Soft Magnetic Error Compensation Method of Electronic Compass

 

1. Analysis of soft magnetic error of electronic compass

There is another ferromagnetic substance in the working environment of the electronic compass sensor, which, unlike hard ferromagnetic materials, is easily magnetized in a weak magnetic field. When the external magnetic field changes, its induced magnetism will also undergo a related change. The size and direction of the induced magnetic field will also change with the attitude and position of the carrier.
Because of its special properties, this material is called soft iron material. This soft iron material magnetizes itself due to the size of the external magnetic field it receives to produce a magnetic field that resists changes in magnetic flux, which can vary over a wide range. If the magnetic field in the space where the electronic compass sensor is located is known, the magnetic field actually measured by the electronic compass sensor is equal to the superposition of the geomagnetic field and the magnetic field generated by the soft iron interference. The soft iron error is equivalent to a time-varying error superimposed on the output of the electronic compass sensor. Because of the different properties of soft magnetic interference error and hard magnetic interference error, the least square method is no longer applicable when compensating soft magnetic interference error. Soft magnetic interference will lead to the deviation of the measurement Angle of the electronic compass. In an ideal environment, the Angle rotated by the measurement of the electronic compass is controllable, but the existence of soft magnetic interference error will lead to the deviation and uncontrollable Angle of the measurement process of the electronic compass. In the application of navigation system, a small Angle difference will lead to a large route error. The modern electronic compass has strong anti-interference and can suppress most of the Angle deviation, but the compensation of soft magnetic error is still worth studying and discussing.

2. Soft magnetic interference error compensation method
In the actual use of electronic compass, the noise errors caused by soft magnetic interference are mostly random noise errors. At present, there are many algorithms that can be used to compensate random noise and most of them are relatively mature, but considering the characteristics of electronic compass requiring real-time and rapid processing of large amounts of data. Three very mature random noise compensation algorithms, namely Kalman filter, improved Sage adaptive Kalman filter and particle filter, are selected as soft magnetic interference compensation algorithms. These three algorithms are easy to implement and can handle dense data.

2.1 Kalman filter
Kalman filtering algorithm can estimate the linear system with Gaussian white noise, which is the most widely used filtering method at present, and has been well applied in the fields of communication, navigation, guidance and control. The basic idea is that the minimum mean square error criterion is the best estimation criterion, and the future state quantity of the system is estimated by recursion theory, so that the estimated value is as close as possible to the real value.

2.2 Adaptive Kalman filtering
Traditional Kalman filter requires that the mean of dynamic noise and observed noise of the system be zero, and the statistical characteristics are known white noise, but these conditions may not be satisfied in practice, so there are modeling errors. Due to the limitation of objective conditions such as computing tools, the filtering algorithm is easy to produce error accumulation when running on the computer. This results in the loss of positivity or symmetry of error covariance matrix and the instability of numerical calculation.

2.3 Particle filter algorithm
The particle filter algorithm originated from the research of Poor Man's Monte Carlo problem in the 1950s, but the first applied particle filter algorithm was proposed by Gordon et al in 1993. The particle filter is based on the Monte Carlo method, which uses sets of particles to represent probabilities and can be used for any form of state-space model. Particle filter can accurately express the posterior probability distribution based on the observed and controlled quantities, and is a sequential important sampling method. Bayesian inference and importance sampling are the basis of understanding particle filtering.

3.Allan variance simulation experiment
The Allan analysis of variance is used to simulate the original data of random sequence, the data compensated by Kalman filter algorithm, the data compensated by particle filter algorithm, and the four groups of data compensated by adaptive Kalman filter algorithm. Verify the feasibility of Allan variance analysis algorithm. The Allan standard deviation curve of each data is drawn according to the analysis results. The Allan standard deviation curves of the four groups of data are shown in FIG. 14-17 respectively.

4. Summary
From FIG. 14 to FIG. 17, it can be seen that the Allan variance program of the paper can effectively analyze the experimental data.
Several sets of experimental data show that the program is effective.

