s(t)+n(t).

[0100] Furthermore, the sensor array in each of the above-mentioned detection points includes three inductive sensors, and the inductive sensors respectively constitute the X, Y, and Z axes of the Cartesian coordinate system of the detection node with the center of gravity of the detection node as the origin. The inductive sensors are fixed in a fixed bracket, and an inertial navigation device is installed on the fixed bracket, wherein the first sensor array and the second sensor array are also referred to as the first detection node and the second detection node.

[0101] Step S2: After preprocessing the first signal and the second signal, noise and interference line spectra in the first signal and the second signal are suppressed to obtain a detection signal, a reference signal, a differential signal, and a target signal.

[0102] Figure 2 The figure shows a flow chart of signal processing in a method for detecting an underwater target shaft frequency magnetic field based on an inductive sensor array according to an embodiment of the present invention, that is, a specific flow chart of signal processing in step S2.

[0103] Specifically, the first signal and the second signal are preprocessed as follows:

[0104] S201, remove outliers in the first signal and the second signal; in one embodiment, first calculate the first quartile Q1 and the third quartile Q3 of each data, then calculate the interquartile range IQR, and set a threshold for the outlier, for example, 1.5 times the IQR is used as the fluctuation value, where the lower limit of the threshold is Q1-1.5×IQR, and the upper limit of the threshold is Q3+1.5×IQR, and values ​​below the lower limit or above the upper limit are regarded as outliers, and finally replace the outlier with the previous non-outlier value, where the previous non-outlier value refers to the value that is arranged before the outlier in the original data.

[0105] S202, removing the DC amount in the first signal and the second signal; specifically, after completing step S201, removing the DC noise in the first signal and the second signal to filter out the influence of the DC magnetic field noise in the environment.

[0106] S203, convert the first signal and the second signal in the carrier coordinate system into signal expressions in the geographic coordinate system; further, perform coordinate transformation processing on the first signal and the second signal after completing the processing in step S202. In the embodiment of the present invention, since the posture and direction of the first detection node and the second detection node cannot be kept completely consistent when the first detection node and the second detection node are deployed, in order to maintain the accuracy of the data, the first signal and the second signal collected by the two detection nodes, i.e., the first detection node and the second detection node, are uniformly converted to the geographic coordinate system. Specifically, the original coordinate system is called the carrier coordinate system, and the formula for converting the carrier coordinate system to the geographic coordinate system is as follows:

[0107]

[0108] in, It is the data of three single-axis inductive sensors in the detection node in the carrier coordinate system, the coordinate values ​​of x, y, and z axes, is the coordinate value of x, v, z axis in the geographic coordinate system after conversion, where

[0109] is the rotation matrix from the carrier coordinate system to the geographic coordinate system, where φ bn ,θ bn , bn They are the yaw angle, pitch angle and roll angle output by the attitude sensor of the detection node, for example, the yaw angle φ is obtained by the inertial navigation device installed on the fixed bracket of the fixed sensor array bn , pitch angle θ bn and the roll angle ψ bn , specifically, the rotation around the Z axis of the carrier coordinate system is φ bn The angle is used to obtain the transition coordinate system ψ; the Y axis of the transition coordinate system ψ is rotated by θ bn The angle is used to obtain the transition coordinate system Θ; and the rotation ψ around the X axis of the transition coordinate system Θ bn The angle obtains the geographic coordinate system, thereby obtaining the expressions of the first signal and the second signal in the geographic coordinate system.

[0110] S204, bandpass filter the first signal and the second signal in the geographic coordinate system to complete preprocessing. In one embodiment, after the first signal and the second signal in the carrier coordinate system are converted to the geographic coordinate system, the converted signals are bandpass filtered. According to the distribution of the frequency band of the typical underwater background magnetic field noise, in the embodiment of the present invention, the filter is a second-order Butterworth bandpass filter, and the passband range of the filter is set to [0.130] Hz, wherein the transfer function H(s) of the second-order Butterworth bandpass filter is expressed as follows:

[0111]

[0112] Wherein, ω0 is the center circle frequency, Q is the quality factor, and in one embodiment of the present invention, the center circle frequency ω0 is set to 94.561937 Hz, and the quality factor Q is set to 1.00668896. After the first signal and the second signal are input into the second-order Butterworth bandpass filter, the filter can filter out the environmental magnetic field noise of the power frequency electromagnetic field and the geomagnetic field.

