Unmanned helicopter magnetic interference real-time compensation method and system based on improved kalman filter

Abstract

The application provides an unmanned helicopter magnetic interference real-time compensation method and system based on an improved Kalman filter, and relates to the technical field of unmanned aerial vehicle navigation and control, comprising: a response processing of a trigger instruction received by an unmanned helicopter flight control system to form a compensation start signal, wherein the response processing comprises flight control program initialization, attitude parameter loading and sensor self-checking operation; and a time synchronization processing of magnetic field joint sensor data collected during unmanned helicopter flight to form a multi-source flight data set, wherein the time synchronization processing adopts a unified timestamp calibration mechanism. The unmanned helicopter magnetic interference real-time compensation method and system based on the improved Kalman filter compensate the magnetic interference of the unmanned helicopter in real time by adopting the improved Kalman filter algorithm. Through state estimation and dynamic weight adjustment, the dynamic model of the unmanned helicopter and the magnetic field distribution model are combined to realize magnetic field error correction during flight and ensure accurate control of the flight attitude.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of unmanned aerial vehicle navigation and control technology, in particular to a real-time magnetic interference compensation method and system for unmanned helicopters based on improved Kalman filtering. BACKGROUND

[0002] When performing airborne magnetic exploration tasks, existing unmanned helicopters are often affected by magnetic interference generated by the body structure, motor, power supply system and electronic equipment. Magnetic interference will superimpose on the geomagnetic signal, causing distortion of the measured data and affecting the analysis and interpretation accuracy of the geomagnetic anomaly. Traditional airborne magnetic compensation techniques mostly use ground calibration methods, and the operating personnel need to manually control the helicopter to complete the preset attitude action, and collect magnetometer data using ground equipment. The compensation parameters are usually calculated offline before the task and loaded into the flight control system, and the operation steps are complicated, and the compensation process relies on human experience. Some improved schemes try to embed real-time compensation algorithms in the flight control system, but most of them use fixed parameters or linear models, and do not fully consider the dynamic changes of flight attitude and the nonlinear characteristics of the environmental magnetic field, making it difficult to maintain compensation stability in complex flight environments.

[0003] In the dynamic flight process, the noise model of the traditional Kalman filtering algorithm is set fixedly, lacking adaptive adjustment mechanism. When the attitude angle, speed or acceleration changes greatly, the filter estimation error increases, resulting in lag or even distortion of the compensation result, and the magnetic measurement data drifts and jumps, affecting the continuity analysis of the geomagnetic anomaly. Especially when performing tasks in highland, coastal or strong magnetic gradient areas, the external magnetic field fluctuates violently, and the static compensation parameters cannot adapt to the changes in complex environment. The existing systems generally have slow response, calculation delay and parameter mismatch problems, and cannot realize real-time identification and dynamic compensation of magnetic interference in flight. This problem directly limits the application effect of unmanned helicopters in high-precision airborne magnetic measurement and scientific exploration fields. SUMMARY

[0004] In view of the shortcomings of the prior art, the present application provides a real-time magnetic interference compensation method and system for unmanned helicopters based on improved Kalman filtering, which solves the technical problem of how to use an improved Kalman filtering algorithm combined with multi-source data synchronization and flight state adaptive compensation method to solve the problems of unstable attitude and insufficient control accuracy of unmanned helicopters caused by magnetic interference during flight.

[0005] To achieve the above purpose, the present application realizes the following technical scheme: a real-time magnetic interference compensation method for unmanned helicopters based on improved Kalman filtering, comprising:

[0006] S1, a compensation start signal is formed by responding to the trigger instruction received by the flight control system of the unmanned helicopter, and the response processing includes flight control program initialization, attitude parameter loading and sensor self-checking operation;

[0007] S2, time synchronization processing is performed on the magnetic field joint sensor data collected during the flight of the unmanned helicopter to form a multi-source flight data set, and the time synchronization processing adopts a unified timestamp calibration mechanism;

