CN107747940A - A kind of Multi-sensor Fusion guider based on FPGA and RTOS - Google Patents
A kind of Multi-sensor Fusion guider based on FPGA and RTOS Download PDFInfo
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- CN107747940A CN107747940A CN201710329851.4A CN201710329851A CN107747940A CN 107747940 A CN107747940 A CN 107747940A CN 201710329851 A CN201710329851 A CN 201710329851A CN 107747940 A CN107747940 A CN 107747940A
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- 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
- G01C21/005—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 with correlation of navigation data from several sources, e.g. map or contour matching
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Abstract
The invention discloses a kind of Multi-sensor Fusion guider based on FPGA and RTOS, based on FPGA and RTOS Multi-sensor Fusion guider on the basis of the Big Dipper/INS integrated navigation systems, by increasing the sensors such as three axle magnetometer, barometer and laser range finder, processing is filtered to gathered data using distributed Kalman filter, to improve the navigation accuracy of the combined navigation device；The condition selecting wave filter of different external environments should be handled by design decoding data, and real-time operation is carried out by RTOS, can be with the real-time, reliability and fault freedom of enhanced navigation system.The present invention solves the problems, such as to face that data processing speed is slow under multi-sensor device, data fusion efficiency is low in the prior art, and by the design of logic judgment module, finally improves the efficiency and precision of guider.
Description
Technical field
The invention belongs to navigation field, more particularly to a kind of Multi-sensor Fusion guider based on FPGA and RTOS.
Background technology
In the early 1990s, laboratory director federation of U.S. Department of Defense proposes the concept of " data fusion ", refer to
The data that will be collected by different sensors, associated and merged, finally obtain accurate target identification, state estimation and prestige
The side of body is assessed.And complicated war can not be met in traditional navigation field, traditional single navigation system, its precision and reliability
Field environment, so the assembling of different types of navigation system is cooperated in the unified platform realizes more efficient navigator fix system
System turns into direction of concern.
Integrated navigation system is also a kind of multi-sensor Information Fusion System, and each navigation system has itself when working independently
The defects of can not avoiding, and for integrated navigation system, it is possible to achieve the mutual supplement with each other's advantages between different navigation system, contribute to
Dynamic locating accuracy is lifted, ensures system stable operation, improves System Error-tolerance Property.The integrated navigation system of world today's main flow
Have：GPS/INS, INS/ celestial navigation, GPS/ rowlands etc., and China promotes mainly triones navigation system at present, so the Big Dipper/INS
The integrated navigation system of+other sensors possesses huge developing space.
At present, navigation computer system largely employs DSP technologies, is to use microcontroller than more typical mode
(MCU) main frame data acquisition and control function are used as, the reasonable distribution side of the information processing function is completed using dsp chip
Formula.And the development with FPGA in terms of data processing, increasing people start to realize hardware design using FPGA, make
Navigation system is obtained to continue to develop to microminaturization, high performance and cost degradation direction.Complete to lead just with single-chip simultaneously
The design of boat system, the design cycle is not only shortened, and reduce the development difficulty of designer.But using FPGA platform as
The integrated navigation system of navigational computer usually requires to write corresponding code when receiving the decode sensing data
CPU processing is then transferred to, so as to result in the reduction of FPGA overall execution efficiency.
Multi-sensor combined navigation system carries out Data Fusion using distributed Kalman filter more, but not right
The use environment of sensor is paid close attention to, and institute's gathered data may be caused to have influence on biography due to the severe of extraneous use environment
The measurement accuracy of sensor, so as to influence filtering accuracy.Thus, it is necessary to selected according to the change of extraneous use environment using conjunction
Suitable sensing data, the final filtering accuracy for causing wave filter are guaranteed.
The content of the invention
The technical problems to be solved by the invention be in order to solve current FPGA as navigational computer processing receive and
The problem of FPGA overall execution efficiency can be caused to reduce during code sensor data, and eliminate due to not considering sensor
Use environment and the potential impact brought to navigation accuracy, the invention provides a kind of multisensor based on FPGA and RTOS
Merge guider.