[0113] S205, the first signal and the second signal are windowed using a Hamming window, where the first signal and the second signal are signals obtained after filtering in step S204, and the signals in each step of the present invention are based on the results of the previous processing, and in order to avoid redundancy, the first signal and the second signal after processing are no longer emphasized in the subsequent steps. The first signal and the second signal are windowed and framed, specifically, the signal is windowed by applying a Hamming window, and the windowing formula is as follows:

[0114]

[0115] w(n) is the Hamming window function, N is the number of windows, and a0 is the Hamming window coefficient.

[0116] S206, performing Fourier transform on each window of the windowed signal to extract the frequency spectrum and phase spectrum of the first signal and the second signal; the specific formula for performing Fourier transform on the signal to obtain the frequency spectrum is as follows:

[0117]

[0118] Where x(n) is the discrete time domain sequence of the input signal, which can be the discrete time domain sequence of the first signal or the second signal, X(k) is the frequency domain sequence after the discrete Fourier transform, N is the length of the sequence, k represents the discrete frequency index, and n is the discrete time index. Further, the formula for obtaining the phase spectrum by Fourier transforming the signal is as follows:

[0119]

[0120] Where Im represents the imaginary part of the frequency domain sequence X(k), and Re represents the real part of the frequency domain sequence X(k).

[0121] S207, differentiate the frequency spectrum of the windowed signal of the first signal and the second signal, and identify the detection signal, the reference signal and the differential signal according to the differential result. In an embodiment of the present invention, after the frequency spectrum of the windowed signal of the first signal and the second signal is differentiated, if the differential result shows that there is line spectrum information, the first signal is the detection signal and the second signal is the reference signal, otherwise the first signal is the reference signal and the second signal is the detection signal, wherein the differential signal is the frequency spectrum of the detection signal minus the frequency spectrum of the reference signal. The method of selecting the detection signal and the reference signal from the first signal and the second signal is a conventional method in the art, and will not be described in detail to avoid redundancy. Furthermore, the formula for differential spectrum of the detection signal and the reference signal after windowing is as follows:

[0122]

[0123] Among them, x k(n) is the window data after windowing, w(n) is the Hamming window function, x(n) is the input signal, such as the detection signal or the reference signal, x k1 (ω) is the spectrum data of each window of the detection signal, x k2 (ω) is the spectrum data of each window of the reference signal, Y k (ω) is the spectrum after subtraction of the kth window, recorded as the kth window difference signal in the difference signal.

[0124] S208, using the phase spectrum of each window of the detection signal and the differential signal spectrum to obtain the interference line spectrum suppression signal. Further, the average value of the spectrum of each window of the differential signal is obtained and recorded as the spectrum estimation with a smaller error. The calculation formula is as follows:

[0125]

[0126] Where M is the number of windows.

[0127] Furthermore, the phase spectrum and frequency spectrum of each window of the detection signal are estimated Get the interference line spectrum suppression signal, the calculation formula is as follows:

[0128]

[0129] Where y(n) is the interference line spectrum suppression signal, is the spectrum estimation, and N is the number of windows.

[0130] S209, inputting the interference line spectrum suppression signal into a whitening filter to obtain a whitening filtered signal, wherein the expression of the whitening filter is:

[0131]

[0132] Where P is the model order, a k is the autoregressive model coefficient, z is the complex variable in the Z transform, the interference line spectrum suppression signal y(n) is input into the AR whitening filter, and the output signal W(n) with a power spectrum density closer to white noise is obtained, which is recorded as the whitening filtered signal.

[0133] S210, performing variational modal decomposition on the whitened filtered signal W(n), and updating the mode and frequency of the decomposed whitened filtered signal, wherein the objective function expression is:

[0134]

[0135] Where K is the number of decomposed modes, u k is the kth mode, f kis the frequency corresponding to the kth mode, α is the penalty factor, the number of decomposed modes K is set to 8, and the penalty factor α is set to 9500. Specifically, after determining the number of modes K and the penalty factor α of variational mode decomposition, the whitened filtered signal W(n) can be input into the objective function of variational mode decomposition, and the Lagrange multiplier is introduced. The alternating direction multiplier method is used to iteratively solve the objective function and update the decomposed modes and frequencies.

