CN109655057B - Filtering optimization method and system for accelerator measurement value of six-push unmanned aerial vehicle - Google Patents
Filtering optimization method and system for accelerator measurement value of six-push unmanned aerial vehicle Download PDFInfo
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Abstract
The invention relates to the technical field of six-push unmanned aerial vehicles, in particular to a filtering optimization method and a system for a measured value of an accelerator of a six-push unmanned aerial vehicle; firstly, filtering an acceleration measurement value at the K moment by a Kalman filter; then acquiring an acceleration measured value and an acceleration predicted value at the moment K; thereby obtaining the covariance of the K moment; then calculating the Kalman gain at the K moment; then obtaining the optimal predicted value at the moment K; performing iterative computation through the optimal predicted value at the moment K to obtain the optimal predicted value at the moment K + 1; the method carries out real-time filtering optimization on the measured value of the accelerator to obtain the measured data which is closer to the real acceleration; the acceleration data after the filtering optimization is subjected to secondary integral processing, more accurate displacement data of the underwater unmanned aerial vehicle can be obtained, the probability and error of occurrence of an unreal extreme value of the measured acceleration are greatly reduced, and the precision of positioning of the underwater unmanned aerial vehicle through the nine-axis motion attitude module is improved.
Description
Technical Field
The invention relates to the technical field of six-push unmanned aerial vehicles, in particular to a filtering optimization method for accelerator measurement values of six-push unmanned aerial vehicles.
Background
At present, a six-push underwater unmanned aerial vehicle adopts an MPU605 six-axis motion attitude sensor and a processing component, and is combined with an IST (inertial measurement unit) win magnetometer to realize complete nine-axis motion attitude fusion output.
The underwater unmanned aerial vehicle can not realize underwater positioning through a GPS electromagnetic signal at present, the most common underwater positioning method is underwater acoustic echo technology positioning or an inertial navigator, the underwater unmanned aerial vehicle adopts a nine-axis motion attitude module to simulate the inertial navigator, and each axial displacement of the underwater unmanned aerial vehicle is converted through integral of a measurement value of an accelerometer (accelerator), so that an underwater position is obtained. However, the problem that the measurement data of the accelerometer in the nine-axis motion attitude module of the underwater unmanned aerial vehicle is inaccurate is that the displacement of the underwater unmanned aerial vehicle calculated by integral will generate jump and inaccuracy due to the fact that rough positioning is only carried out by pure application and more errors (measurement noise) and jump of unreal extreme values exist.
Disclosure of Invention
The invention mainly solves the technical problem of providing a filtering optimization method for accelerator measured values of a six-axis unmanned aerial vehicle, which carries out real-time filtering optimization on the accelerator measured values, greatly reduces the probability and error of occurrence of non-real extreme values of measured acceleration, and improves the precision of positioning the underwater unmanned aerial vehicle through a nine-axis motion attitude module; a filtering optimization system for the measured value of the accelerator of the six-wheeled unmanned aerial vehicle is also provided.
In order to solve the technical problems, the invention adopts a technical scheme that: the utility model provides a filtering optimization method of six-wheeled unmanned aerial vehicle accelerator measured value, it simulates inertial navigator through nine axis motion gesture module to measure the acceleration measured value through the accelerometer in nine axis motion gesture module, wherein, includes the following steps:
s1, filtering the acceleration measurement value at the moment K through a Kalman filter;
s2, acquiring the filtered acceleration measurement value at the K moment from the Kalman filter and setting the acceleration predicted value at the K moment through the acceleration measurement value at the K moment;
step S3, acquiring covariance of K time from a Kalman filter;
step S4, calculating Kalman gain at the K moment through the covariance at the K moment;
step S5, correcting the acceleration predicted value at the moment K by combining the acceleration measured value, the acceleration predicted value and the Kalman gain at the moment K to obtain the optimal predicted value at the moment K;
and step S6, performing iterative computation through the optimal predicted value at the moment K to obtain the optimal predicted value at the moment K +1, wherein the moment K +1 is the next moment of the moment K.
In step S2, the kalman filter predicts and sets a predicted acceleration value at time K by a predicted acceleration value at time K-1, where time K-1 is a time immediately preceding time K.