[0008] S3, state estimation processing is performed on the multi-source flight data set to form a magnetic interference estimation result, and the state estimation processing adopts an improved Kalman filter algorithm, and the improved Kalman filter algorithm adopts a joint state space equation, and the joint state space equation includes an unmanned helicopter dynamics model and a magnetic field distribution model;

[0009] S4, flight state adaptive compensation processing is performed on the magnetic interference estimation result to form magnetic field correction data, and the flight state adaptive compensation processing includes adjusting compensation coefficients and calculating compensation parameters;

[0010] S5, difference evaluation processing is performed on the magnetic field correction data to form a compensation feedback update result, and the difference evaluation processing includes magnetic field intensity fluctuation comparison and gradient feature analysis, and the compensation feedback update result is transmitted to a flight control system, and the compensation feedback update result updates parameters of the improved Kalman filter algorithm to form a closed-loop compensation mechanism.

[0011] Preferably, the flight control program initialization includes flight control parameter loading, attitude control loop PID initial value setting, and magnetometer bias automatic correction, the attitude parameter loading includes attitude angle calculation, and the sensor self-checking operation includes amplitude and noise characteristic detection.

[0012] Preferably, the magnetic field joint sensor data includes magnetometer data, accelerometer data, gyroscope data, and GPS data.

[0013] Preferably, the improved Kalman filter algorithm includes prediction, update, and adaptive adjustment, the joint state space equation adopts a dynamic weight and nonlinear correction coupled formula, and the expression of the dynamic weight and nonlinear correction coupled formula is:

[0014] .

[0015] wherein, is a next time state estimation result, dimensionless, is a current state estimation vector, dimensionless, is an observation residual, dimensionless, is a dynamic gain matrix, dimensionless, and the value range is 0-1, is a linear weight matrix, dimensionless, is a nonlinear weight matrix, dimensionless, is a two-norm square of a residual vector, dimensionless, is a hyperbolic tangent function, dimensionless, with an amplitude range of [-1, 1].

[0016] Preferably, the unmanned helicopter dynamics model outputs the current state estimation vector, the current state estimation vector including attitude angle state quantities and angular velocity state quantities, the magnetic field distribution model outputs the observation residual, the observation residual including a geomagnetic reference vector, a direction cosine matrix and a magnetic interference component, the unmanned helicopter dynamics model and the magnetic field distribution model being in a bidirectional coupling relationship.

[0017] Preferably, the compensation coefficient is dynamically corrected according to attitude angular velocity, acceleration and magnetic disturbance rate of change of the unmanned helicopter, and the compensation parameter is a geomagnetic direction vector of the unmanned helicopter dynamics model and the magnetic field distribution model.

[0018] Preferably, the difference evaluation process adopts a nonlinear feedback regulation formula, and a calculation formula of the nonlinear feedback regulation formula is:

[0019] .

[0020] wherein, is a feedback gain adjustment quantity, dimensionless, is a gain scaling coefficient, dimensionless, is a sensitivity coefficient, dimensionless, is a current time magnetic field gradient vector, dimensionless, is a last time magnetic field gradient vector, dimensionless, is a nonlinear suppression function, dimensionless.

[0021] Preferably, the compensation feedback update result includes magnetic field intensity fluctuation data, spatial gradient characteristic information, parameter update quantity and closed-loop control signal.

[0022] Preferably, the compensation feedback update result is transmitted by using a UART interface and an SPI bus, the UART interface transmits the parameter update quantity and the closed-loop control signal, and the SPI bus transmits the magnetic field intensity fluctuation data and the spatial gradient characteristic information.

[0023] The unmanned helicopter magnetic interference real-time compensation system based on the improved Kalman filter includes:

[0024] A sensor module, which collects magnetic field and attitude information during flight of the unmanned helicopter, outputs flight attitude and magnetic field joint sensing data, and includes a three-axis magnetometer, a three-axis accelerometer, a three-axis gyroscope and a GPS module.