[0136] S211, when the objective function reaches the preset convergence condition, extract each modal component of the whitened filter signal W(n) and the frequency corresponding to each modal component. Specifically, after the objective function reaches the set convergence condition in the cyclic iteration process, the iteration can be stopped, and each mode and its corresponding frequency can be extracted from the optimization result. The convergence condition here can be set according to experience.

[0137] S212, select the modal component containing the target signal from each mode, and obtain the target signal after noise reduction. Specifically, when the iteration stops, select the modal component containing the target signal from each mode, complete the suppression of the background magnetic field noise and the environmental interference line spectrum in the original detection signal, and obtain the target signal after noise reduction. The target signal here refers to the detection signal after the processing of step S2, that is, the signal representing the underwater target position or information in the axial frequency magnetic field of the underwater target extracted from the detection signal. After the target signal is obtained by the method disclosed in step S2, the target signal is further detected to verify the quality of the target signal.

[0138] S3: Input the target signal into the intermittent chaotic oscillator system to obtain the time domain diagram of the intermittent chaotic oscillator system output, and determine the target line spectrum in the target signal based on the time domain diagram and obtain the internal driving frequency of the intermittent chaotic oscillator in this state. Step S3 is to detect the target signal.

[0139] S4: Use Hilbert envelope to analyze the time domain diagram of the intermittent chaotic oscillator system output and obtain its spectrum information, and analyze the line spectrum information of the target signal in combination with the internal driving frequency of the intermittent chaotic oscillator in this state. Step S4 is to analyze the target signal.

[0140] Figure 3 The figure is a flow chart of signal detection in a method for detecting an underwater target shaft frequency magnetic field based on an inductive sensor array according to an embodiment of the present invention. Figure 1 Detailed step diagram of step S3 in FIG. Figure 3 Will combine Figure 1 To describe, the specific steps in step S3 are as follows:

[0141] S31, determine the driving force of the intermittent chaotic oscillator system, where the intermittent chaotic oscillator model is:

[0142] y″+ky′=ay+by3 =γcos(ωt)+s(t)

[0143] Where k is the damping coefficient, -ax+bx 3 is the nonlinear dynamic term, γ is the internal driving force, y is the output of intermittent chaos, s(t) is the target signal, and ω is the internal driving frequency.

[0144] In one embodiment of the present invention, the method for determining the internal driving force γ is as follows: first, the threshold of the intermittent chaotic oscillator system is determined. In the absence of an input signal, the expression of the intermittent chaotic oscillator system is:

[0145] x″+kx′-ax+bx 3 =γcos(ωt)

[0146] Where k is the damping coefficient, -ax+bx 3 is a nonlinear dynamic term, γ is the internal driving force, x is the output of intermittent chaos, and ω is the internal driving frequency. In the absence of input, the parameters are determined by calculating the multiscale entropy of the output of intermittent chaos with different parameters, where the calculation formula of the multiscale entropy is as follows:

[0147]

[0148] Among them, SampleEn is the sample entropy, m is the embedding dimension, r is the similarity tolerance, N is the data length, and τ is the time scale. The state of the output of the intermittent chaotic oscillator system is measured by multi-scale entropy, and the critical value between the chaotic and large-period states is found. The threshold at this time is used as the size of the internal driving force γ. The internal driving force γ here is substituted into the intermittent chaotic oscillator model to determine the intermittent chaotic oscillator model.

