CN101082496A - System capable of effectively decreasing vehicle GPS navigation error - Google Patents

Abstract

This invention discloses a sort of system which reduces effectively the GPS navigation error to the cars; it can reduce effectively the navigation error, and increase the positioning accuracy. Its technical project is that: This invention combines the GPS with the dead reckoning and the electronic map information in the revolutionary method by the manner of federation filtering according to the principle of federation Kalman filter. The system in this invention consists of the module of electronic map filter, the module of GPS filter, the module of the time revolution and data amalgamation. This invention applies to the field of the GPS navigation system.

Description

Effectively reduce the system of vehicle GPS navigation error

Technical field

The present invention relates to a kind of GPS Vehicular navigation system, relate in particular to a kind of system that can effectively reduce the navigation time error.

Background technology

In the existing GPS navigation, correlation techniques such as reckoning and electronic map match are arranged.Wherein reckoning is by accumulation time dependent travelling speed of automobile and angle, extrapolates the travel track of automobile, thereby learns the position of particular moment automobile.The electronic map match technology is to compare with road characteristic points by current anchor point and last anchor point are put into map datum, draws the most probable site of road of current anchor point by exclusive method.

But there is certain error in existing GPS navigation, in some field that need carry out accurate relatively vehicle location, needs to reduce navigation error, improves bearing accuracy.

Summary of the invention

The objective of the invention is to address the above problem, a kind of system that effectively reduces the vehicle GPS navigation error is provided, it can effectively reduce navigation error, improves bearing accuracy.

Technical scheme of the present invention is: a kind of system that effectively reduces the vehicle GPS navigation error comprises:

The electronic chart filter module, position that when map datum satisfies filtering condition reckoning is obtained and course information adopt with position that is obtained by map match and course information concentrates kalman filter method to carry out combined filter, and the filter value time that outputs to upgraded and data fusion module as one of data source of data fusion, and the initial value of next filtering iteration is obtained by the fusion results feedback;

The GPS filter module, position that when gps data satisfies filtering condition reckoning is obtained and course information adopt with position that is obtained by GPS and course information concentrates kalman filter method to carry out combined filter, and the filter value time that outputs to upgraded and data fusion module as one of data source of data fusion, and the initial value of next iteration is obtained by the fusion results feedback;

Time upgrades and data fusion module, the filter value time of carrying out that described electronic chart filter module and GPS filter module are sent upgrades and the processing of data fusion, and the result of data fusion is fed back to described electronic chart filter module and GPS filter module.

The above-mentioned system that effectively reduces the vehicle GPS navigation error, wherein, in the described electronic chart filter module, the state equation of this wave filter and measurement equation are $X_k^M={\mathrm{\Φ}}_{k,k-1}{X}_{k-1}{\mathrm{\Γ}}_{k-1}{W}_{k-1},$ $Z_k^M=H_k{X}_k+V_k,$ Wherein

$\mathrm{\Γ}=\left[\begin{array}{cc}0& 0\\ 0& 0\\ 0& 0\\ 1& 0\\ 0& 1\end{array}\right]$ $H=\left[\begin{array}{ccccc}0& 0& 1& 0& 0\\ 0& 0& 0& 1& 0\\ 0& 0& 0& 0& 1\end{array}\right]$

${\mathrm{\Φ}}_{k,k-1}=\left[\begin{array}{ccccc}1-\mathrm{\βv}& 0& 0& 0& 0\\ 0& 1-\mathrm{\βk}& 0& 0& 0\\ 0& 0& 1& 0& -\left(\frac{v*\cos \left(\mathrm{\Ψ}\right)}{\mathrm{Re}}\right)\\ 0& 0& \left(\frac{v*\cos \left(\mathrm{\Ψ}\right)*\sin \left(\mathrm{\φ}\right)}{\mathrm{Re}*\cos \left(\mathrm{\φ}\right)}\right)& 1& -\left(\frac{v*\sin \left(\mathrm{\Ψ}\right)}{\mathrm{Re}*\cos \left(\mathrm{\Ψ}\right)}\right)\\ (1/k)& -\left(\frac{U-U_0}{k^2}\right)& 0& 1& 0\end{array}\right]$

