CN110793517B - Broadband micro angular velocity measurement method based on multi-rate fusion technology - Google Patents
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/18—Stabilised platforms, e.g. by gyroscope
Abstract
The invention discloses a broadband micro angular velocity measurement method based on a multi-rate fusion technology, which comprises the following steps: (1) Fixing two sensors on a rotary table, enabling sensitive axes of the two sensors to be collinear with a rotary shaft of the rotary table, connecting a signal wire to a collection card, and starting collection after power is turned on; (2) Synchronizing sampling rates of output signals of the two sensors; (3) And constructing a state equation and an observation equation, and carrying out Kalman filtering fusion on the synchronized signals. The two sensors are an MHD micro-angle vibration sensor and a high-precision gyroscope respectively, and in the step (2), the high-precision gyroscope is up-sampled or the MHD micro-angle vibration sensor is down-sampled, so that the sampling rates of the two sensors are uniform.
Description
Technical Field
The invention belongs to the technical field of test and measurement, and particularly relates to a broadband micro angular velocity measurement method based on a multi-rate fusion technology.
Background
High-precision spacecrafts represented by high-resolution remote sensing satellites play an important role in the fields of earth observation, deep space exploration, laser communication and the like. However, due to the existence of various micro-vibration disturbance sources such as a momentum wheel high-speed rotating component, a solar cell array driving stepping component, space debris collision impact and the like, a spacecraft and a payload thereof can be disturbed by micro-vibration with small amplitude in an in-orbit running process. The frequency spectrum contains high-frequency angular micro-vibration of thousands of hertz, and the imaging quality of high-resolution earth observation of a spacecraft is seriously affected, so that a vibration reduction system must be designed for loads such as a high-resolution camera and the like, and the active inhibition capability of a load platform on the micro-angular vibration is improved.
In order to detect the micro-angle vibration signal of the load platform and feed back the micro-angle vibration signal to the active vibration damping system in real time, the micro-angle vibration sensor needs to have the characteristics of wide frequency band, high precision, small volume, shock resistance, long service life and the like. The micro-angle vibration sensor based on the principle of Magnetohydrodynamics (MHD) can meet the requirements, is particularly suitable for high-frequency micro-angle vibration measurement, and is a relatively mature satellite platform micro-angle vibration sensor at present.
However, due to principle limitations, the MHD micro-angle vibration sensor does not respond well to low frequency angle vibration signals below 1Hz, which is embodied as low frequency amplitude attenuation and phase error increase, and cannot respond to direct current signals, so that low frequency motion information of a spacecraft payload cannot be effectively measured, and therefore, the low frequency error of the MHD micro-angle vibration sensor must be effectively compensated.
Chinese patent CN106840155a proposes a scheme for fusion measurement of MHD micro-angle vibration sensor and MEMS gyroscope, but the scheme is only suitable for fusion of sensors with the same rate, and has no requirement of taking the noise characteristics of the sensors as fusion basis and evaluation index of fusion effect, and has great limitation.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a broadband micro angular velocity measurement method based on a multi-rate fusion technology. The method aims to effectively compensate amplitude attenuation and phase errors of the MHD micro-angle vibration sensor under the low-frequency condition by utilizing the characteristic that the high-precision gyroscope performs well under the low-frequency (< 10 Hz) vibration condition, and meanwhile, the original ideal high-frequency characteristic of the MHD micro-angle vibration sensor is ensured, and the fluctuation of the output amplitude along with the frequency is controlled in a smaller range, so that full-band measurement is realized.
The invention aims at realizing the following technical scheme:
a wideband micro angular velocity measurement method based on a multi-rate fusion technology comprises the following steps:
(1) Fixing two sensors on a rotary table, enabling sensitive axes of the two sensors to be collinear with a rotary shaft of the rotary table, connecting a signal wire to a collection card, and starting collection after power is turned on;
(2) Synchronizing sampling rates of output signals of the two sensors;
(3) And constructing a state equation and an observation equation, and carrying out Kalman filtering fusion on the synchronized signals.
Further, the two sensors are an MHD micro-angle vibration sensor and a high-precision gyroscope respectively, and in the step (2), the high-precision gyroscope is up-sampled or the MHD micro-angle vibration sensor is down-sampled, so that the sampling rates of the two sensors are uniform;
the CIC filter, namely a cascade integral comb filter, is used for eliminating image frequency components introduced by up-sampling or aliasing components caused by down-sampling; then applying Noble equivalence transformation, equivalent exchange filter and decimator or interpolator positions.