After analyzing the data before and after compensation, it can be seen that the quantization noise and zero bias instability noise of the data after compensation by Kalman filter algorithm are reduced by 64% and 66.4% respectively. The quantization noise and zero bias instability noise of the compensated particle filter data are reduced by 70% and 72.1% respectively. The quantization noise and zero bias instability noise of the data compensated by adaptive Kalman filter are reduced by 91.5% and 75.7% respectively. All the algorithms we mentioned can have a better compensation effect for the original data noise.
It can be seen from the compensation effect that compared with traditional Kalman filter and particle filter, adaptive Kalman filter can better remove the noise in the original data, and filter the noise of ER-EC-385, ER-EC-365B and other types of electronic compass. The random data in the simulation experiment is based on the simulation of the noise caused by soft magnetic interference. The simulation results show that the filtering algorithm can compensate the noise of soft magnetic interference. 

Soft Magnetic Error Compensation Method of Electronic Compass

 

1. Analysis of soft magnetic error of electronic compass

There is another ferromagnetic substance in the working environment of the electronic compass sensor, which, unlike hard ferromagnetic materials, is easily magnetized in a weak magnetic field. When the external magnetic field changes, its induced magnetism will also undergo a related change. The size and direction of the induced magnetic field will also change with the attitude and position of the carrier.
Because of its special properties, this material is called soft iron material. This soft iron material magnetizes itself due to the size of the external magnetic field it receives to produce a magnetic field that resists changes in magnetic flux, which can vary over a wide range. If the magnetic field in the space where the electronic compass sensor is located is known, the magnetic field actually measured by the electronic compass sensor is equal to the superposition of the geomagnetic field and the magnetic field generated by the soft iron interference. The soft iron error is equivalent to a time-varying error superimposed on the output of the electronic compass sensor. Because of the different properties of soft magnetic interference error and hard magnetic interference error, the least square method is no longer applicable when compensating soft magnetic interference error. Soft magnetic interference will lead to the deviation of the measurement Angle of the electronic compass. In an ideal environment, the Angle rotated by the measurement of the electronic compass is controllable, but the existence of soft magnetic interference error will lead to the deviation and uncontrollable Angle of the measurement process of the electronic compass. In the application of navigation system, a small Angle difference will lead to a large route error. The modern electronic compass has strong anti-interference and can suppress most of the Angle deviation, but the compensation of soft magnetic error is still worth studying and discussing.

2. Soft magnetic interference error compensation method
In the actual use of electronic compass, the noise errors caused by soft magnetic interference are mostly random noise errors. At present, there are many algorithms that can be used to compensate random noise and most of them are relatively mature, but considering the characteristics of electronic compass requiring real-time and rapid processing of large amounts of data. Three very mature random noise compensation algorithms, namely Kalman filter, improved Sage adaptive Kalman filter and particle filter, are selected as soft magnetic interference compensation algorithms. These three algorithms are easy to implement and can handle dense data.

2.1 Kalman filter
Kalman filtering algorithm can estimate the linear system with Gaussian white noise, which is the most widely used filtering method at present, and has been well applied in the fields of communication, navigation, guidance and control. The basic idea is that the minimum mean square error criterion is the best estimation criterion, and the future state quantity of the system is estimated by recursion theory, so that the estimated value is as close as possible to the real value.

2.2 Adaptive Kalman filtering
Traditional Kalman filter requires that the mean of dynamic noise and observed noise of the system be zero, and the statistical characteristics are known white noise, but these conditions may not be satisfied in practice, so there are modeling errors. Due to the limitation of objective conditions such as computing tools, the filtering algorithm is easy to produce error accumulation when running on the computer. This results in the loss of positivity or symmetry of error covariance matrix and the instability of numerical calculation.