As a further improvement of the present invention, step S3 includes: and the Kalman filter obtains and updates the covariance at the K moment according to the covariance at the K-1 moment and the prediction deviation predicted by the Kalman filter.
As a further improvement of the present invention, step S4 includes: and calculating by combining the covariance at the K moment with the measurement deviation predicted by the accelerator to obtain the Kalman gain at the K moment.
As a further improvement of the present invention, step S6 includes: and obtaining and updating the covariance at the K +1 moment through the covariance at the K moment, repeating the steps from S1 to S5, and automatically and recursively performing iterative computation to obtain and update the optimal predicted value at the K +1 moment.
As a further development of the invention, the measured value of the acceleration at the moment K is
Wherein xk-1Measuring a measured acceleration measurement for the accelerator;
the predicted value of the acceleration at the time K is
Wherein r iskIs a constant.
As a further improvement of the invention, the covariance at time K is
Wherein, Pk-1Namely the covariance at the moment K-1, and Q is the prediction deviation predicted by the Kalman filter.
As a further improvement of the invention, the Kalman gain at time K is
Where R is the measured deviation of the accelerator prediction.
As a further improvement of the invention, the optimal predicted value at the time K is
Wherein H is a constant.
The utility model provides a be applied to filtering optimization system of six and push away unmanned aerial vehicle accelerator measurement value, its and be used for simulating nine motion gesture module connections of inertial navigation appearance, be provided with the accelerometer in the nine motion gesture module, wherein, include:
the Kalman filter is used for filtering the acceleration measurement value measured by the accelerometer;
the information acquisition module is used for acquiring the acceleration measurement value filtered by the Kalman filter;
the setting module is used for setting an acceleration predicted value;
an obtaining module, configured to obtain a covariance from the kalman filter;
and the calculation module is used for calculating Kalman gain through the covariance, and correcting the acceleration predicted value by combining the acceleration measured value, the acceleration predicted value and the Kalman gain to obtain the optimal predicted value.
The invention has the beneficial effects that: compared with the prior art, the method carries out real-time filtering optimization on the measured value of the accelerator to obtain the measured data which is closer to the real acceleration; the acceleration data after the filtering optimization is subjected to secondary integral processing, more accurate displacement data of the underwater unmanned aerial vehicle can be obtained, the probability and error of occurrence of an unreal extreme value of the measured acceleration are greatly reduced, and the precision of positioning of the underwater unmanned aerial vehicle through the nine-axis motion attitude module is improved.
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FIG. 1 is a block diagram of the steps of a method of filter optimization of six-push drone accelerator measurements of the present invention;
fig. 2 is a connection block diagram of the filtering optimization system for the measured value of the six-push unmanned aerial vehicle accelerator according to the present invention.
Detailed Description
A Kalman filter and a nine-axis motion attitude module (sensor) are arranged in the six-push unmanned aerial vehicle.
The nine-axis motion attitude module simulates an inertial navigator to position, and an accelerometer is arranged in the nine-axis motion attitude module and can measure an acceleration measurement value through the accelerometer.
A Kalman Filter (Kalman Filter) is an algorithm that uses a linear system state equation to perform optimal estimation of the system state by inputting and outputting observation data through the system. The essence of the kalman filter is an optimized autoregressive data processing algorithm (optimal regression data processing algorithm), which is the best and most efficient filter for processing the self-noise and measurement error of signals, and is widely applied to navigation, communication, control, image processing, sensor data fusion and other aspects. The core principle of the Kalman filter is to continuously recurse the estimated value and the covariance of the measured value at the next moment, so as to estimate the optimal estimated value, and only the covariance at the previous moment is kept.
The invention briefly describes the following work flow and principle of the kalman filter, as follows:
the principle of the kalman filter can be described using the following discrete control process:
state estimation
The state estimation is the beginning and important component links of Kalman filtering, the main process of the state estimation is to carry out quantitative inference on random quantity according to observation data, and the real-time estimation and prediction on the running state can be realized, which is the most significant of the state estimation on the Kalman filtering.