Further, the step (3) includes the steps of:
(301) Modeling the motion of an object to be observed, determining a state transition matrix and process noise, and listing a state transition differential equation of the object;
(302) Modeling the two sensors, determining an observation matrix and observation noise, and listing the observation equation of the sensor group;
(303) Performing Kalman recursion estimation, and dividing the Kalman recursion estimation into two parts: predicting and updating; wherein predicting comprises performing a priori estimates of state values, a priori estimates of noise covariance; updating the posterior estimation including calculating the Kalman gain and the state value and the noise covariance;
(304) And obtaining a state value through the observed values of the two sensors, and finishing the fusion of the two sensors.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
1. the invention creatively uses the scheme of synchronous Kalman fusion, breaks through the limitation that the signal transmission rates of two sensors in the scheme of the reference patent are necessarily the same, can perform analog-digital sensor multi-rate fusion, double analog sensor multi-rate fusion and double digital sensor multi-rate fusion, expands the application range of the fusion scheme and improves the flexibility of the MHD micro-angle vibration sensor combination measurement scheme.
2. And in the second step, a CIC filter is selected as an anti-aliasing filter before extraction or a smoothing filter after interpolation, and the filter has a simple structure, is provided with an integrator and an adder, has no multiplier, has high calculation efficiency, and is very suitable for a system with high requirements on real-time performance, such as angular vibration measurement. On the basis, the novel equivalent transformation is applied to the extraction structure formed by the CIC filter and the extractor, so that a part of unnecessary filtering operation is reduced, and the time delay of the system is reduced to the greatest extent.
3. Different from the reference patent, the Kalman filtering fusion is selected, the state at the current moment is estimated only by the state at the previous moment, and the possibility is provided for the multi-sensor online fusion.
Drawings
FIG. 1 is a flow chart of the steps of the present invention.
Fig. 2 is a block diagram of decimated filtering to which Noble equivalence is applied.
Fig. 3 is a block diagram of CIC filter decimation to which Noble equivalence is applied.
Detailed Description
The invention is described in further detail below with reference to the drawings and the specific examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The invention provides a wideband micro angular velocity measurement method based on a multi-rate fusion technology, which is used for obtaining an optimized angular velocity value output by carrying out multi-rate fusion on an MHD micro angular vibration sensor and a high-precision gyroscope, thereby realizing full-band measurement. For better illustration, the MEMS gyroscope is selected as the high-precision gyroscope in this embodiment.
As shown in fig. 1, the method mainly comprises the following steps:
step one: acquisition of
The two sensors are fixed on the rotary table, so that the sensitive axes of the two sensors are collinear with the rotary shaft of the rotary table, then the signal wire is connected to the acquisition card, and the acquisition program is operated to start acquisition after the power is turned on.
Step two: fusion of
1. The sampling rate is synchronized.
Downsampling of the MHD micro-angle vibration sensor or upsampling of the MEMS gyroscope is described herein as an example of downsampling of the MHD.
The sampling frequency of the MEMS gyroscope is 2KHz, the sampling frequency of the MHD micro-angle vibration sensor is 10KHz, and 5 times of downsampling is required for reducing the MHD sampling rate to be consistent with the MEMS.
The locations of the filter and the decimator are exchanged by applying the Noble identity, so as to improve the response speed of the system, as shown in fig. 2.
The CIC filter is selected as a low-pass filter of the extraction structure, and the system function is as follows:
wherein M is the order of the CIC filter,is an integrator, H 2 (z)＝1-Z -M Is a comb filter.
Applying the Noble equivalent to the CIC filter and decimator is to swap the positions of the downsampler and comb filter, the structure is shown in fig. 3.
2. Kalman filter fusion
And establishing a state equation and a measurement equation, and carrying out Kalman recursion estimation.
Wherein, establish the state equation:
x k ＝x k-1 +w k
x k ＝x k-1 +0.01
here, x k Is the angular velocity of an object, and has the unit of deg/s, w k Is process noise, which is regarded as a random process, w, because the motion condition of a target object cannot be known in advance k Take 0.01 according to the experience value.
Establishing an observation equation:
z k ＝H k X k +V k
wherein H is 1 For a sensor observation matrix, the dimension is 2x1, two components s 1 、s 2 The scale factors of the two sensors, respectively. H 1 The noise matrix is measured for the sensor and is a 2x1 dimensional matrix, and two components r 1 、r 2 The noise variance, z, of the two sensors, respectively 1 、z 2 The unit of the voltage output value of the two collected sensors at the kth moment is V.
After the state equation and the observation equation are established, four parameters required by Kalman iterative fusion are determined so far, and the four parameters are respectively a state transition matrix F:1, observation matrix H:process noise Q: w (w) k Measuring noise R: />
The following is a Kalman iterative fusion process:
prediction part:
1) Estimation using k-1 time instantCalculating a priori estimates of the current k moment +.>
2) Using the estimate P of time k-1 k-1|k-1 Calculating the current k momentPrior estimation covariance matrix P k|k-1 ：
P k|k-1 ＝P k-1|k-1 +w k
Wherein P is k Is a 1x1 matrix because in this example only one state value, the angular velocity, is the initial value of 1.