2.3 Particle filter algorithm
The particle filter algorithm originated from the research of Poor Man's Monte Carlo problem in the 1950s, but the first applied particle filter algorithm was proposed by Gordon et al in 1993. The particle filter is based on the Monte Carlo method, which uses sets of particles to represent probabilities and can be used for any form of state-space model. Particle filter can accurately express the posterior probability distribution based on the observed and controlled quantities, and is a sequential important sampling method. Bayesian inference and importance sampling are the basis of understanding particle filtering.

3. Allan variance simulation experiment 
The Allan analysis of variance is used to simulate the original data of random sequence, the data compensated by Kalman filter algorithm, the data compensated by particle filter algorithm, and the four groups of data compensated by adaptive Kalman filter algorithm. Verify the feasibility of Allan variance analysis algorithm. The Allan standard deviation curve of each data is drawn according to the analysis results. The Allan standard deviation curves of the four groups of data are shown in FIG. 14-17 respectively.

4 Summary
From FIG. 14 to FIG. 17, it can be seen that the Allan variance program of the paper can effectively analyze the experimental data.
Several sets of experimental data show that the program is effective.

After analyzing the data before and after compensation, it can be seen that the quantization noise and zero bias instability noise of the data after compensation by Kalman filter algorithm are reduced by 64% and 66.4% respectively. The quantization noise and zero bias instability noise of the compensated particle filter data are reduced by 70% and 72.1% respectively. The quantization noise and zero bias instability noise of the data compensated by adaptive Kalman filter are reduced by 91.5% and 75.7% respectively. All the algorithms we mentioned can have a better compensation effect for the original data noise.
It can be seen from the compensation effect that compared with traditional Kalman filter and particle filter, adaptive Kalman filter can better remove the noise in the original data, and filter the noise of ER-EC-385, ER-EC-365B and other types of electronic compass. The random data in the simulation experiment is based on the simulation of the noise caused by soft magnetic interference. The simulation results show that the filtering algorithm can compensate the noise of soft magnetic interference.  

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How to choose an electronic compass ?

There are a lot of people in the choice of electronic compass do not know how to choose, sometimes because did not consider the following content, and choose the wrong electronic compass, resulting in a waste of time and money, let us to see some tips when choose an electronic compass. 

First, let's take a look at what an electronic compass, also known as a digital compass, is a way of locating the North Pole using the geomagnetic field.

Secondly, as an important navigation and orientation tool, electronic compass is increasingly used in navigation and orientation systems.Most current navigation systems use an electronic compass to indicate direction.The electronic compass can accurately output Azimuth, pitch, Roll and other parameters by calculating the earth's magnetic field and gravitational field.

Known the basic concept of the electronic compass, now let's take a look at the electronic compass types on the market, manufacturers, many models, in the face of these we may look confused, so how to distinguish the performance of the electronic compass, how to choose a suitable for your application of the electronic compass?What's the difference between a good compass and a bad compass?

The main difference between electronic compasses is the difference in accuracy, namely the accuracy of heading, pitch and roll, and especially the accuracy of heading.The heading is defined as the Angle between the projection of the compass's axis on the horizontal plane and the north direction, the pitch is defined as the Angle between the compass's axis and the ground plane, and the roll is defined as the Angle at which the compass rotates about its axis.As shown in the figure below

According to the above definition, if the compass changes only in pitch, then its output azimuth and roll Angle should remain the same.In the same way, if a compass rolls about itself, that is, on its axis, the azimuth and pitch angles of its output should remain the same.In the use of compass, such as used in vehicles, antenna orientation and surface buoys and many other occasions, the carrier will often tilt back and forth, when used in petroleum, geological logging, often rotation, so most applications require compass to meet the requirements of tilt and roll orientation will not change.

Another requirement for compass accuracy is that the output of the compass should be 0 degrees if the current carrier is pointing north, and 90 degrees if the carrier is pointing east. The measured value and the true value should match exactly within the margin of error

For petroleum, geological, and coal logging, the orientation and inclination of the product should not change as the logging instrument rotates downhole, which is important for logging applications.

