CN102410837B - Combined locating navigation system and method for vehicles - Google Patents
Combined locating navigation system and method for vehicles Download PDFInfo
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- CN102410837B CN102410837B CN201110215437.3A CN201110215437A CN102410837B CN 102410837 B CN102410837 B CN 102410837B CN 201110215437 A CN201110215437 A CN 201110215437A CN 102410837 B CN102410837 B CN 102410837B
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Abstract
The invention provides a combined navigation method for locating vehicles. A dual-sensor combined locating navigation filter system taking a navigation computer as the core is formed by reconfiguring an angular speed gyroscope, a low-pass filter, an A/D converter, a millimeter, a GPS, a microprocessor, a level switch and a digital processor. The dual-sensor data is filtered and optimized by the information fusion technology. The shortcoming that the GPS locating method cannot normally locate vehicles caused by blocked signals is overcome by the function provided by DR system. The error of the DR system is corrected and compensated by the vehicle position and speed information received by GPS. Therefore, the problem that the DR locating technology cannot be used singly after accumulating errors for a long time is solved. Meanwhile, data share is realized by the embedded CAN control transceiver.
Description
Technical field
The invention discloses a kind of vehicle combination positioning navigation method, belong to vehicle positioning and navigation technical field.
Background technology
On vehicle, widely used GPS GPS has the features such as locating speed is fast, precision is high, the continuous covering in ground at present, but in built-up city, blocks with multipath effect problem and make the positioning precision of vehicle decline and have a lot of blind areas.Dead reckoning system (DR) is utilized the direction of Vehicle Driving Cycle and the instantaneous position that range information is calculated vehicle, do not rely on outer signals, there is autonomous location navigation performance, and the inherent defect of dead reckoning system be positioning error along with time integral, can not be independent carry out for a long time dead reckoning and location.Therefore GPS and two kinds of systems of DR are reconstructed to filtering and information fusion, can make both learn from other's strong points to offset one's weaknesses, reach good locating effect.On the other hand, the steering of vehicle, brake system etc. are the information such as the definite vehicle location of shared group assembly system, speed of a motor vehicle how, is vital problem undoubtedly at aspects such as automotive vehicle, intelligent transportation.The present invention relates to a kind of vehicle combination positioning navigation method of the CAN of embedding bus functionality, in order to address the above problem.
Summary of the invention
Technical matters to be solved by this invention is to use reconfiguration technique that vehicle combination Position Fixing Navigation System is reconstituted to two sensors system.Utilize Kalman Filter Technology to carry out filtering processing to this two sensors signal, gained status information is carried out information fusion with global filtering device, obtains global optimum's estimated value, reaches and improves the positioning precision of vehicle combination Position Fixing Navigation System and the object of integrality.
Combined system of the present invention, utilizes reconfiguration technique, and system is reconstructed, specific as follows:
One, GPS (5) is reconstituted to digital sensor A(22);
Two, rate-of-turn gyroscope (1), low-pass filter (2), A/D converter (3) and mileometer (4) reconstitute digital sensor B(21);
Three, microprocessor (6), level conversion (7), digital processing unit (8) reconstitute navigational computer (28);
Four,, in navigational computer (28), dead reckoning device (23), wave filter A(24 are set), wave filter 2(25) and global filtering device (26);
Five, further by digital sensor B(21) and dead reckoning device (23) reconstitute intelligent dead reckoning sensor (27).
Carry out after above-mentioned reconstruct, thereby reconstituted dual sensor filtering combination Position Fixing Navigation System.
In dual sensor filtering combination Position Fixing Navigation System, use wave filter A(24), wave filter 2(25) and global filtering device (26), to the gps signal of digital sensors A (22) and digital sensor B(21) DR signal carry out filtering and merge optimization process, concrete grammar is as follows:
The first step, choosing
,
,
be respectively vehicle in the state variable of location components, speed component and the component of acceleration of east orientation,
,
,
be respectively vehicle in the state variable of location components, speed component and the component of acceleration of north orientation, choosing
,
for the error of vehicle at east orientation and north orientation, the error source during by GPS and DR system location is equivalent to first-order Markov process, gets
state vector for integrated positioning system.