The present invention uses following technical scheme to solve above-mentioned technical problem
A kind of Multi-sensor Fusion guider based on FPGA and RTOS, is deposited comprising data reception module, the first data
Store up module, condition selecting filter module, the second data memory module；
Wherein, data reception module includes Beidou receiver, six axle IMU Inertial Measurement Units, three axle magnetometer, barometer
With laser range finder and respectively with Beidou receiver, six axle IMU Inertial Measurement Units, three axle magnetometer, barometer and laser
Big Dipper IP kernel module that rangefinder connects one to one, IMU IP kernels module, three axle magnetometer IP kernel module, barometer IP kernel
Module, laser range finder IP kernel module；
Wherein, data reception module, for Beidou receiver, six axle IMU Inertial Measurement Units, three axle magnetometer, air pressure
Meter receives the decode with laser range finder data；
First data memory module, for storing by Big Dipper IP kernel module, IMU IP kernels module, three axle magnetometer IP kernel mould
Data after the completion of block, barometer IP kernel module, the decoding of laser range finder IP kernel module；
Condition selecting filter module, for highly determining corresponding sensor using strategy according to real-time, and to the Big Dipper
Data after the completion of receiver, six axle IMU Inertial Measurement Units, three axle magnetometer, barometer and laser range finder decoding are carried out
Local optimum is estimated, and then obtains Beidou receiver, six axle IMU Inertial Measurement Units, three axle magnetometer, barometer and laser
The optimal estimation of rangefinder state vector；
Second data memory module, for the optimal estimation of storage state vector, and then pass through the navigation information to carrier
It is corrected, obtains optimal navigation information.
As a kind of further preferred scheme of the Multi-sensor Fusion guider based on FPGA and RTOS of the present invention, north
Struggle against IP kernel module, IMU IP kernels module, three axle magnetometer IP kernel module, barometer IP kernel module, laser range finder IP kernel module
By being write with VHDL language.
As a kind of further preferred scheme of the Multi-sensor Fusion guider based on FPGA and RTOS of the present invention, institute
State condition selecting filter module and include distributed Kalman filter, logic judgment module；The logic judgment module is used for
Setting sensor switching strategy；The distributed Kalman filter is used to combine logic judgment module output result to each biography
The decoding data of sensor is handled.
As a kind of further preferred scheme of the Multi-sensor Fusion guider based on FPGA and RTOS of the present invention, institute
State the first data memory module and the second data memory module uses both-end RAM.
As a kind of further preferred scheme of the Multi-sensor Fusion guider based on FPGA and RTOS of the present invention, institute
The state vector for stating Beidou receiver, six axle IMU Inertial Measurement Units, three axle magnetometer, barometer and laser range finder is specific
It is as follows：
The state vector of (1) six axle IMU Inertial Measurement Units is：
Wherein, δ L, δ λ, δ h are respectively latitude error, longitude error and the height error of the carrier of IMU measurements；δvE、δvN、
δvURespectively east orientation, north orientation and vertical velocity error；Respectively east orientation, north orientation and vertical attitude angle are missed
Difference；εx、εy、εzThe respectively gyroscopic drift error of x-axis, y-axis, z-axis；Respectively x-axis, y-axis, z-axis plus
The speedometer error of zero；
(2) state vector of Beidou receiver is：
Wherein, δ LBD、δλBD、δhBDRespectively latitude error, longitude error and the height of the carrier of Beidou receiver measurement
Error；Respectively east orientation, north orientation and vertical velocity error；δtuIt is inclined for the clock of Beidou receiver
Difference, δ trFor the clock Random Drift Error amount of Beidou receiver；
(3) state vector of three axle magnetometer is：
Wherein, dyMGE、dyMGN、dyMGUFor magnetic east orientation, north orientation and the vertical error of magnetometer；
(4) barometrical state vector is：
Wherein, dhBFor barometrical elevation carrection error；
(5) state vector of laser range finder is：
Wherein, dhLRFFor the elevation carrection error of laser range finder.