Another rule when choosing a compass is whether it is resistant to harsh conditions.A natural disadvantage of compasses is that they become less accurate in environments with magnetic interference.Compasses use the direction of the Earth's magnetic field to determine orientation. If the Earth's magnetic field is disturbed or distorted, the accuracy of orientation measurement will be reduced.Iron materials, batteries, motors, high currents, and so on can interfere with the geomagnetic field, and the closer these materials are to the compass, the worse the interference is.So if you want the compass to be accurate, you have to stay away from the source of the interference, but in many cases this is not possible.So the only way to solve this problem is to do magnetic calibration.In the process of magnetic calibration, the compass rotates with the measured object (i.e., the interference source) in a certain way and learns the surrounding magnetic environment to distinguish which is magnetic interference and which is geomagnetic field. Through this learning, the interference is eliminated and the high-precision azimuth output is obtained.

How is the application of 3-axis electronic compass in pipeline monitoring?

 The 3-axis electronic compass can help us to know the precise location of the pipeline.

In recent years, with the acceleration of urbanization, there are more and more underground pipe networks in cities, which brings with it frequent problems.This is because the pipeline is laid in a buried way. When the pipeline runs for a period of time, there are often some defects in the external anti-corrosion system, which causes the chemical corrosion and electrochemical corrosion of the pipeline, thus causing the pipeline corrosion perforation, stress corrosion cracking and other accidents.

Therefore, in order to ensure the safety of the pipeline and reduce the occurrence of accidents, it is necessary for us to monitor the pipeline to ensure the normal application of the pipelin

Pipeline monitoring requires monitoring of a lot of data, including monitoring of pipeline direction, deformation, corrosion, crack, pipeline environment and other key pipeline data.In the whole monitoring process, the precise positioning of the pipeline is a very important part, for example, when we detect deformation, corrosion and other problems, we need to know the specific position of the pipeline problem before the deformation, corrosion and other repairs.

The use of the 3-axis electronic compass can help us to know the precise location of the pipeline.

• The composition and working principle of 3-axis electronic compass

The 3-axis electronic compass consists of a 3-D reluctance sensor, a biaxial inclination sensor and an MCU, which can locate the direction and measure the inclination.

The three dimensional reluctance sensor is used to measure the earth's magnetic field. The inclination sensor compensates when the magnetometer is not horizontal. The MCU processes the signals from the magnetometer and inclination sensor as well as the data output and the soft and hard iron compensation

The geomagnetic field is a vector, and for a fixed location this vector can be decomposed into two components parallel to the local level and one component perpendicular to the local level.If the electronic compass is kept parallel to the local horizontal plane, then the three axes of the compass magnetometer correspond to these three components.

The three dimensional reluctance sensor uses three mutually perpendicular reluctance sensors, each axial sensor detects the strength of the geomagnetic field in that direction.

The forward direction is called the x-direction sensor to detect the vector value of the geomagnetic field in the x-direction;

Right - or Y-oriented sensors detect the vector value of the geomagnetic field in the Y direction;

The downward or z-direction sensor detects the vector value of the geomagnetic field in the z-direction

The sensor sensitivity in each direction has been adjusted to an optimal point according to the component vector of the geomagnetic field in that direction and has a very low transverse sensitivity.The analog output signal generated by the sensor is amplified and sent to MCU for processing. Combined with the dip Angle data measured by the biaxial dip Angle sensor, the error is reduced and the azimuth is finally determined.

3 axis electronic compass application in pipeline monitoring

As can be seen from the above, the 3-axis electronic compass can locate the direction and measure the inclination Angle, and can be used in pipeline monitoring.However, the 3-axis electronic compass is not directly used. In general, it will be used as a real-time pipeline trajectory detector.The pipeline trajectory detector consists of single chip microcomputer, electronic compass and rotary encoder, etc., which can detect the azimuth Angle, pitch Angle and distance between measuring points of the pipeline in real time, and reconstruct the pipeline trajectory in real time through software algorithm.

3D electronic compass used in pipeline