[0025] A flight control main control unit is configured to perform time synchronization and state estimation processing on the flight attitude and magnetic field combined sensing data to form a magnetic interference estimation result, wherein the time synchronization and state estimation processing adopts an improved Kalman filtering algorithm, and the improved Kalman filtering algorithm is based on a joint state space equation established by combining a model of unmanned helicopter dynamics and a model of magnetic field distribution.

[0026] A storage unit is configured to store a flight control program, a magnetic compensation algorithm, historical compensation parameters and flight data.

[0027] A communication unit is configured to transmit the magnetic interference estimation result, and the communication unit comprises a wireless communication module and a data interface.

[0028] An execution unit is configured to adjust the flight attitude of the unmanned helicopter according to the magnetic interference estimation result, and the execution unit comprises a motor controller and a rudder controller.

[0029] The application provides an unmanned helicopter magnetic interference real-time compensation method and system based on an improved Kalman filtering.

[0030] The unmanned helicopter magnetic interference real-time compensation method and system based on the improved Kalman filtering can compensate the magnetic interference of the unmanned helicopter in real time by using the improved Kalman filtering algorithm. Through state estimation and dynamic weight adjustment, the model of unmanned helicopter dynamics and the model of magnetic field distribution are combined to realize magnetic field error correction during flight and ensure accurate control of the flight attitude.

[0031] The improved Kalman filtering algorithm and the adaptive compensation processing mechanism are used to dynamically correct compensation coefficients and parameters, adjust magnetic field correction data in real time, improve the adaptability of the flight state and the stability of the system, enhance the accuracy and reliability of the magnetic interference compensation, and ensure efficient flight of the unmanned helicopter in complex environments. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 The application provides an unmanned helicopter magnetic interference real-time compensation method and system based on an improved Kalman filtering.

[0033] Figure 2 The application provides an unmanned helicopter magnetic interference real-time compensation method and system based on an improved Kalman filtering.

[0034] Figure 3 The application provides an unmanned helicopter magnetic interference real-time compensation method and system based on an improved Kalman filtering.

[0035] Figure 4 The application provides an unmanned helicopter magnetic interference real-time compensation method and system based on an improved Kalman filtering.

[0036] Figure 5 The application provides an unmanned helicopter magnetic interference real-time compensation method and system based on an improved Kalman filtering. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present invention have been clearly and completely described. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] Example 1

[0039] like Figures 1-5 As shown, this embodiment of the invention provides a real-time magnetic interference compensation method for unmanned helicopters based on an improved Kalman filter, including: S1, processing the trigger command received by the unmanned helicopter flight control system to form a compensation start signal; the response processing includes flight control program initialization, attitude parameter loading, and sensor self-testing. Flight control program initialization includes loading flight control parameters, setting initial values ​​for the attitude control loop PID, and automatic correction of magnetometer bias; attitude parameter loading includes attitude angle calculation; and sensor self-testing includes amplitude and noise characteristic detection.

[0040] S2. Time synchronization processing is performed on the magnetic field joint sensing data collected during the flight of the unmanned helicopter to form a multi-source flight dataset. The time synchronization processing adopts a unified timestamp calibration mechanism. The magnetic field joint sensing data includes magnetometer data, accelerometer data, gyroscope data, and GPS data.

[0041] S3. State estimation processing is performed on the multi-source flight dataset to generate magnetic interference estimation results. The state estimation process employs an improved Kalman filter algorithm, which uses a joint state-space equation. This joint state-space equation includes the unmanned helicopter dynamics model and the magnetic field distribution model. The improved Kalman filter algorithm includes prediction, updating, and adaptive adjustment. The joint state-space equation uses a dynamic weighting and nonlinear correction coupling formula, the expression of which is:

[0042] .

[0043] in, The result is a dimensionless state estimate for the next time step. The current state estimation vector, dimensionless. For observing residuals, dimensionless, This is the dynamic gain matrix, dimensionless, with values ​​ranging from 0 to 1. The weight matrix is ​​a linear, dimensionless matrix. The weight matrix is ​​non-linear and dimensionless. Let L be the square of the L2 norm of the residual vector, which is dimensionless. It is a hyperbolic tangent function, dimensionless, with an amplitude range of [-1, 1].