[0149] S32, detect the target signal by changing the solution step size and the size of the internal driving force of the intermittent chaotic oscillator system. Specifically, after the target signal is input into the intermittent chaotic oscillator system, the target signal is detected by continuously changing the solution step size of the system and the size of the internal driving force γ. If the input target signal contains a frequency with a frequency difference of less than 0.08ω (ω is the size of the internal driving frequency) from the internal driving frequency, the intermittent chaotic oscillator system will have intermittent chaos. Furthermore, the flowchart of the underwater target shaft frequency magnetic field detection method based on the inductive sensor array disclosed in the present invention also includes:

[0150] Figure 4 FIG. 1 is a flow chart of signal analysis in a method for detecting an underwater target shaft frequency magnetic field based on an inductive sensor array according to an embodiment of the present invention, that is, Figure 1 The detailed steps of analyzing the target signal in step S4 specifically include:

[0151] S41: Perform Hilbert envelope analysis on the time domain diagram of the intermittent chaotic oscillator system output, where the Hilbert transform expression is as follows:

[0152]

[0153] The Hilbert envelope expression is as follows:

[0154]

[0155] S42: Fourier transform of the Hilbert envelope;

[0156] S43: Analyze the detection result of the target signal according to the spectrum diagram, wherein when the spectrum of the Hilbert envelope has a line spectrum within 0.08ω, calculate the energy ratio of the line spectrum relative to the line spectrum of other frequencies, and if the energy ratio exceeds 10dB, it is determined that the intermittent chaotic oscillator system detects the target signal.

[0157] In summary, the underwater target shaft frequency magnetic field detection method based on the inductive sensor array disclosed in the present invention suppresses the background environment magnetic field noise and false line spectrum in the underwater target, and detects the shaft frequency line spectrum signal of the underwater target. According to the detection method disclosed in the present invention, the interference existing in the detection signal can be suppressed, the signal-to-noise ratio of the target signal can be increased by more than 20dB under extremely low signal-to-noise ratio conditions, and the detection of long-distance shaft frequency magnetic field signals can be realized with high accuracy and low false alarm rate.

[0158] Figure 5 An underwater target shaft frequency magnetic field detection system based on an inductive sensor array provided according to an embodiment of the present invention, Figure 5 Will combine Figure 1 Describe. Figure 5 As shown, the underwater target shaft frequency magnetic field detection system based on the inductive sensor array includes: a signal acquisition unit 501, a signal processing unit 502, a signal detection unit 503 and a signal analysis unit 504. The signal acquisition unit 501 includes Figure 1 The first inductive sensor array and the second inductive sensor array disclosed in the invention are used to collect the shaft frequency magnetic field signal in the underwater target water area, and capture the first signal and the second signal respectively; the signal processing unit 502 is used to collect the shaft frequency magnetic field signal in the underwater target water area, and capture the first signal and the second signal respectively; Figure 1 The method disclosed in step S2 in the above process the first signal and the second signal. The signal detection unit 503 is used to process the first signal and the second signal according to the above Figure 1 The method disclosed in step S3 detects the target signal; the signal analysis unit 504 is used to detect the target signal according to the above Figure 1 The target signal is analyzed by the method disclosed in step S4.

[0159] The underwater target shaft frequency magnetic field detection system based on the inductive sensor array disclosed by the present invention is designed and optimized by the structure and placement position of the sensor array node, and the detection signal and the reference signal are obtained by the optimized inductive magnetic sensor array; after the detection signal and the reference signal are preprocessed, the background noise and the environmental interference line spectrum in the detection signal are suppressed by window spectrum subtraction, whitening filtering and variational mode decomposition; the detection signal after the noise reduction processing is detected by the intermittent chaotic oscillator system, and after the output time domain diagram of the intermittent chaotic oscillator system is obtained, a preliminary judgment can be made on whether there is a target line spectrum in the target signal, and then the line spectrum information in the target signal is extracted by Hilbert envelope analysis, and the result of Hilbert envelope analysis is judged. Compared with the prior art, the underwater target shaft frequency magnetic field detection system based on the inductive sensor array proposed by the present invention can effectively suppress the background noise and interference line spectrum in the underwater target shaft frequency magnetic field detection, effectively realize the background noise suppression and weak shaft frequency line spectrum signal detection under extremely low signal-to-noise ratio, and solve the technical problem that the existing underwater target shaft frequency magnetic field detection has large background noise intensity and many false line spectrum interferences, which leads to the difficulty of line spectrum recognition.

[0160] In order to demonstrate the excellent performance of the present invention in detecting shaft-frequency magnetic field signals, the performance of the present method is demonstrated and analyzed through field test data.