The present invention compared with prior art, has following technique effect using above technical scheme：
1st, the present invention is direct to sensing data on data receiver and the IP kernel of decoding by being write with VHDL language
Handled, can not only greatly improve data processing speed, reduce CPU burden, and improve the entirety of guider
Execution efficiency；
2nd, for the present invention by design logic judge module, improve sensor uses strategy, and essence of navigating is improved to be final
Degree is laid a good foundation；
3rd, condition selecting wave filter of the invention is by the combination of logic judgment module and distributed Kalman filter, no
The amount of calculation of senior filter can be only reduced, improves filtering speed, is advantageous to the real-time execution of algorithm, and by selecting son
Wave filter, can be by the data input senior filter of the higher subfilter of precision, the final precision for lifting guider.
Brief description of the drawings
Fig. 1 is the multi-sensor combined navigation schematic diagram of device based on FPGA platform of the present invention；
Fig. 2 is guider RTOS mission flow diagram.
Fig. 3 is the logic judgment module flow chart of the present invention；
Fig. 4 is the schematic diagram of the condition selecting wave filter of the present invention；
Embodiment
Technical scheme is described in further detail below in conjunction with the accompanying drawings：
As shown in figure 1, the present invention is a kind of multi-sensor combined navigation device based on FPGA platform, mainly connect by data
Receive module, data memory module and condition selecting filter module composition.Wherein data reception module include Beidou receiver,
Six axle IMU Inertial Measurement Units, three axle magnetometer, the sensor assembly of barometer and laser range finder composition and corresponding hard
Part IP core modules；Data memory module mainly stores the decoding data of each sensor and the navigation of amendment of filtered device output
Information；Condition selecting wave filter is then made up of logic judgment module and Federated Filters.
The present invention use FPGA as navigational computer, using FPGA programmable characteristic to the data receiver in system with
The IP kernel progress of decoder module is independent to write, so as to improve the operation efficiency of whole integrated navigation system and utilization rate.
Guider RTOS flow of task is as shown in Fig. 2, wherein it is desired to three tasks performed parallel are respectively data
Acquisition tasks, logic judgment task and filter task.In data acquisition session, motion pick number that each sensor passes through carrier
According to FPGA navigational computers complete receiving the decode for data by IP kernel, and then decoding data is stored in shared RAM；Patrol
To collect in judgement task, system carries out logic judgment by using logic judgment strategy shown in Fig. 3, i.e., when height is more than 10 meters,
If there is Big Dipper signal, merged with Beidou receiver altitude information using barometer, if without Big Dipper signal, be used alone
Barometer height data are merged；And then melted when height is less than or equal to 10 meters using laser range finder altitude information
Close；In filter task, the decoding data that each subfilter is read in shared RAM in distributed Kalman filter is resolved,
The local optimum estimation of state is obtained, the output of each subfilter is then input to senior filter parallel, finally gives main filter
The optimal estimation of ripple device state vector.
Distributed Kalman filter selection IMU Inertial Measurement Units are common reference system, are distinguished with its sub-systems
Combination, 4 Kalman's subfilters are formed, then distributed Kalman filter structure is as shown in Figure 4.Wherein subfilter i (i
=1,2,3,4) optimal estimation of IMU and corresponding subsystem state is provided.Due to only being utilized when subfilter i does measurement renewal
Corresponding measurement Zi(i=1,2,3,4), so the estimation on IMU Inertial Measurement Unit states obtained is office
Portion is optimal.The effect of senior filter be by each subfilter provide on IMU Inertial Measurement Units state optimization estimation by
Blending algorithm synthesizes the global best estimates on IMU states, i.e., according to all measurement Z1, Z2, Z3, Z4Determine on IMU
The optimal estimation of state.
Wherein, each sensor states vector is respectively：
The state vector of (1) six axle IMU Inertial Measurement Units is：
Wherein, δ L, δ λ, δ h are respectively latitude error, longitude error and the height error of the carrier of IMU measurements；δvE、δvN、
δvURespectively east orientation, north orientation and vertical velocity error；Respectively east orientation, north orientation and vertical attitude angle are missed
Difference；εx、εy、εzThe respectively gyroscopic drift error of x-axis, y-axis, z-axis；Respectively x-axis, y-axis, z-axis plus
The speedometer error of zero.