[0044] The unmanned helicopter dynamics model outputs a current state estimation vector, the current state estimation vector including attitude angle state quantities and angular velocity state quantities, the magnetic field distribution model outputs an observation residual, the observation residual including a geomagnetic reference vector, a direction cosine matrix and a magnetic interference component, and the unmanned helicopter dynamics model and the magnetic field distribution model are in a bidirectional coupling relationship.

[0045] S4, flight state adaptive compensation processing is performed on the magnetic interference estimation result to form magnetic field correction data, the flight state adaptive compensation processing including adjustment of a compensation coefficient and calculation of a compensation parameter. The compensation coefficient is dynamically modified according to the attitude angular velocity, acceleration and magnetic disturbance rate of change of the unmanned helicopter, and the compensation parameter is a geomagnetic direction vector of the unmanned helicopter dynamics model and the magnetic field distribution model.

[0046] S5, difference evaluation processing is performed on the magnetic field correction data to form a compensation feedback update result, the difference evaluation processing including magnetic field intensity fluctuation comparison and gradient feature analysis, the compensation feedback update result is transmitted to a flight control system, and the compensation feedback update result updates parameters of the improved Kalman filter algorithm to form a closed-loop compensation mechanism. The difference evaluation processing uses a nonlinear feedback adjustment formula, and the calculation formula of the nonlinear feedback adjustment formula is:

[0047] .

[0048] wherein, is a feedback gain adjustment amount, dimensionless, is a gain scaling coefficient, dimensionless, is a sensitivity coefficient, dimensionless, is a current time magnetic field gradient vector, dimensionless, is a last time magnetic field gradient vector, dimensionless, is a nonlinear suppression function, dimensionless.

[0049] The compensation feedback update result includes magnetic field intensity fluctuation data, spatial gradient feature information, parameter update amount and closed-loop control signal. The compensation feedback update result is transmitted using a UART interface and an SPI bus, the UART interface transmits the parameter update amount and the closed-loop control signal, and the SPI bus transmits the magnetic field intensity fluctuation data and the spatial gradient feature information.

[0050] The unmanned helicopter magnetic interference real-time compensation system based on the improved Kalman filter includes:

[0051] A sensor module, the sensor module collecting magnetic field and attitude information during flight of the unmanned helicopter, the sensor module outputting flight attitude and magnetic field joint sensing data, and the sensor module including a three-axis magnetometer, a three-axis accelerometer, a three-axis gyroscope and a GPS module.

[0052] The flight control master unit performs time synchronization and state estimation processing on the flight attitude and magnetic field combined sensing data to form a magnetic interference estimation result. The time synchronization and state estimation processing adopts an improved Kalman filter algorithm, which is based on an unmanned helicopter dynamics model and a magnetic field distribution model to establish a joint state space equation.

[0053] The storage unit stores the flight control program, the aeromagnetic compensation algorithm, the historical compensation parameters, and the flight data.

[0054] The communication unit transmits the magnetic interference estimation result. The communication unit includes a wireless communication module and a data interface.

[0055] The execution unit adjusts the flight attitude of the unmanned helicopter according to the magnetic interference estimation result. The execution unit includes a motor controller and a rudder controller.

[0056] The improved Kalman filter-based unmanned helicopter magnetic interference real-time compensation method realizes real-time compensation of unmanned helicopter magnetic interference through an improved Kalman filter, with key features such as dynamic adjustment of compensation coefficients, integration of multi-source data, and closed-loop feedback control. Through dynamic correction of flight attitude, acceleration, and magnetic disturbance rate, the compensation process can adapt to changes in flight state, improving the stability and adaptability of the system. The improved Kalman filter-based unmanned helicopter magnetic interference real-time compensation system continuously optimizes the compensation effect through real-time evaluation of magnetic field intensity fluctuations and gradient characteristics, and improves the magnetic interference estimation accuracy using multi-source sensor data, ensuring smooth and accurate flight.