[0161] Figure 6 Shown is a diagram of a fixed bracket for fixing the inductive sensor array used in the experiment; as shown in the figure, the inductive sensor array in the fixed bracket is arranged in the form of a three-dimensional coordinate diagram.

[0162] The experiment uses a coil to generate a periodic magnetic field signal to simulate the shaft frequency magnetic field signal. The simulated magnetic field signal period is set to 9.5Hz, and the equivalent magnetic moment of the coil is about 50Am2. According to the above-mentioned detection node placement method, the two detection nodes are about 200 meters apart. The coil is placed about 320 meters away from the nearest detection node, the sampling rate is set to 250Hz, the sampling time is 5 minutes, and the five-minute signal is analyzed.

[0163] Figure 7 The figure shows the time domain diagram and spectrum diagram of the target signal when the target signal contains the shaft frequency signal in the experiment; Figure 8 Shown are a time domain diagram of the target signal and a spectrum diagram of the target signal after the target signal in the experiment has been processed in the embodiment disclosed in the present invention.

[0164] It is obvious from the test results that the product has excellent performance in all key performance indicators. Figure 7 and attached Figure 8 contrast, Figure 7 The signal collected from the nearest measuring point is Figure 7 It can be seen that the 9.5 Hz signal cannot be directly detected in either the time domain or the spectrum analysis of the signal. Figure 8 It can be seen that the target signal after calculation and processing can be clearly seen in the spectrum. After the signal-to-noise ratio calculation, it is known that the signal-to-noise ratio has increased by nearly 40dB, which is mainly due to the technology of suppressing the environmental background noise magnetic field and false line spectrum. By performing multi-window spectrum subtraction on two groups of detection nodes 200 meters apart, the environmental background noise magnetic field noise and interference false line spectrum are suppressed. At the same time, the differential data is whitened and filtered to effectively suppress the 1 / f magnetic field noise in the environment. Finally, after the obvious noise is eliminated by variational mode decomposition, the signal-to-noise ratio of the target signal is effectively improved.

[0165] Fig. 9 The figure shows the time domain diagram of the intermittent chaotic system output and the linear amplitude spectrum of the Hilbert envelope when the target signal is input into the intermittent chaotic oscillator system and analyzed by the Hilbert envelope in the experiment; Fig.10 Shown is a table of detection errors in the experiment; and Fig.11 Shown is a statistical table of false alarm rates in the experiment.

[0166] In terms of detection indicators, through its unique design and technical means, it can achieve accurate detection of target signal frequency. Fig. 9 As shown, the detection results can calculate that the detection frequency is 9.499372Hz, and the error between the calculated result and the actual target signal frequency of 9.5Hz is only 0.0066%. Among the 15 detected data samples, as shown in Table 10 attached to the specification, the number of successful detections is 14 times, and the success rate reaches 93.3%. At the same time, this experiment also analyzed the false alarm rate performance in the detection signal. 30 environmental magnetic field data samples that do not contain target signals were selected, and the samples were processed using the method of this patent. Finally, after processing, as shown in Table 11 attached to the specification, the number of false alarms was 1, and the false alarm rate was only 3.3%, which fully proves the superiority and advancement of the underwater target shaft frequency magnetic field detection method based on the inductive sensor array disclosed in the present invention.

[0167] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting shaft frequency magnetic field of underwater targets based on an inductive sensor array, characterized in that: include: S1, setting a first sensor array and a second sensor array in an underwater target water area to capture a first signal and a second signal; S2, after preprocessing the first signal and the second signal, suppressing the noise and interference line spectra in the first signal and the second signal to obtain a detection signal, a reference signal, a differential signal and a target signal; S3, inputting the target signal into the intermittent chaotic oscillator system to obtain a time domain diagram of the output of the intermittent chaotic oscillator system, and judging the target line spectrum in the target signal according to the time domain diagram and obtaining the internal driving frequency of the intermittent chaotic oscillator in this state; as well as S4, using Hilbert envelope to analyze the time domain diagram of the intermittent chaotic oscillator system output and obtain its spectrum information, and combining the internal driving frequency of the intermittent chaotic oscillator in this state to analyze the line spectrum information of the target signal.