(II) state quantity
In the Kalman filtering algorithm, the state quantity of a filtering object is a random quantity and is interfered by noise, a measured value cannot be a real accurate value due to the existence of the noise, but the real accurate value can be estimated according to a series of observed values, so that the estimated value is as close to the real accurate value as possible, and the process is the optimal estimation. Kalman filtering is an optimal estimation process, in the process, the difference between an accurate value and an estimated value is an estimation error, the Kalman filtering adopts a recursion optimal estimation theory, and the optimal estimation is obtained by recursion of the estimated value and a measured value containing noise in a recursion form on a state space.
(III) Kalman algorithm process
Kalman filtering is mainly directed to a discrete variable control process in a filtering process aiming at an acceleration value. Recursion is carried out according to the previous and later moments, and the measured value and the predicted value of the system process are respectively as follows:
wherein the measured value is measured and read by a nine-axis motion attitude module (sensor) in real time, thereby
Is relatively simple, H is a parameter coefficient matrix of the measurement system (H is typically 1 if the measurement system has no gain), and it is necessary to first determine the noise (error) r of the measurement valuekA is a parameter predicted by the system process, uk-1Is a control quantity for the system process, i.e. represents the control of the system process x at time k, B is a parameter of the control quantity, q and r are the noise (error) for the system process and the measured value, respectively. Meanwhile, the covariance of the system state at the current time k needs to be solved through the time k-1 of the previous time:
wherein, the k moment and the k-1 moment respectively represent the k moment of the current moment point and the k-1 moment of the last moment point; the covariance of the general system state is the matrix, and Q is the matrix for estimating the noise from the r measurement value.
(IV) Kalman gain
In the estimation process of the system process and the measured value, the two formulas are time updating of the previous time and the current time, and Kalman filtering carries out state updating on system state parameters at the time k, wherein the state updating comprises a Kalman gain coefficient, an optimal estimated value of the system process at the time k and an optimal estimated value of measurement at the time k:
kalman gain:
the gain factor for solving the predicted value and the measured value based on the system process can be obtained by taking the system process into considerationThe error of the predicted value and the error of the measured value are reflected, and the influence of the error of the predicted value and the error of the measured value on the optimal estimated value is reflected; for the predicted value
Correcting and updating to obtain an optimal estimation value:
finally, updating the iterative calculation of the optimal estimated value for estimating the k +1 moment at the next moment, namely updating the covariance PkAnd updating by using the Kalman gain coefficient obtained by the previous solution:
as shown in fig. 1, the invention provides a filtering optimization method for a six-axis unmanned aerial vehicle accelerator measurement value, which simulates an inertial navigator through a nine-axis motion attitude module, so that an acceleration measurement value is measured through an accelerometer in the nine-axis motion attitude module, and the method comprises the following steps:
s1, filtering the acceleration measurement value at the moment K through a Kalman filter;
s2, acquiring the filtered acceleration measurement value at the K moment from the Kalman filter and setting the acceleration predicted value at the K moment through the acceleration measurement value at the K moment;
step S3, acquiring covariance of K time from a Kalman filter;
step S4, calculating Kalman gain at the K moment through the covariance at the K moment;
step S5, correcting the acceleration predicted value at the moment K by combining the acceleration measured value, the acceleration predicted value and the Kalman gain at the moment K to obtain the optimal predicted value at the moment K;
and step S6, performing iterative computation through the optimal predicted value at the moment K to obtain the optimal predicted value at the moment K +1, wherein the moment K +1 is the next moment of the moment K.
In the invention, the optimal predicted value is estimated by continuously recursing the covariance of the estimated value and the measured value at the next moment, and only the covariance at the previous moment is kept; real-time filtering optimization is carried out on the measured value of the accelerator, and measured data closer to the real acceleration are obtained; the acceleration data after the filtering optimization is subjected to secondary integral processing, more accurate displacement data of the underwater unmanned aerial vehicle can be obtained, the probability and error of occurrence of an unreal extreme value of the measured acceleration are greatly reduced, and the precision of positioning of the underwater unmanned aerial vehicle through the nine-axis motion attitude module is improved.