[0057] Embodiment Two

[0058] This embodiment is an improved Kalman filter-based unmanned helicopter magnetic interference real-time compensation method and system. Through dimensionless processing of the sensor data of the unmanned helicopter, real-time compensation in a magnetic interference environment is achieved to improve the flight stability of the aircraft. The specific implementation is as follows:

[0059] 1. Data input preparation

[0060] During a flight, the sensor module of the unmanned helicopter collected the following data:

[0061] Magnetometer data in Gauss: 45.2, 37.8, 23.5. Accelerometer data in m / s 2 : 0.12, 0.05, -0.03. Gyroscope data in rad / s: 0.05, -0.02, 0.08. GPS data in degrees: 37.7749, -122.4194, 150.

[0062] 2. Dimensionless process

[0063] Normalization is to normalize the original data, so that the data is independent of physical units and suitable for different scenarios. The maximum value normalization method is used, and the specific steps are as follows:

[0064] Magnetometer data: the maximum geomagnetic field strength is 50 Gauss.

[0065]

[0066] Accelerometer data: the maximum gravity acceleration is 9.8 m / s 2 .

[0067] .

[0068] Gyroscope data: the maximum angular velocity is 10 rad / s.

[0069]

[0070] GPS data: longitude range [-180°, 180°], latitude range [-90°, 90°], height range [0, 5000] meters.

[0071]

[0072]

[0073]

[0074] Normalized GPS data is:

[0075]

[0076] 3. Current state estimate

[0077] The flight control system outputs the current state estimate, expressed as a dimensionless quantity:

[0078]

[0079] 4. Observation residual calculation

[0080] According to the model of the flight control system, the geomagnetic reference vector at the current time is obtained:

[0081]

[0082] Calculate the observation residual:

[0083]

[0084]

[0085] 5. Dynamic gain matrix and weight matrix

[0086] The flight control system uses the following values to dynamically adjust the gain matrix and weight matrix:

[0087] Dynamic gain matrix: The dynamic gain matrix is used to balance the weight of prior estimation and current observation, which is adjusted adaptively by Kalman filter algorithm according to the state estimation error of the system. The current estimation value has a higher degree of confidence in the previous estimation result, which is set based on experience , which is suitable for a less noisy environment.

[0088] The linear weight matrix is used to balance the parameters of the linear part of the Kalman filter that affect the state estimation, which is adjusted according to the size of the observation residual and the trust degree of the system to each sensor. The nonlinear weight matrix controls the influence of the nonlinear adjustment part on the estimation result, which is related to the nonlinear characteristics of the system, the nonlinear factors of the observation error and the dynamic changes of the system. Set , .

[0089] 6. Calculate the square of the two-norm of the residual vector

[0090] Calculate the square of the two-norm of the residual vector:

[0091]

[0092] 7. Hyperbolic tangent function calculation

[0093] Calculate the nonlinear adjustment term:

[0094]

[0095] First, calculate :

[0096]

[0097]

[0098]

[0099] Apply the hyperbolic tangent function , the amplitude range is [-1, 1]:

[0100]

[0101] 8. State estimation update

[0102] The improved Kalman filter algorithm includes prediction, update and adaptive adjustment. The joint state space equation adopts a dynamic weight and nonlinear correction coupling formula. The expression of the dynamic weight and nonlinear correction coupling formula is:

[0103] .

[0104] wherein, is the next time state estimation result, dimensionless, is the current state estimation vector, dimensionless, is the observation residual, dimensionless, is the dynamic gain matrix, dimensionless, the value range is 0-1, is the linear weight matrix, dimensionless, is the nonlinear weight matrix, dimensionless, is the two norm square of the residual vector, dimensionless, is the hyperbolic tangent function, dimensionless, the amplitude range is [-1, 1].

[0105] The current state estimation value is: .

