CN108345021A - A kind of Doppler radar assistant GPS/INS vehicle speed measuring methods - Google Patents

A kind of Doppler radar assistant GPS/INS vehicle speed measuring methods Download PDF

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Publication number
CN108345021A
CN108345021A CN201810052512.0A CN201810052512A CN108345021A CN 108345021 A CN108345021 A CN 108345021A CN 201810052512 A CN201810052512 A CN 201810052512A CN 108345021 A CN108345021 A CN 108345021A
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radar
error
imu
gps
vehicle speed
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CN108345021B (en
Inventor
陈熙源
宋锐
邵鑫
石春凤
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Southeast University
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Southeast University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/52—Determining velocity
    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/50—Systems of measurement based on relative movement of target
    • G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems

Abstract

The invention discloses a kind of Doppler radar assistant GPS/INS vehicle speed measuring methods, by defining different geographical position coordinates system and coordinate conversion matrix, and radar/IMU output datas are pre-processed using filtering methods such as wavelet transformations, simultaneously according to the operation principle and main error mechanism of different sensors, corresponding error model is established, to analyzing influence rate accuracy and the main path and method of raising safe driving of vehicle performance.Finally propose combined system training and prediction model based on neural network.The present invention fully considers the advantage and disadvantage of different measurement modes, by establishing different error models, corresponding velocity estimation model is independently selected according to state change during vehicle actual travel, to realize the raising of rate accuracy, while also making system that there is better robustness.