In the above-mentioned formula, δ _, δ _λ, δ _Ψ, δ _k, δ _vBe respectively latitude error, longitude error, course error, gyro calibration factor sum of errors gyro zero error partially,  _M, λ _M, Ψ _MBe respectively carrier longitude and latitude and the course angle that obtains by map,  _R, λ _R, Ψ _RBe respectively carrier longitude and latitude and the course angle that is obtained by reckoning, Ψ is the current course angle of vehicle, U, U ₀, K is respectively current gyroscope output voltage, total null voltage and calibration factor, , λ are represented latitude and the longitude that vehicle is current respectively, v is the current travelling speed of vehicle, Re is a reference ellipsoid equatorial plane radius, β _k, β _vBe the inverse correlation time constant, W _K-1Be that K-1 r constantly maintains system noise, V _kIt is K m dimension measurement noise constantly.

The above-mentioned system that effectively reduces the vehicle GPS navigation error, wherein, in the described GPS filter module, the state equation of this wave filter and measurement equation are: $X_k^G={\mathrm{\Φ}}_{k,k-1}{X}_{k-1}+{\mathrm{\Γ}}_{k-1}{W}_{k-1},$ $Z_k^G=H_k{X}_k+V_k,$ Wherein

In the above-mentioned formula,  _G, λ _G, Ψ _GBe respectively carrier longitude and latitude and the course angle that obtains by GPS.

The above-mentioned system that effectively reduces the vehicle GPS navigation error, wherein, in described time renewal and the data fusion module, the equation that the described time upgrades is:

${\hat{X}}_{g,k/k-1}={\mathrm{\Φ}}_{k,k-1}{\hat{X}}_{g,k-1},$ P _g，k/k-1＝ΦP _g，k-1Φ ^T+Γ _k-1QΓ ^T _k-1

In the formula P _{G, k-1}Be respectively the state estimation and the estimation variance matrix of senior filter,

P _{G, k/k-1}Be respectively the status predication value and the predicated error variance matrix of senior filter;

The data fusion equation of global optimum is:

${\hat{X}}_{g,k}={\hat{X}}_{g,k-1}+P_{g,k}({P^{-1}}_{G,k}{\hat{X}}_k^G+{P^{-1}}_{M,k}{\hat{X}}_k^M),$ P _g，k＝(P _g，k-1 ^-1+P ^-1 _G，k+P ^-1 _M，k) ^-1

P in the formula _{G, k}, P _{M, k}Be respectively the state error variance battle array of GPS wave filter and electronic chart wave filter,

Be respectively the state estimation of GPS wave filter and electronic chart wave filter, Φ is the transition matrix of system's current time, and Q is the covariance matrix of current time system noise W.

The above-mentioned system that effectively reduces the vehicle GPS navigation error, wherein, described system makes up GPS, reckoning and the electronic map information mode by federal filtering according to federal Kalman filter principle.

System of the present invention has following beneficial effect compared to existing technology: the present invention makes up GPS, reckoning and the electronic map information mode by federal filtering according to federal Kalman filter principle, with the error of effective minimizing vehicle GPS navigation.

Description of drawings

Fig. 1 is the synoptic diagram that the present invention effectively reduces the system embodiment of vehicle GPS navigation error.

Embodiment

The invention will be further described below in conjunction with drawings and Examples.

See also Fig. 1, system 10 mainly comprises electronic chart filter module 11, GPS filter module 12 and senior filter module 13.Wherein, comprise time updating submodule 131 and optimum fusion submodule 132 in the senior filter module 13 again.