(2) state vector of Beidou receiver is：
Wherein, δ LBD、δλBD、δhBDRespectively latitude error, longitude error and the height of the carrier of Beidou receiver measurement
Error；Respectively east orientation, north orientation and vertical velocity error；δtuIt is inclined for the clock of Beidou receiver
Difference, δ trFor the clock Random Drift Error amount of Beidou receiver.
(3) state vector of magnetometer is：
Wherein, dyMGE、dyMGN、dyMGUFor magnetic east orientation, north orientation and the vertical error of magnetometer.
(4) barometrical state vector is：
Wherein, dhBFor barometrical elevation carrection error.
(5) state vector of laser range finder is：
Wherein, dhLRFFor the elevation carrection error of laser range finder.
The state equation and measurement equation of each subfilter is established below：
(1) subfilter 1
Wherein, FIMU、FBDRespectively IMU, Beidou receiver systematic observation matrix；XIMU、XBDRespectively IMU, the Big Dipper
The state vector of receiver；GIMUFor IMU system dynamic noise matrix；wIMU、wBDRespectively IMU, Beidou receiver system
Process white noise vector；HIMU、HBDRespectively IMU, Beidou receiver system measurements matrix；VBDWhat it is for Beidou receiver is
Noise of uniting measures vector.
(2) subfilter 2
Wherein, FMGFor the systematic observation matrix of three axle magnetometer；GMGFor the system dynamic noise matrix of three axle magnetometer；
wMGFor the systematic procedure white noise vector of three axle magnetometer；HMGFor the system measurements matrix of three axle magnetometer；VMGFor three axle magnetic force
The system noise of meter measures vector.
(3) subfilter 3
Wherein, FBFor barometrical systematic observation matrix；GBFor barometrical system dynamic noise matrix；wBFor barometer
Systematic procedure white noise vector；HBFor barometrical system measurements matrix；VBVector is measured for barometrical system noise.
(4) subfilter 4
Wherein, FLRFFor the systematic observation matrix of laser range finder；GLRFFor the system dynamic noise matrix of laser range finder；
wLRFFor the systematic procedure white noise vector of laser range finder；HLRFFor the system measurements matrix of laser range finder；VLRFFor Laser Measuring
The system noise of distance meter measures vector.
Subfilter i filtering equations are
Wherein,For the filter value of subfilter；For subfilter i one-step prediction value, PikFor subfilter
I estimation error variance battle array.
Local state estimation and the estimation error variance battle array of each subfilter are can obtain by the filtering of subfilter.Will be each
The filter result of subfilter is input to senior filter and carries out fusion resolving parallel, so as to obtain senior filter state vector
Optimal estimation, it is corrected eventually through the navigation information to carrier, you can obtain optimal navigation information.
Claims (5)
- A kind of 1. Multi-sensor Fusion guider based on FPGA and RTOS, it is characterised in that：Include data reception module, One data memory module, condition selecting filter module, the second data memory module；Wherein, data reception module includes Beidou receiver, six axle IMU Inertial Measurement Units, three axle magnetometer, barometer and swashed Optar and respectively with Beidou receiver, six axle IMU Inertial Measurement Units, three axle magnetometer, barometer and laser ranging Big Dipper IP kernel module that instrument connects one to one, IMU IP kernels module, three axle magnetometer IP kernel module, barometer IP kernel module, Laser range finder IP kernel module；Wherein, data reception module, for Beidou receiver, six axle IMU Inertial Measurement Units, three axle magnetometer, barometer and Laser range finder data receive the decode；First data memory module, for store by Big Dipper IP kernel module, IMU IP kernels module, three axle magnetometer IP kernel module, Data after the completion of barometer IP kernel module, the decoding of laser range finder IP kernel module；Condition selecting filter module, for highly determining corresponding sensor using strategy according to real-time, and the Big Dipper is received Data after the completion of machine, six axle IMU Inertial Measurement Units, three axle magnetometer, barometer and laser range finder decoding carry out local Optimal estimation, and then obtain Beidou receiver, six axle IMU Inertial Measurement Units, three axle magnetometer, barometer and laser range finder The optimal estimation of state vector；Second data memory module, for the optimal estimation of storage state vector, and then by being carried out to the navigation information of carrier Correction, obtains optimal navigation information.