Second step, the continuous state equation of getting integrated positioning system is
=
In formula,
，
，
In formula:
,
be respectively
,
white Gaussian noise;
,
be respectively constant correlation time of vehicle east orientation and north orientation Maneuver Acceleration rate of change;
,
constant correlation time of the corresponding markov of difference;
,
be respectively the current average of vehicle east orientation and north orientation Maneuver Acceleration component.
The 3rd step, getting T is the sampling period, the continuous motion model of vehicle is maneuvering target, and " discretization model of present statistical model is
（1）
In formula,
（2）
Get
,
,
,
,
be respectively
，
，
（3）
In formula
，
，
，
，
=
=0。
The 4th step, the state variable of peek word sensors A (22), wave filter A (24) subsystem
, state equation is identical with overall status equation, selects the east orientation positional information of subsystem
positional information with north orientation
(unit all turns to m) is observed quantity,
, discretize observation equation is:
（4）
In formula,
（5）
with
be GPS receiver location observation noise, be approximately
,
white Gaussian noise, measurement noise covariance matrix is:
。
The 5th step, the state variable of getting intelligent dead reckoning sensor (27), wave filter 2 (25) subsystems
, state equation is identical with overall status equation, the output of the rate gyro of selecting the role
and the distance exported within a sampling period of odometer
as observed quantity, the calibration factor of odometer is taken as K=1.Get
, the observation equation obtaining is continuously:
drift for gyro, is approximately
white Gaussian noise;
observation noise for odometer, is approximately
white Gaussian noise.Observation noise covariance matrix is:
By observation equation discretize, the discrete observation equation of the system that obtains is
In formula
，
Above-mentioned nonlinear observation equation is adopted to expansion Kalman filtering linearization, right
in predicted value
place's Taylor series expansion, and ignore second order and above item,
Abbreviation obtains the discrete observation equation of DR system linear
（6）
Wherein,
（7）
。
The 6th step, by formula (2) and formula (3) above, obtains system state transition matrix
, system control vector matrix
for:
By formula (5) above, (7) the observing matrix of system
with
for:
According to the statistical property of system noise,
.
According to the statistical property of system noise, obtain
as follows:
，
，
Wherein:
In formula:
with
all symmetric matrix,
in element expression and
in element expression similar, will
in each element expression in
with
replace, can correspondingly obtain
in element expression.
The 7th step, according to the state equation (1) of set up wave filter A and observation equation (4) the discrete model of GPS wave filter A is:
According to the state equation of set up wave filter 2 (1) and observation equation (6), the discrete model that obtains DR wave filter 2 is:
According to expansion Kalman filtering recurrence equation, the filtering equations of the discrete model of GPS wave filter A and the discrete model of DR wave filter 2 is as follows:
1, system state one-step prediction estimate equation:
2, system state estimation value equation:
3, filter gain equation:
4, one-step prediction estimation error variance equation:
5, filtering error variance equation:
In formula,
;
Wherein,
=
The state estimation value of output filter A
, estimation error variance
, noise mean square value
state estimation value with wave filter 2
, estimation error variance
, noise mean square value
.
The 7th step, merges the estimated information of wave filter A and wave filter 2 by following method, obtains global optimum's estimated value, overall estimation error variance
, global noise mean square value
with globalstate estimation value:
。
The 8th step, global filtering device feeds back to wave filter A and wave filter 2 by information
At GPS, can normally work, positioning precision is when higher, get
=
=0.5; When blocking etc. that reason GPS positioning system can not normally be located or positioning precision is poor, get
=0,
=1.