An updating section:
3) Calculating Kalman gain for measuring accuracy degree:
4) Using Kalman gain K k Correcting the prior estimate based on the measured value:
wherein, kalman gain K k Is a 1x2 vector because one state value corresponds to two sensor measurements.
5) Updating the posterior estimation covariance matrix:
wherein I is an identity matrix.
And finally, transmitting the acquired sensor voltage output sequence into a fusion program, and carrying out iterative updating on the sequence to obtain a fused angular velocity estimation sequence.
So far, the more accurate estimated value of the angular velocity is obtained by multi-rate fusion of the two sensors, and the wide-band angular velocity measurement is completed.
The invention is not limited to the embodiments described above. The above description of specific embodiments is intended to describe and illustrate the technical aspects of the present invention, and is intended to be illustrative only and not limiting. Numerous specific modifications can be made by those skilled in the art without departing from the spirit of the invention and scope of the claims, which are within the scope of the invention.
Claims (1)
1. The wideband micro angular velocity measurement method based on the multi-rate fusion technology is characterized by comprising the following steps:
(1) Fixing two sensors on a rotary table, enabling sensitive axes of the two sensors to be collinear with a rotary shaft of the rotary table, connecting a signal wire to a collection card, and starting collection after power is turned on; the two sensors are an MHD micro-angle vibration sensor and a high-precision gyroscope respectively;
(2) Synchronizing sampling rates of output signals of the two sensors; up-sampling the high-precision gyroscope or down-sampling the MHD micro-angle vibration sensor to unify the sampling rates of the high-precision gyroscope and the MHD micro-angle vibration sensor; the CIC filter, namely a cascade integral comb filter, is used for eliminating image frequency components introduced by up-sampling or aliasing components caused by down-sampling; then applying Noble equivalent transformation, and equivalently exchanging the positions of the filter and the decimator or the interpolator;
(3) Constructing a state equation and an observation equation, and carrying out Kalman filtering fusion on the synchronized signals; the method comprises the following steps:
(301) Modeling the motion of an object to be observed, determining a state transition matrix and process noise, and listing a state transition differential equation of the object; wherein, establish the state equation:
x k ＝x k-1 +w k
x k ＝x k-1 +0.01
here, x k Is the angular velocity of an object, and has the unit of deg/s, w k Is process noise, which is regarded as a random process, w, because the motion condition of a target object cannot be known in advance k Taking 0.01 according to an empirical value;
(302) Modeling the two sensors, determining an observation matrix and observation noise, and listing the observation equation of the sensor group;
z k ＝H k X k +V k
wherein H is k For a sensor observation matrix, the dimension is 2x1, two components s 1 、s 2 The scale factors of the two sensors respectively; v (V) k The noise matrix is measured for the sensor and is a 2x1 dimensional matrix, and two components r 1 、r 2 The noise variance, z, of the two sensors, respectively 1 、z 2 The unit of the voltage output value of the two collected sensors is V;
(303) Performing Kalman recursion estimation, and dividing the Kalman recursion estimation into two parts: predicting and updating; wherein predicting comprises performing a priori estimates of state values, a priori estimates of noise covariance; updating the posterior estimation including calculating the Kalman gain and the state value and the noise covariance; four parameters required for performing the kalman iterative fusion are determined, and the four parameters are respectively a state transition matrix F:1, observation matrix H:process noise Q: w (w) k Measuring noise R: />
(304) Obtaining a state value through the observed values of the two sensors, and finishing fusion of the two sensors; the kalman iterative fusion process is as follows:
prediction part:
1) Estimation using k-1 time instantCalculating a priori estimates of the current k moment +.>
2) Using the estimate P of time k-1 k-1|k-1 Calculating prior estimation covariance matrix P of current k moment k|k-1 ：
P k|k-1 ＝P k-1|k-1 +w k
Wherein P is k Is a 1x1 matrix, the state value is only angular velocity, and the initial value is 1;
an updating section:
3) Calculating Kalman gain for measuring accuracy degree:
4) Using Kalman gain K k Correcting the prior estimate based on the measured value:
wherein, kalman gain K k Is a 1x2 vector because one state value corresponds to two sensor measurements; z 1k 、z 2k The voltage output value of the two collected sensors at the kth moment is obtained;
5) Updating the posterior estimation covariance matrix:
wherein I is an identity matrix;
and finally, transmitting the acquired sensor voltage output sequence into a fusion program, and carrying out iterative updating on the sequence to obtain a fused angular velocity estimation sequence.
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