- A kind of 2. Multi-sensor Fusion guider based on FPGA and RTOS according to claim 1, it is characterised in that Big Dipper IP kernel module, IMU IP kernels module, three axle magnetometer IP kernel module, barometer IP kernel module, laser range finder IP kernel mould Block with VHDL language by writing.
- A kind of 3. Multi-sensor Fusion guider based on FPGA and RTOS according to claim 1, it is characterised in that： The condition selecting filter module includes distributed Kalman filter, logic judgment module；The logic judgment module is used In setting sensor switching strategy；The distributed Kalman filter is used to combine logic judgment module output result to each biography The decoding data of sensor is handled.
- A kind of 4. Multi-sensor Fusion guider based on FPGA and RTOS according to claim 1, it is characterised in that： First data memory module and the second data memory module use both-end RAM.
- A kind of 5. Multi-sensor Fusion guider based on FPGA and RTOS according to claim 2, it is characterised in that： The Beidou receiver, six axle IMU Inertial Measurement Units, three axle magnetometer, the state vector of barometer and laser range finder tool Body is as follows：The state vector of (1) six axle IMU Inertial Measurement Units is：Wherein, δ L, δ λ, δ h are respectively latitude error, longitude error and the height error of the carrier of IMU measurements；δvE、δvN、δvU Respectively east orientation, north orientation and vertical velocity error；Respectively east orientation, north orientation and vertical attitude error； εx、εy、εzThe respectively gyroscopic drift error of x-axis, y-axis, z-axis；The respectively acceleration of x-axis, y-axis, z-axis Count the error of zero；(2) state vector of Beidou receiver is：<mrow> <msub> <mi>X</mi> <mrow> <mi>B</mi> <mi>D</mi> </mrow> </msub> <mo>=</mo> <msup> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mrow> <msup> <mi>&delta;L</mi> <mrow> <mi>B</mi> <mi>D</mi> </mrow> </msup> </mrow> </mtd> <mtd> <mrow> <msup> <mi>&delta;&lambda;</mi> <mrow> <mi>B</mi> <mi>D</mi> </mrow> </msup> </mrow> </mtd> <mtd> <mrow> <msup> <mi>&delta;h</mi> <mrow> <mi>B</mi> <mi>D</mi> </mrow> </msup> </mrow> </mtd> <mtd> <mrow> <msubsup> <mi>&delta;v</mi> <mi>E</mi> <mrow> <mi>B</mi> <mi>D</mi> </mrow> </msubsup> </mrow> </mtd> <mtd> <mrow> <msubsup> <mi>&delta;v</mi> <mi>N</mi> <mrow> <mi>B</mi> <mi>D</mi> </mrow> </msubsup> </mrow> </mtd> <mtd> <mrow> <msubsup> <mi>&delta;v</mi> <mi>U</mi> <mrow> <mi>B</mi> <mi>D</mi> </mrow> </msubsup> </mrow> </mtd> <mtd> <mrow> <msub> <mi>&delta;t</mi> <mi>u</mi> </msub> </mrow> </mtd> <mtd> <mrow> <msub> <mi>&delta;t</mi> <mi>r</mi> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> <mi>T</mi> </msup> </mrow>Wherein, δ LBD、δλBD、δhBDRespectively latitude error, longitude error and the height error of the carrier of Beidou receiver measurement；Respectively east orientation, north orientation and vertical velocity error；δtuFor the clock jitter of Beidou receiver, δ tr For the clock Random Drift Error amount of Beidou receiver；(3) state vector of three axle magnetometer is：XMG=[dyMGE dyMGN dyMGU]Wherein, dyMGE、dyMGN、dyMGUFor magnetic east orientation, north orientation and the vertical error of magnetometer；(4) barometrical state vector is：XB=[dhB]Wherein, dhBFor barometrical elevation carrection error；(5) state vector of laser range finder is：XLRF=[dhLRF]Wherein, dhLRFFor the elevation carrection error of laser range finder.
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