[0106] Calculate the state estimation update:

[0107]

[0108]

[0109] 9. Output magnetic interference estimation result

[0110] The magnetic interference estimation result is:

[0111]

[0112] The geomagnetic reference vector estimation is: .

[0113] 10. Subsequent data transmission and compensation

[0114] Magnetic interference estimation result: through the wireless communication module, the magnetic interference estimation result will be transmitted to the flight control system.

[0115] Compensation parameters: the flight control system uses the estimation result for attitude adjustment and updates the compensation coefficient.

[0116] Data will continue to be transmitted and updated in real time to ensure that the unmanned helicopter can fly stably in a strong magnetic interference environment.

[0117] Through the above steps, real-time estimation and compensation of magnetic interference are realized. Normalized magnetometer, accelerometer, gyroscope and GPS data ensure the applicability and reliability of the algorithm, so that the system works stably under various flight conditions. The flight control system uses the real-time magnetic interference estimation result to accurately adjust the attitude of the aircraft, ensuring flight stability and precise control in a strong magnetic interference environment.

[0118] Embodiment Three

[0119] This embodiment is a real-time compensation method and system for magnetic interference of unmanned helicopters based on improved Kalman filtering. The dimensional processing and feedback gain adjustment optimize the flight attitude control of unmanned helicopters in a magnetic interference environment, ensuring flight stability. The specific implementation is as follows:

[0120] The difference evaluation process uses a nonlinear feedback adjustment formula, and the calculation formula of the nonlinear feedback adjustment formula is:

[0121] .

[0122] wherein, is the feedback gain adjustment quantity, dimensionless, is the gain scaling coefficient, dimensionless, is the sensitivity coefficient, dimensionless, is the current magnetic field gradient vector, dimensionless, is the magnetic field gradient vector at the last time, dimensionless, is a nonlinear suppression function, dimensionless.

[0123] 1. System initialization and data acquisition

[0124] During the flight of the unmanned helicopter, the flight control system first initializes, acquires and records the magnetic field and attitude data. The original data are as follows:

[0125] Attitude parameter initialization: Roll: 0°, Pitch: 0°, Yaw: 0°.

[0126] Magnetic field data acquisition:

[0127] The current magnetic field intensity is 0.32, 0.52, 0.75, and the unit is T. The magnetic field intensity at the last time is 0.31, 0.5, 0.77, and the unit is T.

[0128] Magnetic field gradient calculation:

[0129] The current magnetic field gradient is 0.03, 0.02, -0.02, and the unit is T / m. The magnetic field gradient at the last time is 0.02, 0.01, -0.01, and the unit is T / m.

[0130] 2. Dimensional processing and dimensionless

[0131] To meet the requirements of dimensionless formula, the original data needs to be converted into dimensionless values. The dimensionless process depends on the reference value, usually the range or maximum value of the sensor.

[0132] Reference value:

[0133] Maximum magnetic field strength , Maximum magnetic field gradient .

[0134] Magnetic field data dimensionless:

[0135]

[0136]

[0137] Magnetic field gradient data dimensionless:

[0138]

[0139]

[0140] After dimensionless processing, the units of magnetic field and magnetic field data are eliminated, and the data becomes dimensionless value.

[0141] 3. Difference evaluation and gain calculation

[0142] The dimensionless magnetic field gradient data is used for difference evaluation and gain adjustment.

[0143] Calculate the magnetic field gradient difference:

[0144]

[0145]

[0146] Nonlinear suppression function application:

[0147] Gain scaling factor is a coefficient used to control the size of feedback gain, adjust the gain scale of the system, affect the compensation effect, and select the appropriate value helps to avoid overcompensation or slow response problems. Sensitivity factor is used to control the sensitivity of feedback gain, indicating the sensitivity of feedback adjustment to magnetic field gradient changes. Determines the response degree of feedback gain adjustment to magnetic field gradient difference. Set gain scaling factor , sensitivity factor .