The position of supposing surface car initial time east orientation and north orientation is zero; Vehicle with
speed, along 45 degree course angle linear uniform motion, altogether running time is 500s; The sampling period of system is 1s, and relevant parameters is got:
，
，
，
，
，
，
，
，
。
Beneficial effect of the present invention is, use reconfiguration technique, gyroscope, wave filter, A/D converter, mileometer, GPS, microprocessor, level shifting circuit and digital processing unit etc. are reconstructed, GPS and two kinds of systems of DR are reconstructed to filtering and information fusion, both are learnt from other's strong points to offset one's weaknesses, improved positioning precision and the integrality of system, made the vehicle can round-the-clock, unobstructed, high-precision location.Utilize microprocessor to gather GPS, gyroscope and mileometer data, with digital processing unit, carry out digital filtering and information fusion optimal processing, system real time is strong.By display, show in real time the information such as GPS, gyroscope and mileometer and control transceiver by the CAN embedding they are sent to the data bus of vehicle, realize data sharing.
Accompanying drawing explanation
Fig. 1 is vehicle GPS/DR integrated navigation and location system composition diagram of the present invention.
Fig. 2 is the combined system figure of the dual sensor navigator fix filtering that reconstitutes of the present invention.
Front system east orientation Error Graph is merged in Fig. 3 filtering.
Front system north orientation Error Graph is merged in Fig. 4 filtering.
The Error Graph of rear system east orientation is merged in Fig. 5 filtering.
The Error Graph of rear system north orientation is merged in Fig. 6 filtering.
Embodiment
embodiment 1:
According to Fig. 1, native system by angular rate gyroscope (1) (ADRS300), GPS (5) (GM-210), microprocessor (6) (P89C669), digital processing unit (8) (TMS320vc5410), low-pass filter (2), A/D converter (3) (TLC1549), mileometer (4), level conversion (7) (74LVC1654A), CAN controller (9) (SJA1000), transmitter-receiver (10) (TJA1050) and display (11) etc. form.GPS (5) (GM-210) is reconstituted to digital sensor A(22 after being connected with microprocessor (6) P3.0, P3.1 (P89C669) by MAX232).Rate-of-turn gyroscope (1) output (ADRS300) is connected with the input of low-pass filter (2), the output of low-pass filter (2) is connected with A/D converter (3) input (TLC1549), A/D converter (3) output (TLC1549) is connected with microprocessor (6) P1.1 (P89C669), microprocessor (6) P1.2, P1.0 (P89C669) is connected with A/D converter (3) CS, CLK (TLC1549) respectively, under the control of P1.2, P1.0, A/D converter (3) (TLC1549) is input to P89C669 by the data after conversion with serial mode.The output of mileometer (4) is connected with microprocessor (6) P3.4 (P89C669).By rate-of-turn gyroscope (1) (ADRS300), low-pass filter (2), A/D converter (3) (TLC1549) and mileometer (4) reconstitute digital sensor B(21).Microprocessor (6) (P89C669) (74LVC1654A) (TMS320vc5410) is connected with digital processing unit (8) by level conversion (7), and by microprocessor (6) (P89C669), level conversion (7) (74LVC1654A) and digital processing unit (8) (TMS320vc5410) reconstitute navigational computer (28).In navigational computer (28), design dead reckoning device (23), wave filter A(24), wave filter 2(25) and global filtering device (26); Further by digital sensor B(21) and dead reckoning device (23) reconstitute intelligent dead reckoning sensor (27), thereby reconstitute the combined system of the dual sensor navigator fix filtering shown in Fig. 2.
embodiment 2:
Shown in Fig. 2, the inventive method is by digital sensor A(22) wave filter A for signal (24) carries out filtering processing, gained state estimation value
and estimation error variance
as an input signal of global filtering device, by wave filter 2(25) to gained state estimation value after the filtering of intelligent dead reckoning sensor signal
and estimation error variance
as another input signal of global filtering device, global filtering device fused filtering device A (24) and wave filter 2(25) estimated information, when obtaining global optimum's estimated value, information
,
feed back to respectively wave filter A (24) and wave filter 2(25).Improved positioning precision and the integrality of system, made the vehicle can round-the-clock, unobstructed, high-precision location.
embodiment 3:
According to Fig. 1, microprocessor (6) data bus (P89C669) is connected with CAN controller (9) data bus (SJA1000), and CAN controller (9) TX0, RX0 (SJA1000) is connected with transmitter-receiver (10) TXD, RXD (TJA1050) respectively.According to Fig. 1, Fig. 2, the locating navigation information of gained vehicle, by display (11), CAN controller (9) (SJA1000) and transmitter-receiver (10) (TJA1050) show and send to CAN bus, realize data sharing.
