A geomagnetic vector measurement error compensation method based on particle swarm optimization algorithm

Abstract

This invention provides a method for compensating errors in geomagnetic vector measurement based on a particle swarm optimization algorithm. This method uses a magnetic field sensor and an attitude sensor to measure the ambient magnetic field and attitude of a carrier. After calculating the standard Earth magnetic field value for the carrier's region using the world geomagnetic magnetic field model, a geomagnetic vector measurement error compensation model is established based on the carrier's interference magnetic field model. A particle swarm optimization algorithm is then used to calculate the global optimal solution for 12 compensation parameters. Error compensation for geomagnetic vector measurement is achieved based on the optimal compensation parameters and the compensation model. This method is suitable for use in marine magnetic field measurement, boasting excellent algorithm adaptability and high compensation accuracy.

Description

Technical Field

[0001] The present invention relates to the field of geomagnetic vector measurement, and in particular to a geomagnetic vector measurement error compensation method based on a particle swarm optimization algorithm. Background Art

[0002] The measurement of geomagnetic field vector has important research value in the fields of geomagnetic navigation, resource exploration, and ship magnetic detection. There are two main methods to obtain geomagnetic field vector: geomagnetic field model solution and field measurement. Geomagnetic field model solution is suitable for predicting large-scale geomagnetic fields and usually does not include local magnetic anomalies. Field measurement can obtain more accurate geomagnetic field data including local anomalies. Field geomagnetic field measurement is often achieved by carrying sensors on carriers, such as aircraft used in aeromagnetic detection and autonomous underwater vehicles used in underwater magnetic anomaly detection. Since field measurements are affected by the interfering magnetic field of the carrier, a method that can effectively compensate for geomagnetic vector measurement errors is needed in the field of geomagnetic vector measurement. Summary of the Invention

[0003] The purpose of the present invention is to propose a geomagnetic vector measurement error compensation method based on a particle swarm optimization algorithm. A magnetic field sensor and an attitude sensor are used to measure the environmental magnetic field and attitude of a carrier. After the standard earth magnetic field value of the carrier area is calculated using the WMM model (World Geomagnetic Magnetic Field Model), a geomagnetic vector measurement error compensation model is established based on the carrier interference magnetic field model. The particle swarm optimization algorithm is used to calculate the global optimal solution of 12 compensation parameters. Finally, error compensation of geomagnetic vector measurement is achieved based on the optimal compensation parameters and the compensation model.

[0004] The specific technical solution of the present invention is to provide a geomagnetic vector measurement error compensation method based on a particle swarm optimization algorithm, and the geomagnetic vector measurement error compensation method based on a particle swarm optimization algorithm comprises the following steps:

[0005] Step 1: Install the magnetic field sensor and attitude sensor at the geometric center of the carrier;

[0006] Step 2: Use magnetic field sensors and attitude sensors to collect the carrier's environmental magnetic field and attitude data, and calculate the standard Earth magnetic field value in the area where the carrier is located using the world geomagnetic field model;

[0007] Step 3: Based on the carrier interference magnetic field model, a geomagnetic vector measurement error compensation model is established. The standard earth magnetic field value and the carrier environmental magnetic field and attitude data collected by the magnetic field sensor and attitude sensor are used as known conditions. The carrier induced magnetic field coefficient matrix and the fixed magnetic field interference vector are used as the parameters to be solved. The particle swarm optimization algorithm is used to calculate the optimal compensation parameters of 12 compensation parameters.

[0008] Step 4: Based on the optimal compensation parameters and the geomagnetic vector measurement error compensation model, the compensated geomagnetic field value is calculated using the carrier environment magnetic field and attitude data collected by the magnetic field sensor and attitude sensor.