In step S2, the kalman filter predicts and sets an acceleration predicted value at the time K by using the acceleration predicted value at the time K-1, where the time K-1 is the previous time of the time K; acceleration measurement at time K of
Wherein xk-1Measured acceleration values measured by an accelerometer; the predicted value of the acceleration at the time K is
Wherein r iskIs a constant.
Wherein, step S3 includes: the Kalman filter obtains and updates the covariance of the K moment according to the covariance of the K-1 moment and the prediction deviation predicted by the Kalman filter; covariance at time K of
Wherein, Pk-1Namely the covariance at the moment K-1, and Q is the prediction deviation predicted by the Kalman filter.
Wherein, step S4 includes: calculating by combining the covariance at the K moment with the measurement deviation predicted by the accelerometer to obtain the Kalman gain at the K moment; kalman gain at time K of
Where R is the measured deviation predicted by the accelerometer.
Further, step S6 includes: obtaining and updating the covariance at the K +1 moment through the covariance at the K moment, repeating the steps from S1 to S5, and automatically recursively performing iterative computation to obtain and update the optimal predicted value at the K +1 moment; the optimal predicted value at the moment K is
Wherein H is a constant.
In the invention, the iterative calculation of the optimal estimation value for estimating the k +1 moment at the next moment is updated: covariance at time K +1 of
The invention is described in detail herein:
1. the measured and predicted values for the system process are respectively:
in the system process for predicting the current k time from the k-1 time of the previous time, the control quantity u is not used for predicting the accelerationk-1And the control quantity parameter B is suitable for predicting the current time k from the previous time k-1, so that the influence of the control quantity and the parameter is not considered, and the system process prediction parameter A is set to be 1; meanwhile, for the prediction of the measured value, because the measured value of the acceleration is measured and obtained by adopting a nine-axis motion attitude module (sensor), the gain of a measurement parameter does not exist, and H is set to be 1; and simultaneously updating the covariance of the system state:
wherein P isk-1I.e., the covariance at the last time k-1, Q is the predicted deviation, and Q is typically predetermined based on the deviation and is typically set constant against time.
2. Kalman gain:
where R is the measured deviation of the measured value, R is typically predetermined from the measured deviation, and is largely dependent on the measuring equipment and is typically set constant and does not change over time.
3. After the Kalman gain is obtained, the predicted value is corrected
Correcting and updating to obtain an optimal estimation value:
4. finally, updating the iterative calculation of the optimal estimated value for estimating the k +1 moment at the next moment, namely updating the covariance PkAnd updating by using the Kalman gain coefficient obtained by the previous solution:
calculating the optimal estimated value x at the current time kkAnd then, predicting the next moment k +1, recycling the steps once again, and automatically and recursively calculating the change curve of the optimal estimated value along the time by the Kalman filter.
As shown in fig. 2, the invention provides a filtering optimization system applied to a measured value of an accelerator of a six-wheeled unmanned aerial vehicle, which is connected with a nine-axis motion attitude module for simulating an inertial navigator, wherein an accelerometer is arranged in the nine-axis motion attitude module, and the system comprises:
the Kalman filter is used for filtering the acceleration measurement value measured by the accelerometer;
the information acquisition module is used for acquiring the acceleration measurement value filtered by the Kalman filter;
the setting module is used for setting an acceleration predicted value;
the acquisition module is used for acquiring covariance from a Kalman filter;
and the calculation module is used for calculating Kalman gain through covariance, and correcting the acceleration predicted value by combining the acceleration measured value, the acceleration predicted value and the Kalman gain to obtain the optimal predicted value.
And a filtering unit for filtering the acceleration measurement value is arranged in the Kalman filter.
And an acquisition unit for acquiring the acceleration measurement value at the current K moment after being filtered by the Kalman filter is arranged in the information acquisition module.
And a prediction unit for predicting and setting the acceleration predicted value at the K moment through the acceleration predicted value at the K-1 moment is arranged in the setting module.
The acquisition module is internally provided with an acquisition unit for acquiring covariance at the K-1 moment, a collection unit for acquiring predicted deviation predicted by the Kalman filter, and a covariance unit for calculating the covariance at the K moment by combining the covariance at the K-1 moment acquired by the acquisition unit and the predicted deviation predicted by the Kalman filter acquired by the collection unit, and updating data of the covariance at the K moment by the covariance unit at the same time so as to facilitate acquisition of the covariance at the K +1 moment.