2. The underwater target shaft frequency magnetic field detection method based on the inductive sensor array according to claim 1 is characterized in that: The first sensor array and the second sensor array constitute a first detection node and a second detection node, each of which includes three inductive sensors. The inductive sensors respectively constitute the X, Y, and Z axes of the Cartesian coordinate system of the detection node with the center of gravity of the detection node as the origin. The inductive sensors are fixed in a fixed bracket, and an inertial navigation device is installed on the fixed bracket.

3. The underwater target shaft frequency magnetic field detection method based on the inductive sensor array according to claim 1 is characterized in that: The step of preprocessing the first signal and the second signal comprises: S201, removing outliers in the first signal and the second signal; S202, removing the DC amount in the first signal and the second signal; S203, converting the first signal and the second signal in the carrier coordinate system into signal expressions in a geographic coordinate system; and S204: Band-pass filter the first signal and the second signal in the geographic coordinate system to complete preprocessing.

4. The underwater target shaft frequency magnetic field detection method based on the inductive sensor array according to claim 3 is characterized in that: The method for removing the outliers of the first signal and the second signal in step S201 is: Calculate the first quartile Q1 and the third quartile Q3 in the signal; Calculate the interquartile range (IQR) of the signal and set the lower and upper limits of the outlier threshold; as well as Replace outliers with the previous non-outlier value.

5. The underwater target shaft frequency magnetic field detection method based on the inductive sensor array according to claim 3 is characterized in that: The step S203 specifically includes: Read the yaw angle φ output by the inertial navigation system bn , pitch angle θ bn and the roll angle ψ bn ; Rotate φ around the Z axis of the carrier coordinate system bn The angle gets the transition coordinate system Ψ; Rotate θ around the Y axis of the transition coordinate system Ψ bn The angle obtains the transition coordinate system θ; and Rotate ψ around the X axis of the transition coordinate system Θ bn The angle is used to obtain a geographic coordinate system, thereby acquiring the first signal and the second signal in the geographic coordinate system.

6. The underwater target shaft frequency magnetic field detection method based on the inductive sensor array according to claim 3 is characterized in that: The bandpass filtering step in step S204 is as follows: Set the passband range of the bandpass filter to [0.1 30] Hz; The bandpass filter is a second-order Butterworth bandpass filter, which is used to filter out power frequency electromagnetic field noise and geomagnetic field noise in the first signal and the second signal, wherein the transfer function H(s) of the second-order Butterworth bandpass filter is expressed as follows: Among them, ω0 is the center circle frequency and Q is the quality factor.

7. The underwater target shaft frequency magnetic field detection method based on the inductive sensor array according to claim 1 is characterized in that: The step S2 of suppressing the noise and interference line spectra in the first signal and the second signal includes: S205, windowing the first signal and the second signal using a Hamming window; S206, performing Fourier transform on each window of the windowed signal to extract the frequency spectrum and phase spectrum of the first signal and the second signal; S207, differentiating the frequency spectra of the windowed signals of the first signal and the second signal, and identifying a detection signal, a reference signal, and a differential signal according to the differential result; and S208: Acquire an interference line spectrum suppression signal using the phase spectrum of each window of the detection signal and the differential signal spectrum.

8. The underwater target shaft frequency magnetic field detection method based on the inductive sensor array according to claim 7 is characterized in that: The Hamming window division formula in step S205 is as follows: Where w(n) is the Hamming window function, N is the number of windows, and a0 is the Hamming window coefficient.

9. The underwater target shaft frequency magnetic field detection method based on the inductive sensor array according to claim 7 is characterized in that: The method of extracting the spectrum and phase spectrum by discrete Fourier transform in step S206 is as follows: The frequency domain sequence after discrete Fourier transform is as follows: Wherein x(n) is a discrete time domain sequence of the first signal or the second signal, X(k) is a frequency domain sequence after discrete Fourier transform, N is the length of the sequence, k represents a discrete frequency index, and n is a discrete time index; The discrete-time Fourier transform method to extract the phase spectrum is as follows: Where Im represents the imaginary part of the complex number, and Re represents the real part of the complex number.