[0148] Calculate the feedback gain adjustment:

[0149]

[0150] 4. Compensation feedback update result

[0151] Magnetic field strength fluctuation data: After dimensionless, the difference of magnetic field strength is:

[0152]

[0153]

[0154] Spatial gradient feature information: The difference of magnetic field gradient is 0.1732.

[0155] Feedback gain adjustment amount: According to the nonlinear feedback formula, the calculation result is .

[0156] Closed-loop control signal: Use dimensionless feedback gain adjustment amount Update the parameters of Kalman filter algorithm to optimize the attitude control signal of flight control system.

[0157] 5. Data transmission and compensation update

[0158] The compensation feedback update result is transmitted to the flight control system through the following two interfaces:

[0159] UART interface: Transmit dimensionless feedback gain adjustment amount , used to update the parameters of Kalman filter.

[0160] SPI bus: Transmit magnetic field fluctuation data and spatial gradient feature information 0.1732 for further compensation adjustment by flight control system.

[0161] 6. Closed-loop compensation and flight control adjustment

[0162] The flight control system adjusts the flight attitude according to the compensation feedback result and updates the parameters of Kalman filter. Through the closed-loop control system, the flight attitude control is optimized to reduce the influence of magnetic interference on flight control, ensuring flight precision and stability.

[0163] Through the above steps, real-time compensation of magnetic interference during unmanned helicopter flight is realized. Through dimensionless processing of magnetic field data and magnetic field gradient data, combined with appropriate gain scaling factor and sensitivity coefficient setting, the system adjusts the parameters of Kalman filter to ensure the stability of flight attitude. Experimental results show that appropriate feedback gain adjustment can significantly reduce the influence of magnetic interference on flight control, improve the response speed and control accuracy of the system, and ensure the stable flight of unmanned helicopter in complex magnetic field environment.

[0164] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A real-time compensation method for magnetic interference of unmanned helicopter based on improved Kalman filter, characterized in that, Comprising: S1, responding to the trigger instruction received by the unmanned helicopter flight control system to form a compensation start signal, the response processing including flight control program initialization, attitude parameter loading and sensor self-checking operation; S2, time synchronization processing of the magnetic field joint sensor data collected during the flight of the unmanned helicopter to form a multi-source flight data set, the time synchronization processing using a unified timestamp calibration mechanism; S3, state estimation processing of the multi-source flight data set to form a magnetic interference estimation result, the state estimation processing using an improved Kalman filter algorithm, the improved Kalman filter algorithm using a joint state space equation, the joint state space equation including an unmanned helicopter dynamics model and a magnetic field distribution model; the improved Kalman filter algorithm includes prediction, update and adaptive adjustment, the joint state space equation using a dynamic weight and nonlinear correction coupled formula, the expression of the dynamic weight and nonlinear correction coupled formula being: ， wherein, is a next time state estimation result, is a current state estimation vector, is an observation residual, is a dynamic gain matrix, with a value range of 0-1, is a linear weight coefficient, is a nonlinear weight coefficient, is a squared two-norm of a residual vector, is a hyperbolic tangent function, with an amplitude range of [-1, 1]; the unmanned helicopter dynamics model outputs the current state estimation vector, which includes attitude angle state quantities and angular velocity state quantities; the magnetic field distribution model outputs the observation residual, which includes a geomagnetic reference vector, a direction cosine matrix, and a magnetic interference component; the unmanned helicopter dynamics model and the magnetic field distribution model are in a bidirectional coupling relationship. S4, flight state adaptive compensation processing of the magnetic interference estimation result to form magnetic field correction data, the flight state adaptive compensation processing including adjusting compensation coefficients and calculating compensation parameters; S5, difference evaluation processing of the magnetic field correction data to form a compensation feedback update result, the difference evaluation processing including magnetic field intensity fluctuation comparison and gradient feature analysis, the compensation feedback update result being transmitted to the flight control system, the compensation feedback update result updating the parameters of the improved Kalman filter algorithm to form a closed-loop compensation mechanism.

