Optical fiber vibration signal difference step length optimization method based on PSO algorithm

Abstract

The invention discloses an optical fiber vibration signal difference step length optimization method based on a PSO algorithm. The method comprises the following steps: acquiring a back Rayleigh scattering signal of a sensing optical fiber by using a # imgabs0 # system; performing amplitude calculation and preprocessing on the optical fiber vibration signal, removing interference of a Fresnel reflection peak at the tail end of the optical fiber, and constructing a difference accumulation model; evaluating signal-to-noise ratios of different step lengths through iterative optimization in a set step length search space by using a particle swarm optimization algorithm, dynamically updating particle positions and speeds, and searching an optimal difference step length with the highest signal-to-noise ratio; compared with a traditional method, the method can effectively avoid local optimum and quickly converge to a global optimal solution, the signal-to-noise ratio of the optical fiber vibration signal is remarkably improved, and the detection precision of a target vibration event is enhanced. The whole optimization process is high in calculation efficiency and strong in robustness. According to the method, adaptive selection of the optimal step length can be realized, the signal processing performance and the application effect of the # imgabs1 # system are improved, and the method has wide engineering application value.

Description

(I) Technical field

[0001] The present invention belongs to the technical field of optical fiber vibration signal detection and processing, and in particular to an optical fiber vibration signal differential step length optimization method based on a PSO algorithm. (II) Background technology

[0002] Phase-sensitive Optical Time Domain Reflectometer ) is a high-sensitivity sensing technology based on the backscattering effect of optical fiber, which is widely used in border security monitoring, earthquake monitoring, oil and gas pipeline protection and other fields. Its principle is to detect external vibration events distributed along the optical fiber through the change of the backscattering signal in the optical fiber. However, since the optical fiber vibration signal has the characteristics of high noise intensity and weak signal characteristics, in practical applications, how to extract effective vibration signals from a large amount of noise has become an important factor affecting the detection performance.

[0003] exist In the signal processing process, the differential step size is a key parameter that determines the signal-to-noise ratio and detection sensitivity. Traditional methods usually determine the differential step size through empirical values ​​or simple searches. This method has low computational efficiency and is prone to falling into local optimality, resulting in insufficient detection accuracy. In addition, the Fresnel reflection peak at the end of the optical fiber often significantly increases the local signal-to-noise ratio, further interfering with the accuracy of step size optimization and affecting the overall detection effect.

[0004] In order to solve the above problems, a method is urgently needed to efficiently and accurately determine the optimal difference step size to improve The signal processing capability and event detection performance of the system. The differential step optimization method based on the particle swarm optimization (PSO) algorithm can significantly improve the efficiency and effect of step optimization through intelligent optimization and global search strategy, which is an effective means to solve this technical problem. (III) Summary of the invention

[0005] The purpose of the present invention is to solve the problem that the differential step size selection in the prior art relies on empirical values ​​or inefficient search methods, and to improve the accuracy and efficiency of optical fiber vibration signal detection. A method for optimizing the differential step size of optical fiber vibration signals based on a PSO algorithm is provided.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] The present invention introduces a particle swarm optimization algorithm, establishes a mathematical model with SNR as the optimization target, performs a global search in the candidate differential step space, and automatically finds the optimal differential step k. The present invention is implemented by the following technical solutions:

[0008] Step 201: Use a phase-sensitive optical time-domain reflectometer to collect the backward Rayleigh scattering signal of the vehicle vibration disturbance sensing optical fiber, obtain the I / Q component data of the optical fiber vibration signal, calculate the amplitude matrix, remove the Fresnel reflection peak interference at the end of the optical fiber, and ensure that the optimization algorithm is not affected by the interference signal.

[0009] The matrix is ​​divided according to the length of the laid optical fiber to obtain a two-dimensional amplitude space-time matrix D[i,j] consisting of the amplitude curve of the backscattered Rayleigh signal and the number of sampling points, where the element d in the i-th row and j-th column of the space-time two-dimensional matrix is ij , represents the amplitude information at the jth fiber sampling point under the ith pulse.

[0010] Step 202, particle swarm optimization initialization: setting the number of particle swarms, step search range and speed initialization parameters.

[0011] Calculate the SNR of the initial particle position and record the historical optimal value of the particle.

[0012] Step 203, iterative optimization process: based on the PSO algorithm, the speed and position of each particle are updated, the signal-to-noise ratio (SNR) is calculated to evaluate the quality of the current step length, and the global optimal step length k is updated. * .