[0009] Furthermore, in step 3, a geomagnetic vector measurement error compensation model including an induced magnetic field error and a fixed magnetic field error is established based on the carrier interference magnetic field model. This step includes:

[0010] Establish the carrier interference magnetic field model, that is,

[0011] B v =KAB e +B p

[0012] Where: B v is the magnetic field sensor measurement value, K is the carrier induced magnetic field coefficient matrix, is the posture transformation matrix, θ, ψ are the rotation angles around the X, Y, and Z axes of the attitude sensor, respectively. B e is the standard earth magnetic field value, B p is the fixed magnetic field interference vector;

[0013] Establish a geomagnetic vector measurement error compensation model, namely

[0014] AB e =K′B v +B′ p

[0015] Where: K' is the inverse matrix of the carrier induced magnetic field coefficient matrix, B o ' p =-K′B p .

[0016] Furthermore, in step 3, a particle swarm optimization algorithm is used to calculate the global optimal solution of 12 compensation parameters, which includes:

[0017] The position of each particle in the particle swarm optimization algorithm is represented by a vector with 1 row and 12 columns, and the position of the particle is used to represent the matrix K′ and matrix B to be solved. p '12 parameters in;

[0018] Define a fitness function, substitute the 12 parameters of the particle position into the geomagnetic vector measurement error compensation model, and solve the compensated geomagnetic field B e ′ and the real geomagnetic field B e The error between them is used as the criterion for evaluating the quality of the current particle position.

[0019] Taking the standard earth magnetic field value and the carrier environment magnetic field and attitude data collected by the magnetic field sensor and attitude sensor as known conditions, the particle swarm optimization algorithm is used to iteratively solve the global optimal solution of 12 compensation parameters.

[0020] Furthermore, in step 1, the installation process needs to ensure that the coordinate axes of the two sensors are aligned, and there will be no relative displacement between the two sensors and the carrier after installation.

[0021] Furthermore, in step 3, the particle velocity update formula of the particle swarm optimization algorithm is as follows:

[0022] v i (t+1)=w·v i (t)+c1·r1·(pbest i -x i (t))+c2·r2·(gbest-x i (t))

[0023] Where: v i (t+1) is the velocity of particle i in the next time step, v i (t) is the velocity of particle i at the current time step; w is the inertia weight, which controls the tendency of the particle to maintain its current velocity; c1 and c2 are learning factors, which control the tendency of the particle to move towards its personal best position (pbest) and the group best position (gbest), respectively; r1 and r2 are random numbers uniformly distributed in the range [0,1]; pbest i is the best position of particle i; gbest is the best position of the group; x i (t) is the position of particle i at the current time step.

[0024] Furthermore, in step 3, the particle position update formula of the particle swarm optimization algorithm is as follows:

[0025] x i (t+1)=x i (t)+v i (t+1)

[0026] Where: x i (t+1) is the position of particle i at the next time step; x i (t) is the position of particle i at the current time step; v i (t+1) is the velocity of particle i at the next time step.

[0027] Furthermore, in step 3, a fitness function is defined, and the 12 parameters of the particle position are substituted into the geomagnetic vector measurement error compensation model to solve the compensated geomagnetic field B e ′ and the real geomagnetic field Be The error between them is used as the standard for evaluating the quality of the current particle position. The fitness function is as follows:

[0028] r=|K′B v +B′ p -AB e |

[0029] Where: r is the absolute value of the difference between the compensated geomagnetic field value and the standard geomagnetic field value. The smaller the value of r, the better the particle position and the more accurate the compensated geomagnetic field value.

[0030] After the particle swarm optimization algorithm iterates a predetermined number of times, a set of matrices K′ and B with the smallest r is selected. p ′ is taken as the optimal compensation parameter.

[0031] The present invention proposes a method for compensating geomagnetic vector measurement errors based on a particle swarm optimization algorithm, which has the following advantages:

[0032] (1) The present invention constructs a new geomagnetic vector measurement error compensation model. By adopting the geomagnetic vector measurement error compensation model constructed by the present invention, the induced magnetic field interference and fixed magnetic field interference in the geomagnetic vector measurement process can be eliminated, thereby realizing the geomagnetic field vector measurement in an environment with an interfering magnetic field.