In electronic chart filter module 11, position that when map datum satisfies filtering condition reckoning 14 is obtained and course information adopt with position that is obtained by map match 15 and course information concentrates kalman filter method to carry out combined filter, and this filter value is outputed to optimum fusion submodule 132 in the senior filter module 13 as one of data source of data fusion.Result after the initial value of filtering next time iteration is merged by senior filter 13 obtains through feedback.

The state equation of this filter module 11 and measurement equation are: $X_k^M={\mathrm{\Φ}}_{k,k-1}{X}_{k-1}{\mathrm{\Γ}}_{k-1}{W}_{k-1},$ $Z_k^M=H_k{X}_k+V_k,$ Wherein

$\mathrm{\Γ}=\left[\begin{array}{cc}0& 0\\ 0& 0\\ 0& 0\\ 1& 0\\ 0& 1\end{array}\right]$ $H=\left[\begin{array}{ccccc}0& 0& 1& 0& 0\\ 0& 0& 0& 1& 0\\ 0& 0& 0& 0& 1\end{array}\right]$

${\mathrm{\Φ}}_{k,k-1}=\left[\begin{array}{ccccc}1-\mathrm{\βv}& 0& 0& 0& 0\\ 0& 1-\mathrm{\βk}& 0& 0& 0\\ 0& 0& 1& 0& -\left(\frac{v*\cos \left(\mathrm{\Ψ}\right)}{\mathrm{Re}}\right)\\ 0& 0& \left(\frac{v*\cos \left(\mathrm{\Ψ}\right)*\sin \left(\mathrm{\φ}\right)}{\mathrm{Re}*\cos \left(\mathrm{\φ}\right)}\right)& 1& -\left(\frac{v*\sin \left(\mathrm{\Ψ}\right)}{\mathrm{Re}*\cos \left(\mathrm{\Ψ}\right)}\right)\\ (1/k)& -\left(\frac{U-U_0}{k^2}\right)& 0& 1& 0\end{array}\right]$

In the above-mentioned formula, δ _, δ _λ, δ _Ψ, δ _k, δ _vBe respectively latitude error, longitude error, course error, gyro calibration factor sum of errors gyro zero error partially,  _M, λ _ω, Ψ _MBe respectively carrier longitude and latitude and the course angle that obtains by map match 15,  _R, λ _δ, Ψ _RBe respectively carrier longitude and latitude and the course angle that is obtained by reckoning 14, Ψ is the current course angle of vehicle, U, U ₀, K is respectively current gyroscope output voltage, total null voltage and calibration factor, , λ are represented latitude and the longitude that vehicle is current respectively, v is the current travelling speed of vehicle, Re is a reference ellipsoid equatorial plane radius, β _k, β _vBe the inverse correlation time constant, W _K-1Be that K-1 r constantly maintains system noise, V _kIt is K m dimension measurement noise constantly.

In GPS filter module 12, when gps data satisfies filtering condition, position that reckoning 14 is obtained and course information adopt with position that is obtained by GPS 16 and course information concentrates kalman filter method to carry out combined filter, and filter value is outputed to optimum fusion submodule 132 in the senior filter module 13 as one of data source of data fusion.Result after the initial value of filtering next time iteration is merged by senior filter module 13 obtains through feedback.The state equation of this wave filter and measurement equation are: $X_k^G={\mathrm{\Φ}}_{k,k-1}{X}_{k-1}+{\mathrm{\Γ}}_{k-1}{W}_{k-1},$ $Z_k^G=H_k{X}_k+V_k,$ Wherein

Wherein, δ _, δ _λ, δ _Ψ, δ _k, δ _vBe respectively latitude error, longitude error, course error, gyro calibration factor sum of errors gyro zero error partially,  _G, λ _G, Ψ _GBe respectively carrier longitude and latitude and the course angle that obtains by GPS 16,  _R, λ _R, Ψ _RBe respectively carrier longitude and latitude and the course angle that obtains by reckoning 14, Φ _{K, k-1}, Γ, H definition and electronic chart filter module 11 in identical.