Description

A kind of Doppler radar assistant GPS/INS vehicle speed measuring methods
Technical field
The present invention relates to measurement in a closed series technical fields, and in particular to a kind of Doppler radar assistant GPS/INS vehicle speed measurings Method.
Background technology
For the anti-lock braking system (ABS) involved in safe driving technology in urban road, electronic stabilizing control system (ESC) etc. and the complicated landforms such as mountainous region, marsh when driving vehicle there may be the situations such as break away be both needed to the speed of vehicle into Row is accurate to measure problem, and easy compared to traditional measurement methods such as ultrasonic wave or laser sensors is influenced by conditions such as weather, Doppler radar is superior as a kind of dynamic property and the method that can carry out non-contact measurement has been widely used.
In vehicle-mounted measurement in a closed series high-precision positioning and navigation work(are provided frequently with the mode that GPS/INS is combined with navigation Can, but the inertial device error in INS can gradually accumulate needs at any time, and GPS signal is easy to be blocked in urban environment And interference, and the problems such as dynamic response capability is poor, it is therefore desirable to extraneous auxiliary information is considered as to original measurement in a closed series system System error is modified.For in Practical Project frequently with odometer speed-measuring method, due to its calibration factor change greatly need The problems such as wanting real time calibration so that rate accuracy is limited.Therefore using Doppler radar assistant GPS/INS in urban road and Accurately the problem of testing the speed still needs to be studied under complicated landform environment.
Invention content
Goal of the invention：In order to overcome the deficiencies in the prior art, provide it is a kind of provided for onboard combined navigation it is high-precision The velocity measurement information of degree is used as neural network (NN) model by building Doppler radar and INS signals, and utilizes GPS speed Degree estimated value is trained it, and when GPS signal is weak or blocks, the model trained can be with Accurate Prediction vehicle speed Spend Doppler radar assistant GPS/INS vehicle speed measuring methods of information.
Technical solution：To achieve the above object, the present invention provides a kind of Doppler radar assistant GPS/vehicle speed measuring sides INS Method includes the following steps：
1) coordinate system definition and conversion
Define bodywork reference frame m, origin is located at car body center O, reference axis be respectively directed to the car body right side-it is preceding-on；
The navigational coordinate system n of inertial reference calculation is defined, heart O, reference axis press the east-north-in location to origin respectively in the carrier Its direction；
Define IMU coordinate system b, origin is located at car body center O, reference axis respectively refer to the right-it is preceding-on；
Define Doppler radar coordinate system d, origin sensitivity center O on its interiord, reference axis be respectively directed to car body it is right- Before-on；
There is installation error between bodywork reference frame m when due to IMU installations on the car body, it is assumed that two coordinate systems, three sides To angle be respectively α, beta, gamma then can define coordinate conversion matrix：
2) radar and IMU data predictions
Since the influence of complex environment factor can make with the presence of abnormal data in signal, using filter when Doppler radar tests the speed Wave method pre-processes data.
3) foundation of radar and IMU data noise models
According to the principle of Doppler range rate measurement, speed vdWith Doppler frequency fdRelationship can indicate
λ is wavelength, and θ is radar antenna direction and direction of motion angle (rad), sets the floor projection and x of antenna beamd Axle clamp angle is ρ, and antenna beam angle with horizontal plane is σ, according to angular position relative
Cos θ=cos σ cos ρ (3)
vd=Vcos θ (4)
In view of the scale factor error K of Doppler radar measurement, the error of zeroOutput noise wdAnd lever arm misses Difference, reality output areAnd combine above formula, i.e.,
Wherein LdThe 3 dimension installation lever arms between car body central point and Doppler radar central point.
According to formula (5), car speed can be expressed as by radar measurement value under bodywork reference frame
Wherein K1=1/Kcos θ, radar measurement error model are represented by
IMU measurements obtain relationship between bearer rate and the speed of inertial reference calculation can be by coordinate conversion matrixIt indicates
IMU measurement errors are represented by under navigational coordinate system
Wherein φn×=| φU 0-φE|, φE、φN、φUFor attitude error angle, δ KAIt is respectively accelerometer with δ A Scale coefficient error E and fix error angle, δ KGWith the scale coefficient error and fix error angle that δ G are gyro.
4) the combined system training based on neural network and prediction model utilize work as radar, IMU and GPS effective Radar/IMU output models are trained for the GPS information of reference data, trained Model forecast system is utilized when GPS losing locks Rate predictions.
Further, data are pre-processed using the filtering method of wavelet transformation in the step 2, mainly according to reality Border experimental data selects the major parameter of wavelet transformation so that vehicle speed estimation precision higher.
Further, complex environment factor includes urban environment or landform in the step 2.
Further, radar/IMU output models are trained to be using as the GPS information with reference to benchmark in the step 4 The vehicle speed information resolved when being worked normally by GPS, as the reference value to radar/INS systems, by model The amendment of parameter improves its precision of prediction.
Advantageous effect：Compared with prior art, the present invention the advantage and disadvantage of different measurement modes are fully considered, by establishing not Same error model independently selects corresponding velocity estimation model, to realize according to state change during vehicle actual travel The raising of rate accuracy, while also making system that there is better robustness.
Description of the drawings
Fig. 1 is the definition of carrier coordinate system and the correspondence figure of relevant parameter；
Fig. 2 is the fusion method of main measurement system observation；
Fig. 3 is IMU, GPS, Radar velocity measurement and the velocity estimation results contrast figure of NN methods.
Specific implementation mode
In conjunction with attached drawing 1 and attached drawing 2, the present invention provides a kind of Doppler radar assistant GPS/INS vehicle speed measuring methods, including Following steps：
1) coordinate system definition and conversion
Define bodywork reference frame m, origin is located at car body center O, reference axis be respectively directed to the car body right side-it is preceding-on；
The navigational coordinate system n of inertial reference calculation is defined, heart O, reference axis press the east-north-in location to origin respectively in the carrier Its direction；
Define IMU coordinate system b, origin is located at car body center O, reference axis respectively refer to the right-it is preceding-on；
Define Doppler radar coordinate system d, origin sensitivity center O on its interiord, reference axis be respectively directed to car body it is right- Before-on；
There is installation error between bodywork reference frame m when due to IMU installations on the car body, it is assumed that two coordinate systems, three sides To angle be respectively α, beta, gamma then can define coordinate conversion matrix：
2) radar and IMU data predictions
Since the influence of complex environment factor can so that, with the presence of abnormal data in signal, use is small when Doppler radar tests the speed The filtering methods such as wave conversion pre-process data, wherein the parameter of wavelet transformation according to experimental data can select db2 for Wavelet basis function, Decomposition order are 4 layers.
3) foundation of radar and IMU data noise models
According to the principle of Doppler range rate measurement, speed vdWith Doppler frequency fdRelationship can indicate
λ is wavelength, and θ is radar antenna direction and direction of motion angle (rad), as shown in Figure 1, the horizontal of antenna beam is thrown Shadow and xdAxle clamp angle is ρ, and antenna beam angle with horizontal plane is σ, according to angular position relative
Cos θ=cos σ cos ρ (3)
vd=Vcos θ (4)
In view of the scale factor error K of Doppler radar measurement, the error of zeroOutput noise wdAnd lever arm error Ld Deng reality output isAnd combine above formula, i.e.,
Wherein LdThe 3 dimension installation lever arms between car body central point and Doppler radar central point；
According to formula (5), car speed can be expressed as by radar measurement value under bodywork reference frame
Wherein K1=1/Kcos θ, radar measurement error model are represented by
IMU measurements obtain relationship between bearer rate and the speed of inertial reference calculation can be by coordinate conversion matrixIt indicates
IMU measurement errors are represented by under navigational coordinate system
Wherein φn×=| φU 0-φE|, φE、φN、φUFor attitude error angle, δ KAIt is respectively accelerometer with δ A Scale coefficient error and fix error angle, δ KGWith the scale coefficient error and fix error angle that δ G are gyro.
4) as shown in Fig. 2, based on neural network combined system training and prediction model, when radar, IMU and GPS have When effect, using radar/IMU output models are trained as the GPS information with reference to benchmark, when GPS losing locks using trained The rate predictions of Model forecast system.
It is applied in specific embodiment according to above-mentioned vehicle speed measuring method, that inertial navigation system uses is MEMS IMU, trailer-mounted radar are the Doppler radars of 24Ghz, and using the low precision GPS of sample frequency 1Hz as benchmark, experiment driving path is Complicated landform is run at a low speed, speed about 32km/h, wherein preceding 230 seconds GPS are effective, rear 60sGPS losing locks are utilized and established Model carries out vehicle speed estimation.
Estimated result is as shown in figure 3, IMU, vFwd and GPS are respectively the velocity measurement knot of IMU, Doppler radar and GPS Fruit, NN be using neural network method training and prediction GPS effectively and losing lock situation under velocity estimation as a result, can from result Know that the model established in GPS losing locks and method of estimation can accurately estimate the speed of vehicle.