2. The improved Kalman filter based real-time magnetic interference compensation method for unmanned helicopter according to claim 1, characterized in that: The flight control program initialization includes flight control parameter loading, attitude control loop PID initial value setting and magnetometer bias automatic correction, the attitude parameter loading includes attitude angle calculation, and the sensor self-checking operation includes amplitude and noise characteristic detection.

3. The improved Kalman filter based real-time magnetic interference compensation method for unmanned helicopter according to claim 1, characterized in that: The magnetic field joint sensor data includes magnetometer data, accelerometer data, gyroscope data and GPS data.

4. The improved Kalman filter based real-time magnetic interference compensation method for unmanned helicopter according to claim 1, characterized in that: The compensation coefficients are dynamically modified according to the attitude angular velocity, acceleration and magnetic disturbance rate of the unmanned helicopter, and the compensation parameters are the geomagnetic direction vectors of the unmanned helicopter dynamics model and the magnetic field distribution model.

5. The improved Kalman filter based real-time magnetic interference compensation method for unmanned helicopter according to claim 1, characterized in that: The difference evaluation processing uses a nonlinear feedback adjustment formula, and the calculation formula of the nonlinear feedback adjustment formula is: ， wherein, is a feedback gain adjustment, is a gain scaling factor, is a sensitivity factor, is a current time magnetic field gradient vector, is a previous time magnetic field gradient vector, is a hyperbolic tangent function.

6. The improved Kalman filter based real-time magnetic interference compensation method for unmanned helicopter according to claim 1, characterized in that: The compensation feedback update result includes magnetic field intensity fluctuation data, spatial gradient feature information, parameter update amount and closed-loop control signal.

7. The improved Kalman filter based real-time magnetic interference compensation method for unmanned helicopter according to claim 6, characterized in that: The compensation feedback update result is transmitted using a UART interface and an SPI bus, the UART interface transmitting the parameter update amount and the closed-loop control signal, and the SPI bus transmitting the magnetic field intensity fluctuation data and the spatial gradient feature information.

8. An unmanned helicopter magnetic interference real-time compensation system based on improved Kalman filtering, characterized in that, Comprising: A sensor module that collects magnetic field and attitude information during the flight of the unmanned helicopter, the sensor module outputting flight attitude and magnetic field joint sensor data, the sensor module including a three-axis magnetometer, a three-axis accelerometer, a three-axis gyroscope and a GPS module; The flight control master unit performs time synchronization and state estimation processing on the flight attitude and magnetic field combined sensing data to form a magnetic interference estimation result, the time synchronization and state estimation processing adopts an improved Kalman filtering algorithm, the improved Kalman filtering algorithm establishes a combined state space equation based on an unmanned helicopter dynamics model and a magnetic field distribution model; the improved Kalman filtering algorithm includes prediction, update and adaptive adjustment, the combined state space equation adopts a dynamic weight and nonlinear correction coupled formula, and the expression of the dynamic weight and nonlinear correction coupled formula is: ， wherein, is a next time state estimation result, is a current state estimation vector, is an observation residual, is a dynamic gain matrix, with a value range of 0-1, is a linear weight coefficient, is a nonlinear weight coefficient, is a squared two-norm of a residual vector, is a hyperbolic tangent function, with an amplitude range of [-1, 1]; the unmanned helicopter dynamics model outputs the current state estimation vector, which includes attitude angle state quantities and angular velocity state quantities; the magnetic field distribution model outputs the observation residual, which includes a geomagnetic reference vector, a direction cosine matrix, and a magnetic interference component; the unmanned helicopter dynamics model and the magnetic field distribution model are in a bidirectional coupling relationship. A storage unit stores a flight control program, a geomagnetic compensation algorithm, historical compensation parameters and flight data; A communication unit transmits the magnetic interference estimation result, and the communication unit includes a wireless communication module and a data interface; An execution unit adjusts the flight attitude of the unmanned helicopter according to the magnetic interference estimation result, and the execution unit includes a motor controller and a rudder controller.

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