Senior filter module 13 is cores of whole federal wave filter, and the time renewal equation of its time updating submodule 131 is: ${\hat{X}}_{g,k/k-1}={\mathrm{\Φ}}_{k,k-1}{\hat{X}}_{g,k-1},$ P _g，k/k-1＝ΦP _g，k-1Φ ^T+Γ _k-1QΓ ^T _k-1。In the following formula

P _{G, k-1}Be respectively the state estimation and the estimation variance matrix of senior filter,

P _{G, k/k-1}Be respectively the status predication value and the predicated error variance matrix of senior filter.The data fusion equation of optimum fusion submodule 132 is: ${\hat{X}}_{g,k}={\hat{X}}_{g,k-1}+P_{g,k}({P^{-1}}_{G,k}{\hat{X}}_k^G+{P^{-1}}_{M,k}{\hat{X}}_k^M),$ P _g，k＝(P _g，k-1 ^-1+P ^-1 _G，k+P ^-1 _M，k) ^-1。In the following formula, P _{G, k}, P _{M, k}Be respectively the state error variance battle array of GPS wave filter and electronic chart wave filter,

Be respectively the state estimation of GPS wave filter and electronic chart wave filter, Φ is the transition matrix of system's current time, and Q is the covariance matrix of current time system noise W.

Should be understood that inventive point of the present invention is that mainly according to federal Kalman filter principle, innovatively with GPS, reckoning and electronic map information make up by the mode of federal filtering, to effectively reduce the error of vehicle GPS navigation.Wherein Kalman filtering is a kind of minimum variance estimate in the optimal estimation theory that proposes nineteen sixty, estimates desired signal by algorithm from the measured value relevant with being extracted signal.

The foregoing description provides to those of ordinary skills and realizes or use of the present invention; those of ordinary skills can be under the situation that does not break away from invention thought of the present invention; the foregoing description is made various modifications or variation; thereby protection scope of the present invention do not limit by the foregoing description, and should be the maximum magnitude that meets the inventive features that claims mention.

Claims (5)

1 one kinds of systems that effectively reduce the vehicle GPS navigation error is characterized in that, described system comprises:

The electronic chart filter module, position that when map datum satisfies filtering condition reckoning is obtained and course information adopt with position that is obtained by map match and course information concentrates kalman filter method to carry out combined filter, and the filter value time that outputs to upgraded and data fusion module as one of data source of data fusion, and the initial value of next filtering iteration is obtained by the fusion results feedback;

The GPS filter module, position that when gps data satisfies filtering condition reckoning is obtained and course information adopt with position that is obtained by GPS and course information concentrates kalman filter method to carry out combined filter, and the filter value time that outputs to upgraded and data fusion module as one of data source of data fusion, and the initial value of next iteration is obtained by the fusion results feedback;

Time upgrades and data fusion module, the filter value time of carrying out that described electronic chart filter module and GPS filter module are sent upgrades and the processing of data fusion, and the result of data fusion is fed back to described electronic chart filter module and GPS filter module.

2 systems that effectively reduce the vehicle GPS navigation error according to claim 1 is characterized in that in the described electronic chart filter module, the state equation of this wave filter and measurement equation are $x_k^M={\mathrm{\Φ}}_{k,k-1}{X}_{k-1}{\mathrm{\Γ}}_{k-1}{W}_{k-1},$ $Z_k^M=H_k{X}_k+V_k,$ Wherein

$\mathrm{\Γ}=\left[\begin{array}{cc}0& 0\\ 0& 0\\ 0& 0\\ 1& 0\\ 0& 1\end{array}\right]$ $H=\left[\begin{array}{ccccc}0& 0& 1& 0& 0\\ 0& 0& 0& 1& 0\\ 0& 0& 0& 0& 1\end{array}\right]$