Claims (5)

1. a kind of Doppler radar assistant GPS/INS vehicle speed measuring methods, it is characterised in that：Include the following steps：
1) coordinate system definition and conversion
Define bodywork reference frame m, origin is located at car body center O, reference axis be respectively directed to the car body right side-it is preceding-on；
The navigational coordinate system n of inertial reference calculation is defined, heart O, reference axis press the east-north-old name for the Arabian countries in the Middle East in location to origin respectively in the carrier To；
Define IMU coordinate system b, origin is located at car body center O, reference axis respectively refer to the right-it is preceding-on；
Define Doppler radar coordinate system d, origin sensitivity center O on its interiord, reference axis be respectively directed to car body it is right-preceding-on；
There is installation error between bodywork reference frame m when due to IMU installations on the car body, it is assumed that two coordinate systems, three directions Angle is respectively α, and beta, gamma then can define coordinate conversion matrix：
2) radar and IMU data predictions
Since the influence of complex environment factor can make with the presence of abnormal data in signal, using filtering side when Doppler radar tests the speed Method pre-processes data.
3) foundation of radar and IMU data noise models
According to the principle of Doppler range rate measurement, speed vdWith Doppler frequency fdRelationship can indicate
λ is wavelength, and θ is radar antenna direction and direction of motion angle (rad), sets the floor projection and x of antenna beamdAxle clamp Angle is ρ, and antenna beam angle with horizontal plane is σ, according to angular position relative
Cos θ=cos σ cos ρ (3)
vd=V cos θ (4)
In view of the scale factor error K of Doppler radar measurement, the error of zeroOutput noise wdAnd lever arm error, in fact Border exportsAnd combine above formula, i.e.,
Wherein LdThe 3 dimension installation lever arms between car body central point and Doppler radar central point.
According to formula (5), car speed can be expressed as by radar measurement value under bodywork reference frame
Wherein K1=1/Kcos θ, radar measurement error model are represented by
IMU measurements obtain relationship between bearer rate and the speed of inertial reference calculation can be by coordinate conversion matrixIt indicates
IMU measurement errors are represented by under navigational coordinate system
Wherein φn×=| φU 0 -φE|, φE、φN、φUFor attitude error angle, δ KAIt is respectively the scale of accelerometer with δ A System errors and fix error angle, δ KGWith the scale coefficient error and fix error angle that δ G are gyro.
4) the combined system training based on neural network and prediction model, as radar, IMU and GPS effective, using as ginseng The GPS information of benchmark is examined to train radar/IMU output models, the speed of trained Model forecast system is utilized when GPS losing locks Spend predicted value.
2. a kind of Doppler radar assistant GPS/INS vehicle speed measuring methods according to claim 1, it is characterised in that：Institute It states in step 2 and data is pre-processed using the filtering method of wavelet transformation, mainly small echo is become according to actual experiment data The major parameter changed is selected so that vehicle speed estimation precision higher.
3. a kind of Doppler radar assistant GPS/INS vehicle speed measuring methods according to claim 1 or 2, it is characterised in that： Complex environment factor includes urban environment in the step 2.
4. a kind of Doppler radar assistant GPS/INS vehicle speed measuring methods according to claim 1, it is characterised in that：Institute State in step 4 using as the GPS information with reference to benchmark come to train radar/IMU output models solved when being worked normally by GPS Obtained vehicle speed information improves it as the reference value to radar/INS systems by the amendment to model parameter Precision of prediction.
5. a kind of Doppler radar assistant GPS/INS vehicle speed measuring methods according to claim 1 or 2, it is characterised in that： Complex environment factor includes landform in the step 2.
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CN111722195A (en) * 2020-06-29 2020-09-29 上海蛮酷科技有限公司 Radar occlusion detection method and computer storage medium
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CN111912405A (en) * 2019-05-10 2020-11-10 中国人民解放军火箭军工程大学 Combined navigation method and system based on vehicle-mounted inertial measurement unit and Doppler radar
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CN114690221A (en) * 2021-12-22 2022-07-01 北京航天时代激光导航技术有限责任公司 Elman neural network and Kalman fusion filtering algorithm based on wavelet threshold method
CN115437303A (en) * 2022-11-08 2022-12-06 壹控智创科技有限公司 Wisdom safety power consumption monitoring and control system
CN115437303B (en) * 2022-11-08 2023-03-21 壹控智创科技有限公司 Wisdom safety power consumption monitoring and control system

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