[0013] Step 204, optimal step length output: when the maximum number of iterations is reached or the global optimal step length converges, the optimal step length is output.

[0014] The optimal step size is used for difference accumulation to optimize signal processing.

[0015] Step 205: Signal processing and result display: display the processing results in real time through visualization tools.

[0016] Compared with the existing technology, the present invention has the following advantages: strong adaptability, the optimal differential step size is automatically selected through the PSO algorithm, avoiding the limitations of manual experience setting, and adapting to the signal characteristics in different environments; high optimization efficiency, the particle swarm optimization algorithm can quickly converge to the global optimum, significantly improving the signal processing efficiency. The application effect is good. After adopting the optimal step size, the signal-to-noise ratio and vibration detection accuracy are improved, and the Reliability and practicality of the system. (IV) Description of the drawings

[0017] Figure 1 Schematic diagram of a method for optimizing the differential step size of optical fiber vibration signals based on a PSO algorithm according to an embodiment of the present invention.

[0018] Figure 2 This is a flow chart of a method for optimizing the differential step length of optical fiber vibration signals based on a PSO algorithm according to an embodiment of the present invention.

[0019] Figure 3 It is a real-time waterfall chart of a simulation experiment according to an embodiment of the present invention. (V) Specific implementation methods

[0020] The contents of the present invention are further described below in conjunction with the drawings and embodiments, but are not intended to limit the present invention.

[0021] like Figure 2 As shown, a method for optimizing the differential step length of optical fiber vibration signals based on the PSO algorithm specifically includes the following steps:

[0022] Step 201: Use a phase-sensitive optical time-domain reflectometer to collect the backward Rayleigh scattering signal of the vehicle vibration disturbance sensing optical fiber, obtain the I / Q component data of the optical fiber vibration signal, and calculate the amplitude matrix: Where i represents the pulse number and j represents the fiber distance index.

[0023] Remove the Fresnel reflection peak interference at the end of the optical fiber to ensure that the optimization algorithm is not affected by the interference signal.

[0024] It is divided according to the length of the laid optical fiber to form the original space-time two-dimensional matrix D[i,j].

[0025]

[0026] where d ij It represents the amplitude at the jth optical fiber sampling point at the i-th sampling time, i=1, 2...i, i is the number of sampling pulses; j=1, 2..., j, j is the number of optical fiber vibration samples in one pulse, corresponding to the optical fiber position.

[0027] Step 202, Particle Swarm Optimization (PSO) initialization: set the number of particles N, the maximum number of iterations T max , and the search range of the difference step size k [k min ,k max ].

[0028] Initialize the step position k and velocity v of each particle and randomly generate an initial particle population.

[0029] Initialize the local optimal particle p best and the global optimal g best .

[0030] Fitness function calculation: For each particle, use the current step size k current Perform differential accumulation on the amplitude matrix D(i,j) and calculate SNR(k current ) as the fitness value.

[0031] The difference accumulation formula is: The matrix after difference is Δ(m,n)=|D(m+kcurrent , n) - D(m, n)|, and the accumulated result is

[0032] where D is the signal matrix, m is the pulse index and 0 < m < i, 0 < m + k < i; n is the spatial position index, 0 < n < j; k current is the current step size; S(m) is the signal after differential accumulation.

[0033] The SNR calculation formula is: where the signal energy is the energy of the vibration signal after differential accumulation, and the noise energy is the energy of the non-target signal after differential accumulation.

[0034] Signal energy: where S[i] is the signal value corresponding to the target signal spatial position interval in the signal matrix D, and N is the number of points in the target area.

[0035] Noise energy: where N[j] is the corresponding noise value in the signal matrix D, and M is the number of points in the background area.

[0036] Step 203, Particle position and velocity update: According to the PSO update formula, iteratively update the particle velocity and position: where ω is the inertia weight, c 1 and c 2 are the learning factors, r 1 and r 2 are random numbers.

[0037] When the particle velocity approaches zero, the decrease in velocity indicates that the particle has become stable, indicating that the swarm search is approaching the end. That is, if the velocities of all particles then it is considered convergent.

[0038] Step 204, Iteration termination: When the maximum number of iterations T max or the global optimal fitness value converges, output the optimal step size k * = g best .

[0039] Differential accumulation and signal processing optimization: Use the optimal step size k * to perform differential accumulation calculation on the amplitude matrix after removing the Fresnel reflection peak: Δ(m, n) = |D(m + k * , n) - D(m, n)|, m is the pulse index and 0 < m < i, 0 < m + k * < i; n is the spatial position index, 0 < n < j; S(m) is the signal after differential accumulation.