10. The underwater target shaft frequency magnetic field detection method based on the inductive sensor array according to claim 7 is characterized in that: In step S207, the frequency spectra of the detection signal and the reference signal after windowing are differentiated, and the expression is as follows: where x k (n) is the window data after windowing, w(n) is the Hamming window function, x k1 (ω) is the spectrum data of each window of the detection signal, x k2 (ω) is the spectrum data of each window of the reference signal, Y k (ω) is the frequency spectrum of the k-th window after subtraction, which is recorded as the k-th window difference signal in the difference signal.

11. The underwater target shaft frequency magnetic field detection method based on the inductive sensor array according to claim 7 is characterized in that: The specific steps of step S208 are as follows: The spectrum data of each window of the differential signal is averaged, and the calculation formula is as follows: Where, M is the number of windows; The method of obtaining the interference line spectrum suppression signal according to the phase spectrum of each window of the detection signal and the spectrum of the differential signal is as follows: Wherein, y(n) is the interference line spectrum suppression signal, and N is the sequence length.

12. The underwater target shaft frequency magnetic field detection method based on the inductive sensor array according to claim 6 is characterized in that: The step of suppressing the noise and interference line spectra in the first signal and the second signal in step S2 further includes whitening filtering and variational mode decomposition, and the steps are as follows: S209, inputting the interference line spectrum suppression signal into a whitening filter to obtain a whitening filtered signal, wherein the expression of the whitening filter is: Where P is the model order, a k is the coefficient of the autoregressive model, z is the complex variable in the Z transformation; S210, performing variational modal decomposition on the whitened filtered signal, and updating and decomposing the mode and frequency of the whitened filtered signal, wherein the objective function expression is: Where K is the number of decomposed modes, u k is the kth mode, f k is the frequency corresponding to the kth mode, α is the penalty factor, the number of decomposed modes K is set to 8, and the penalty factor α is set to 9500; S211, after the objective function reaches a preset convergence condition, extracting each modal component of the whitening filter signal and the frequency corresponding to each modal component; as well as S212, selecting a modal component containing a target signal from each of the modes to obtain a denoised target signal.

13. The underwater target shaft frequency magnetic field detection method based on the inductive sensor array according to claim 1 is characterized in that: The step S3 also includes: S31, determining the driving force of the intermittent chaotic oscillator system, wherein the intermittent chaotic model is: y″+ky ′ -month+by 3 Scos(ωt)+s(t) Where k is the damping coefficient, -ax+bx 3 is the nonlinear dynamic term, γ is the internal driving force, y is the output of intermittent chaos, s(t) is the target signal, and ω is the internal driving frequency; and S32, changing the solution step size of the intermittent chaotic oscillator system and the size of the internal driving force, detecting the target signal, wherein when the target signal contains a frequency difference with the internal driving frequency that is less than 0.08ω, intermittent chaos will occur in the system.

14. The underwater target shaft frequency magnetic field detection method based on the inductive sensor array according to claim 1 is characterized in that: The step S4 specifically includes: S41: Performing Hilbert envelope analysis on the time domain diagram output by the intermittent chaotic oscillator system, wherein the Hilbert transform expression is as follows: The Hilbert envelope expression is as follows: S42: Performing Fourier transform on the Hilbert envelope; S43: Analyze the detection result of the target signal according to the spectrum diagram, wherein when a line spectrum appears in the spectrum of the Hilbert envelope within 0.08ω, calculate the energy ratio of the line spectrum relative to the line spectrum of other frequencies, and if the energy ratio exceeds 10dB, determine that the intermittent chaotic oscillator system detects the target signal.

15. An underwater target shaft frequency magnetic field detection system based on an inductive sensor array, characterized in that: include: A signal acquisition unit, used to acquire shaft frequency magnetic field signals of underwater targets, including a first signal and a second signal; A signal processing unit, configured to process the first signal and the second signal according to the method as claimed in claims 1 to 14; A signal detection unit, configured to detect a target signal according to the method as claimed in claims 1 to 14; and A signal analysis unit, configured to analyze a target signal according to the method as claimed in claims 1-14.

Images