[0033] (2) The particle swarm optimization algorithm is used to estimate the error compensation parameters of geomagnetic vector measurement. Compared with the Kalman filter algorithm and the four-heading algorithm, the particle swarm optimization algorithm can more accurately calculate the compensation parameters due to the characteristics of random motion of its particles, thereby achieving higher-precision geomagnetic vector measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a schematic diagram of the principle of the geomagnetic vector measurement error compensation method based on the particle swarm optimization algorithm proposed in the present invention. DETAILED DESCRIPTION

[0035] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0036] The geomagnetic vector measurement error compensation method based on the particle swarm optimization algorithm of this embodiment includes:

[0037] Magnetic field sensors and attitude sensors are installed on the carrier, and the carrier's environmental magnetic field and attitude data are collected using the magnetic field sensors and attitude sensors. After the standard earth magnetic field value of the carrier's area is calculated using the WMM model (World Geomagnetic Magnetic Field Model), a geomagnetic vector measurement error compensation model is established based on the carrier's interference magnetic field model. The particle swarm optimization algorithm is used to calculate the global optimal solution of 12 compensation parameters. Based on the optimal compensation parameters and compensation model, error compensation of geomagnetic vector measurement is achieved.

[0038] Furthermore, the method comprises the following steps:

[0039] Step 1: Install the magnetic field sensor and attitude sensor at the geometric center of the carrier. During the installation process, ensure that the coordinate axes of the two sensors are aligned and there is no relative displacement between the two sensors and the carrier after installation.

[0040] Step 2: Use magnetic field sensors and attitude sensors to collect the carrier's environmental magnetic field and attitude data, and calculate the standard earth magnetic field value of the carrier's area through the WMM model (World Magnetic Field Model).

[0041] Step 3: A geomagnetic vector measurement error compensation model is established based on the carrier interference magnetic field model. The standard earth magnetic field value and the carrier environment magnetic field and attitude data collected by the magnetic field sensor and attitude sensor are used as known conditions. The carrier induced magnetic field coefficient matrix and the fixed magnetic field interference vector are used as parameters to be solved. The particle swarm optimization algorithm is used to calculate the global optimal solution of the 12 compensation parameters.

[0042] The model of the interference magnetic field of the geomagnetic vector measurement carrier in step 3 is as follows:

[0043] B v =KAB e +B p (1)

[0044] Where: B v is the carrier environment magnetic field, that is, the magnetic field sensor measurement value, K is the carrier induced magnetic field coefficient matrix, is the posture transformation matrix, θ, ψ are the rotation angles around the X, Y, and Z axes of the attitude sensor, i.e. attitude data, B e is the standard earth magnetic field value, B p is a fixed magnetic field interference vector.

[0045] The geomagnetic vector measurement error compensation model is as follows:

[0046] AB e =K′B v +B′ p (2)

[0047] Where: K' is the inverse matrix of the carrier induced magnetic field coefficient matrix, B' p =-K′B p .

[0048] When there is no relative displacement between the carrier, magnetic field sensor and attitude sensor, the carrier's induced magnetic field coefficient matrix and fixed magnetic field interference vector are both time domain invariants, so the matrix K' and vector B' pIt is considered as a constant during the error compensation process of geomagnetic vector measurement.

[0049] The particle swarm optimization algorithm is used to solve the geomagnetic vector measurement error compensation model. The position of each particle in the particle swarm optimization algorithm is represented by a vector with 1 row and 12 columns, and the position of the particle is used to represent the matrix K′ and matrix B′ to be solved. p There are 12 parameters in it.

[0050] The particle velocity update formula of the particle swarm optimization algorithm is as follows:

[0051] v i (t+1)=w·v i (t)+c1·r1·(pbest i -x i (t))+c2·r2·(gbest-x i (t)) (3)

[0052] Where: v i (t+1) is the velocity of particle i in the next time step, v i (t) is the velocity of particle i at the current time step; w is the inertia weight, which controls the tendency of the particle to maintain its current velocity; c1 and c2 are learning factors, which control the tendency of the particle to move towards its personal best position (pbest) and the group best position (gbest), respectively; r1 and r2 are random numbers uniformly distributed in the range [0,1]; pbest i is the best position of particle i; gbest is the best position of the group; x i (t) is the position of particle i at the current time step.