Fig. 3 and Fig. 4 are respectively that integrated navigation system adopts the site error curve before filtering is merged; Fig. 5 and Fig. 6 are respectively that system has adopted the site error curve after filtering is merged.East orientation error before GPS/DR system filter merges is 14.566m, and north orientation error is 15.594m; East orientation error after filtering is merged is 4.676m, and north orientation error is 5.385m.So the error of rear system is merged in filtering, before filtering is merged, there is good convergence, therefore this filtering blending algorithm has suppressed dispersing of filtering error effectively, improved the precision of location.
Claims (2)
1. a vehicle combination positioning navigation method, it is characterized in that, two digital sensor signals are carried out to filtering processing with wave filter, gained state estimation value and estimation error variance are as the input signal of global filtering device, global filtering device merges two wave filter estimated informations that digital sensor is corresponding, when obtaining global optimum's estimated value, relevant information is fed back to respectively to two wave filters, improve positioning precision and integrality, concrete steps are:
The first step, choosing
,
,
be respectively vehicle in the state variable of location components, speed component and the component of acceleration of east orientation,
,
,
be respectively vehicle in the state variable of location components, speed component and the component of acceleration of north orientation, choosing
,
for the error of vehicle at east orientation and north orientation, the error source during by GPS and DR system location is equivalent to first-order Markov process, gets
state vector for integrated positioning system;
Second step, the continuous state equation of getting integrated positioning system is
=
In formula,
，
，
In formula:
,
be respectively
,
white Gaussian noise;
,
be respectively constant correlation time of vehicle east orientation and north orientation Maneuver Acceleration rate of change;
,
constant correlation time of the corresponding markov of difference;
,
be respectively the current average of vehicle east orientation and north orientation Maneuver Acceleration component;
The 3rd step, getting T is the sampling period, the continuous motion model of vehicle is maneuvering target, and " discretization model of present statistical model is
（1）
In formula,
（2）
Get
,
,
,
,
be respectively
，
，
（3）
In formula
，
，
，
，
=
=0
The 4th step, the state variable of peek word sensors A (22), wave filter A (24) subsystem
, state equation is identical with overall status equation, selects the east orientation positional information of subsystem
positional information with north orientation
for observed quantity, wherein
,
unit is m,
,
observed quantity for east orientation positional information after subsystem discretize;
for the observed quantity of north orientation positional information after subsystem discretize, discretize observation equation is:
（4）
In formula,
（5）
with
be GPS receiver location observation noise in k value constantly, be approximately
,
white Gaussian noise, measurement noise covariance matrix is:
The 5th step, the state variable of getting intelligent dead reckoning sensor (27), wave filter B (25) subsystem
, state equation is identical with overall status equation, the output of the rate gyro of selecting the role
and the distance exported within a sampling period of odometer
as observed quantity, the calibration factor of odometer is taken as K=1;
Get
, the observation equation obtaining is continuously:
drift for gyro, is approximately
white Gaussian noise;
observation noise for odometer, is approximately
white Gaussian noise;
Observation noise covariance matrix is:
By observation equation discretize, the discrete observation equation of the system that obtains is
In formula
，
Above-mentioned nonlinear observation equation is adopted to expansion Kalman filtering linearization, right
in predicted value
place's Taylor series expansion, and ignore second order and above item,
Abbreviation obtains the discrete observation equation of DR system linear
（6）
Wherein,
（7）
The 6th step, by formula (2) and formula (3) above, obtains system state transition matrix
, system control vector matrix
for:
By formula (5) above, (7) the observing matrix of system
with
for:
According to the statistical property of system noise,
;
According to the statistical property of system noise, obtain
as follows:
，
，
Wherein:
In formula:
with
all symmetric matrix,
in element expression and
in element expression similar, will
in each element expression in
with
replace, can correspondingly obtain
in element expression;
The 7th step, according to the state equation (1) of set up wave filter A and observation equation (4) the discrete model of GPS wave filter A is:
According to the state equation (1) of set up wave filter B and observation equation (6), the discrete model that obtains DR wave filter B is:
According to expansion Kalman filtering recurrence equation, the filtering equations of the discrete model of the discrete model of GPS wave filter A and DR wave filter B is as follows:
(1), system state one-step prediction estimate equation:
(2), system state estimation value equation:
(3), filter gain equation:
(4), one-step prediction estimation error variance equation:
(5), filtering error variance equation:
In formula,
;
Wherein,
The state estimation value of output filter A
, estimation error variance
, noise mean square value
state estimation value with wave filter B
, estimation error variance
, noise mean square value
;
The 7th step, merges the estimated information of wave filter A and wave filter B by following method, obtains global optimum's estimated value, overall estimation error variance
, global noise mean square value
with globalstate estimation value:
The 8th step, global filtering device feeds back to wave filter A and wave filter B by information
At GPS, can normally work, positioning precision is when higher, get
=
=0.5; When GPS positioning system can not normally be located or when positioning precision is poor, get
=0,
=1.