The calculation module is internally provided with a gain unit used for calculating the Kalman gain at the K moment by combining the covariance at the K moment with the measurement deviation predicted by the accelerometer, a correction unit used for correcting the predicted acceleration value, and an optimal calculation unit used for calculating the optimal predicted value by combining the Kalman gain at the K moment obtained by calculation of the gain unit with the predicted acceleration value at the K moment.
The filtering optimization method for the measured value of the accelerator of the six-wheeled unmanned aerial vehicle can further perform Kalman filtering processing on the data acquired by the accelerometer to acquire measured data closer to the real acceleration; and the acceleration data after the filtering optimization is subjected to secondary integral processing, so that more accurate displacement data of the underwater unmanned aerial vehicle can be obtained.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes performed by the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (7)
1. A filtering optimization method for six-axis unmanned aerial vehicle accelerator measurement values simulates an inertial navigator through a nine-axis motion attitude module, so that the acceleration measurement values are measured through an accelerometer in the nine-axis motion attitude module, and the method is characterized by comprising the following steps of:
s1, filtering the acceleration measurement value at the moment K through a Kalman filter;
s2, acquiring the filtered acceleration measurement value at the K moment from the Kalman filter and setting the acceleration predicted value at the K moment through the acceleration measurement value at the K moment;
step S3, acquiring covariance of K time from a Kalman filter;
step S4, calculating Kalman gain at the K moment through the covariance at the K moment;
step S5, correcting the acceleration predicted value at the moment K by combining the acceleration measured value, the acceleration predicted value and the Kalman gain at the moment K to obtain the optimal predicted value at the moment K;
step S6, carrying out iterative computation through the optimal predicted value at the moment K to obtain the optimal predicted value at the moment K +1, wherein the moment K +1 is the next moment of the moment K;
acceleration measurement at time K of
Wherein xk-1Measuring a measured acceleration measurement for the accelerator;
the predicted value of the acceleration at the time K is
Wherein r iskIs a constant;
covariance at time K of
Wherein, Pk-1Namely the assistant party at the moment of K-1The difference, Q, is the prediction bias predicted by the kalman filter.
2. The method of claim 1, wherein the step S3 includes: and the Kalman filter obtains and updates the covariance at the K moment according to the covariance at the K-1 moment and the prediction deviation predicted by the Kalman filter.
3. The method for filter optimization of accelerator measurement values of six-wheeled drone of claim 1 or 2, wherein step S4 includes: and calculating by combining the covariance at the K moment with the measurement deviation predicted by the accelerator to obtain the Kalman gain at the K moment.
4. The method of claim 1, wherein the step S6 includes: and obtaining and updating the covariance at the K +1 moment through the covariance at the K moment, repeating the steps from S1 to S5, and automatically and recursively performing iterative computation to obtain and update the optimal predicted value at the K +1 moment.
5. The method of claim 4, wherein Kalman gain at time K is
Where R is the measured deviation of the accelerator prediction.
6. The method of claim 5, wherein the optimal predicted value at time K is
Wherein H is a constant.
7. The filtering optimization system for the six-push unmanned aerial vehicle accelerator measurement value is applied to the filtering optimization system for the six-push unmanned aerial vehicle accelerator measurement value, the filtering optimization system is connected with a nine-axis motion attitude module for simulating an inertial navigator, an accelerometer is arranged in the nine-axis motion attitude module, and the filtering optimization system is characterized by comprising:
the Kalman filter is used for filtering the acceleration measurement value measured by the accelerometer;
the information acquisition module is used for acquiring the acceleration measurement value filtered by the Kalman filter;
the setting module is used for setting an acceleration predicted value;
an obtaining module, configured to obtain a covariance from the kalman filter;
and the calculation module is used for calculating Kalman gain through the covariance, and correcting the acceleration predicted value by combining the acceleration measured value, the acceleration predicted value and the Kalman gain to obtain an optimal predicted value.
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