[0053] The particle position update formula of the particle swarm optimization algorithm is as follows:

[0054] x i (t+1)=x i (t)+v i (t+1) (4)

[0055] Where: x i (t+1) is the position of particle i at the next time step; x i (t) is the position of particle i at the current time step; v i (t+1) is the velocity of particle i at the next time step.

[0056] Define a fitness function, substitute the 12 parameters of the particle position into the geomagnetic vector measurement error compensation model, and solve the compensated geomagnetic field B e ′ and the real geomagnetic field B eThe error between them is used as the standard for evaluating the quality of the current particle position. The fitness function is as follows:

[0057] r=|K′B v +B′ p -AB e | (5)

[0058] Where: r is the absolute value of the difference between the compensated geomagnetic field value and the standard geomagnetic field value. The smaller the value of r, the better the particle position and the more accurate the compensated geomagnetic field value.

[0059] After the particle swarm optimization algorithm iterates a predetermined number of times, a set of matrices K′ and B with the smallest r is selected. p ′ is taken as the optimal compensation parameter.

[0060] Step 4: Based on the optimal compensation parameters and the geomagnetic vector measurement error compensation model, the compensated geomagnetic field value is calculated using the carrier environment magnetic field and attitude data collected by the magnetic field sensor and attitude sensor.

[0061] This method constructs a geomagnetic vector measurement error compensation model that includes induced magnetic field interference and fixed magnetic field interference. Since the magnetic field sensor and attitude sensor themselves have certain instrument errors, and are also interfered by electromechanical equipment, vibration and other factors during the measurement process, the induced magnetic field coefficient matrix and fixed magnetic field interference vector obtained by traditional calculation methods will have large errors.

[0062] Therefore, this method uses the particle swarm optimization algorithm, and the position of each particle in the particle swarm optimization algorithm is represented by a vector with 1 row and 12 columns, and the position of the particle is used to represent the matrix K′ and the matrix B to be solved. p '. At the same time, a fitness function is defined, and the 12 parameters of the particle position are substituted into the geomagnetic vector measurement error compensation model to solve the compensated geomagnetic field B e ′ and the real geomagnetic field B e The error between the two parameters is used as a criterion for evaluating the quality of the current particle position. Using the standard Earth magnetic field and the carrier's environmental magnetic field and attitude data collected by the magnetic field sensor and attitude sensor as known conditions, a particle swarm optimization algorithm is used to iteratively solve for the global optimal solution for 12 compensation parameters. Finally, error compensation for geomagnetic vector measurement is achieved based on the optimal compensation parameters and compensation model.

[0063] The present invention is not limited to the above-mentioned specific embodiments. A person skilled in the art can implement the present invention in a variety of other specific embodiments based on the embodiments and the contents disclosed in the drawings. Therefore, any design that adopts the design structure and ideas of the present invention and makes some simple transformations or changes falls within the scope of protection of the present invention.

Claims

1. A geomagnetic vector measurement error compensation method based on particle swarm optimization algorithm, characterized in that: The geomagnetic vector measurement error compensation method based on the particle swarm optimization algorithm comprises the following steps: Step 1: Install the magnetic field sensor and attitude sensor at the geometric center of the carrier; Step 2: Use magnetic field sensors and attitude sensors to collect the carrier's environmental magnetic field and attitude data, and calculate the standard Earth magnetic field value in the area where the carrier is located using the world geomagnetic field model; Step 3: Based on the carrier interference magnetic field model, a geomagnetic vector measurement error compensation model is established. The standard earth magnetic field value and the carrier environmental magnetic field and attitude data collected by the magnetic field sensor and attitude sensor are used as known conditions. The carrier induced magnetic field coefficient matrix and the fixed magnetic field interference vector are used as the parameters to be solved. The particle swarm optimization algorithm is used to calculate the optimal compensation parameters of 12 compensation parameters. Step 4: Based on the optimal compensation parameters and the geomagnetic vector measurement error compensation model, the compensated geomagnetic field value is calculated using the carrier environment magnetic field and attitude data collected by the magnetic field sensor and attitude sensor.