[0040] The differential accumulation result is used as the optimized signal input for further vibration position detection and noise filtering.

[0041] Step 205, signal processing and result display: After completing the differential step length calculation based on the particle swarm optimization (PSO) algorithm optimization, the processed data is visualized through a graphical interface so that the user can intuitively view the processing effect.

[0042] After new data is collected or processed, the chart can be automatically updated to help users monitor and analyze signal changes in real time.

[0043] The present invention is simulated and illustrated by taking an example below:

[0044] The sensing fiber is a standard single-mode fiber of 5 km, the pulse period is 50 us, and the detection pulse is 10 ns, so the number of samples is 10,000, of which 5,000 points are I / Q. PZT is used to simulate the vehicle fiber vibration data, and the PZT position is 80-100 meters. Figure 3 As shown in the figure, it is proved that the differential step size optimization method based on the particle swarm optimization (PSO) algorithm can effectively improve the processing effect of the optical fiber vibration signal, accurately locate the target event in real time, significantly improve the signal-to-noise ratio, and find the optimal differential step size in different noise environments. It has good robustness and is suitable for real-time data processing.

[0045] The present invention provides a method for optimizing the differential step length of optical fiber vibration signals based on the PSO algorithm, which can automatically select the optimal differential step length, improve the SNR of the optical fiber vibration signal, and effectively remove environmental noise. Secondly, the PSO algorithm has a global search capability, which avoids the dilemma of the traditional method falling into the local optimum and ensures the accuracy of the optimal step length. In addition, the optimization process does not require manual intervention and has a high degree of automation. It can provide stable and accurate signal processing effects in practical applications, and is particularly suitable for complex optical fiber monitoring and intrusion detection scenarios. It should be noted that although the embodiments described in the present invention are illustrative, they are not limitations of the present invention. Therefore, the present invention is not limited to the above-mentioned specific embodiments. Without departing from the principles of the present invention, other embodiments obtained by those skilled in the art under the enlightenment of the present invention are within the protection of the present invention.

Claims

1. A method for optimizing the differential step length of optical fiber vibration signals based on PSO algorithm, characterized by: right The optical fiber vibration signal collected by the system is amplitude calculated and preprocessed to remove the interference of the Fresnel reflection peak at the end of the optical fiber; the search space of the differential step is constructed, and the initial step range, signal-to-noise ratio evaluation index and particle swarm optimization (PSO) parameters are set; the position, velocity and step search range of the particle swarm are initialized; the position and velocity of the particles are updated through iterative calculation of the particle swarm optimization algorithm; according to the principle of maximizing the signal-to-noise ratio (SNR), the step parameters are dynamically adjusted during the iteration process; the optimal differential step with the highest signal-to-noise ratio is output and applied to signal processing to optimize the detection accuracy of vibration events.

2. The optical fiber vibration signal differential step length optimization method based on the PSO algorithm according to claim 1 is characterized in that: The method for removing the Fresnel reflection peak at the end of the optical fiber includes fixing a number of end data rows of the vibration signal matrix to avoid the influence of the reflection peak interference on the differential step calculation.

3. The optical fiber vibration signal differential step length optimization method based on the PSO algorithm according to claim 1 is characterized in that: The initialization of the particle swarm includes setting the particle position to a random value in the step search space; initializing the particle velocity to 0 or a small range of random values; and setting the global optimal position and the individual optimal position of the particle as initial values.

4. The optical fiber vibration signal differential step length optimization method based on the PSO algorithm according to claim 1 is characterized by: Particle swarm iterative optimization is updated using the following formula: Velocity update formula: v i (t+1)=ω·v i (t)+c1·r1·(p i -x i (t))+c2·r2·(gx i (t)), where ω is the inertia weight, c1 and c2 are acceleration factors, r1 and r2 are random numbers, and p i is the individual optimal position, g is the global optimal position; Position update formula: x i (t+1)=x i (t)+v i (t+1), where x i (t) is the current position of the particle.

5. The optical fiber vibration signal differential step length optimization method based on the PSO algorithm according to claim 1 is characterized by: The signal-to-noise ratio calculation includes: calculating the signal-to-noise ratio values ​​under different step lengths through a differential accumulation model, and taking the step length corresponding to the highest signal-to-noise ratio as the optimal step length.

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