${\mathrm{\Φ}}_{k,k-1}=\left[\begin{array}{ccccc}1-\mathrm{\βv}& 0& 0& 0& 0\\ 0& 1-\mathrm{\βk}& 0& 0& 0\\ 0& 0& 1& 0& -\left(\frac{v*\cos \left(\mathrm{\Ψ}\right)}{\mathrm{Re}}\right)\\ 0& 0& \left(\frac{v*\cos \left(\mathrm{\Ψ}\right)*\sin \left(\mathrm{\φ}\right)}{\mathrm{Re}*\cos \left(\mathrm{\φ}\right)}\right)& 1& -\left(\frac{v*\sin \left(\mathrm{\Ψ}\right)}{\text{Re*cos}\left(\mathrm{\Ψ}\right)}\right)\\ (1/k)& -\left(\frac{U-U_0}{k^2}\right)& 0& 1& 0\end{array}\right]$

In the above-mentioned formula, δ _, δ _λ, δ _Ψ, δ _k, δ _vBe respectively latitude error, longitude error, course error, gyro calibration factor sum of errors gyro zero error partially,  _M, λ _M, Ψ _MBe respectively carrier longitude and latitude and the course angle that obtains by map,  _R, λ _R, Ψ _RBe respectively carrier longitude and latitude and the course angle that is obtained by reckoning, Ψ is the current course angle of vehicle, U, U ₀, k is respectively current gyroscope output voltage, total null voltage and calibration factor, , λ are represented latitude and the longitude that vehicle is current respectively, v is the current travelling speed of vehicle, Re is a reference ellipsoid equatorial plane radius, β _k, β _vBe the inverse correlation time constant, W _K-1Be that k-1 r constantly maintains system noise, V _kIt is k m dimension measurement noise constantly.

3 systems that effectively reduce the vehicle GPS navigation error according to claim 2 is characterized in that, in the described GPS filter module, the state equation of this wave filter and measurement equation are: $X_k^G={\mathrm{\Φ}}_{k,k-1}{X}_{k-1}+{\mathrm{\Γ}}_{k-1}{W}_{k-1},$ $Z_k^G=H_k{X}_k+V_k,$ Wherein

In the above-mentioned formula,  _G, λ _G, Ψ _GBe respectively carrier longitude and latitude and the course angle that obtains by GPS.

4 systems that effectively reduce the vehicle GPS navigation error according to claim 3 is characterized in that, in described time renewal and the data fusion module, the equation that the described time upgrades is:

${\hat{X}}_{g,k/k-1}={\mathrm{\Φ}}_{k,k-1}{\hat{X}}_{g,k-1},$ $P_{g,k/k-1}=\mathrm{\Φ}{P}_{g,k-1}{\mathrm{\Φ}}^T+{\mathrm{\Γ}}_{k-1}Q{{\mathrm{\Γ}}^T}_{k-1}$

In the formula

P _{G, k-1}Be respectively the state estimation and the estimation variance matrix of senior filter, P _{G, k/k-1}Be respectively the status predication value and the predicated error variance matrix of senior filter;

The data fusion equation of global optimum is:

${\hat{X}}_{g,k}={\hat{X}}_{g,k-1}+P_{g,k}({P^{-1}}_{G,k}{\hat{X}}_k^G+{P^{-1}}_{M,k}{\hat{X}}_k^M),$ $P_{g,k}={({P_{g,k-1}}^{-1}+{P^{-1}}_{G,k}+{P^{-1}}_{M,k})}^{-1}$

P in the formula _{G, k}, P _{M, k}Be respectively the state error variance battle array of GPS wave filter and electronic chart wave filter,

Be respectively the state estimation of GPS wave filter and electronic chart wave filter, Φ is the transition matrix of system's current time, and Q is the covariance matrix of current time system noise W.

5 systems that effectively reduce the vehicle GPS navigation error according to claim 1 is characterized in that, described system makes up GPS, reckoning and the electronic map information mode by federal filtering according to federal Kalman filter principle.

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