2. The method for compensating geomagnetic vector measurement errors based on particle swarm optimization algorithm according to claim 1, characterized in that: In step 3, a geomagnetic vector measurement error compensation model including an induced magnetic field error and a fixed magnetic field error is established based on the carrier interference magnetic field model, and a carrier interference magnetic field model is established: B v =KAB e +B p Where B v is the magnetic field sensor measurement value, K is the carrier induced magnetic field coefficient matrix, is the posture transformation matrix, θ, ψ are the rotation angles around the X, Y, and Z axes of the attitude sensor, respectively. B e is the standard earth magnetic field value, B p is the fixed magnetic field interference vector; Establish a geomagnetic vector measurement error compensation model: AB e =K′B v +B′ p Where: K' is the inverse matrix of the carrier induced magnetic field coefficient matrix, B p ′=-K′B p .

3. The method for compensating geomagnetic vector measurement errors based on particle swarm optimization algorithm according to claim 2, characterized in that: In step 3, a particle swarm optimization algorithm is used to calculate the global optimal solution of 12 compensation parameters. This step includes: The position of each particle in the particle swarm optimization algorithm is represented by a vector with 1 row and 12 columns, and the position of the particle is used to represent the matrix K′ and matrix B to be solved. p '12 parameters in; Define a fitness function, substitute the 12 parameters of the particle position into the geomagnetic vector measurement error compensation model, and solve the compensated geomagnetic field B e ′ and the real geomagnetic field B e The error between them is used as the criterion for evaluating the quality of the current particle position. Taking the standard earth magnetic field value and the carrier environment magnetic field and attitude data collected by the magnetic field sensor and attitude sensor as known conditions, the particle swarm optimization algorithm is used to iteratively solve the global optimal solution of 12 compensation parameters.

4. The method for compensating geomagnetic vector measurement errors based on particle swarm optimization algorithm according to claim 1, characterized in that: In step 1, the coordinate axes of the two sensors are aligned during the installation process to ensure that there is no relative displacement between the two sensors and the carrier after installation.

5. The method for compensating geomagnetic vector measurement errors based on particle swarm optimization algorithm according to claim 3, characterized in that: In step 3, the particle velocity update formula of the particle swarm optimization algorithm is as follows: v i (t+1)=w·v i (t)+c1·r1·(pbest i -x i (t))+c2·r2·(gbest-x i (t)) Where: v i (t+1) is the velocity of particle i in the next time step, v i (t) is the velocity of particle i at the current time step; w is the inertia weight, which controls the tendency of the particle to maintain its current velocity; c1 and c2 are learning factors, which control the tendency of the particle to move towards its personal best position (pbest) and the group best position (gbest), respectively; r1 and r2 are random numbers uniformly distributed in the range [0,1]; pbest i is the best position of particle i; gbest is the best position of the group; x i (t) is the position of particle i at the current time step.

6. The method for compensating geomagnetic vector measurement errors based on particle swarm optimization algorithm according to claim 5, characterized in that: In step 3, the particle position update formula of the particle swarm optimization algorithm is as follows: x i (t+1)=x i (t)+v i (t+1) Where: x i (t+1) is the position of particle i at the next time step; x i (t) is the position of particle i at the current time step; v i (t+1) is the velocity of particle i at the next time step.

7. The method for compensating geomagnetic vector measurement errors based on particle swarm optimization algorithm according to claim 6, characterized in that: In step 3, a fitness function is defined, and the 12 parameters of the particle position are substituted into the geomagnetic vector measurement error compensation model to solve the compensated geomagnetic field B. e ′ and the real geomagnetic field B e The error between them is used as the standard for evaluating the quality of the current particle position. The fitness function is as follows: r=|K′B v +B′ p -AB e | Where: r is the absolute value of the difference between the compensated geomagnetic field value and the standard geomagnetic field value. The smaller the value of r, the better the particle position and the more accurate the compensated geomagnetic field value. After the particle swarm optimization algorithm iterates a predetermined number of times, a set of matrices K′ and B with the smallest r is selected. p ′ is taken as the optimal compensation parameter.

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