2. a system of utilizing the vehicle combination positioning navigation method described in claim 1, it is characterized in that, comprise rate-of-turn gyroscope (1), GPS (5), microprocessor (6), digital processing unit (8), low-pass filter (2), A/D converter (3), mileometer (4), level conversion (7), CAN controller (9), transmitter-receiver (10) and display (11); Described GPS (5) reconstitutes digital sensor A(22 after being connected with microprocessor (6)); The output of rate-of-turn gyroscope (1) is connected with the input of low-pass filter (2), the output of low-pass filter (2) is connected with the input of A/D converter (3), the output of A/D converter (3) is connected with microprocessor (6), and A/D converter (3) is input to microprocessor (6) by the data after conversion with serial mode; The output of mileometer (4) is connected with microprocessor (6); Rate-of-turn gyroscope (1), low-pass filter (2), A/D converter (3) and mileometer (4) are reconstituted to digital sensor B(21); Microprocessor (6) is connected with digital processing unit (8) by level conversion (7), and microprocessor (6), level conversion (7) and digital processing unit (8) are reconstituted to navigational computer (28); In navigational computer (28), dead reckoning device (23), wave filter A(24 are set), wave filter B(25) and global filtering device (26); By digital sensor B(21) and dead reckoning device (23) reconstitute intelligent dead reckoning sensor (27).
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| US9250083B2 (en) * | 2013-03-22 | 2016-02-02 | Qualcomm Incorporated | Heading, velocity, and position estimation with vehicle sensors, mobile device, and GNSS inputs |
| CN104990563B (en) * | 2015-07-22 | 2017-12-26 | 广西大学 | VMT Vehicle-Miles of Travel computational methods |
| KR101795381B1 (en) * | 2015-11-20 | 2017-11-09 | 현대자동차 주식회사 | System and method of sharing for vehicle position information, and computer readable medium recording the method |
| CN105509764B (en) * | 2015-12-30 | 2019-03-12 | 北京星网宇达科技股份有限公司 | A kind of vehicle-mounted integrated terminal for intelligent Driving Test |
| JP6677533B2 (en) * | 2016-03-01 | 2020-04-08 | クラリオン株式会社 | In-vehicle device and estimation method |
| CN109115223A (en) * | 2018-08-30 | 2019-01-01 | 江苏大学 | A kind of full source integrated navigation system of full landform towards intelligent agricultural machinery |
| IT201900006735A1 (en) * | 2019-05-10 | 2020-11-10 | Sacertis S R L | Method of investigation of a structure and procedure to define an optimal method of investigation of the structure itself |
| CN110940344B (en) * | 2019-11-25 | 2020-06-26 | 奥特酷智能科技(南京)有限公司 | Low-cost sensor combination positioning method for automatic driving |
| CN113075713A (en) * | 2021-03-29 | 2021-07-06 | 北京理工大学重庆创新中心 | Vehicle relative pose measuring method, system, equipment and storage medium |
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