CN105575154A - Vehicle GPS positioning lost data compensation method - Google Patents

Abstract

The invention provides a vehicle GPS positioning lost data compensation method. The method comprises the steps of: sending data collected from a satellite to a database and storing the data; then judging the time interval between any two pieces of data is within a set sampling interval section; and when the time interval between any two pieces of data is out of the set sampling interval section, then determining that data loss exists, and carrying out data compensation. The invention provides the vehicle GPS positioning lost data compensation method based on least square method fitting, isolated data points of a GPS are fit into a curve model, the continuity of the GPS data is increased by means of data compensation, a basis is provided for subsequent data analysis and processing, and the problem that a vehicle fails to receive GPS positioning data in the case of GPS positioning data loss is solved.

Description

A kind of vehicle GPS location obliterated data compensation method

Technical field:

The present invention relates to physical field, particularly relate to mechanics of communication, particularly a kind of vehicle GPS location obliterated data compensation method.

Background technology:

In prior art, vehicle extensively utilizes GPS to obtain locator data.Although GPS positioning system has higher precision at present, but there is the defects such as drift and signal blind zone due to GPS locator data, thus the loss of data causing GPS device to collect, useful data message goes out active, namely sampled data interval is greater than the sampling interval of equipment, and thus GPS locator data is discontinuous.

Summary of the invention:

The object of the present invention is to provide a kind of vehicle GPS to locate obliterated data compensation method, described this vehicle GPS location obliterated data compensation method will solve vehicle GPS positioning acquisition data in prior art and there is the technical matters of loss of data.

A kind of vehicle GPS location obliterated data compensation method of the present invention, comprise a step from satellite image data, wherein, described from after the step of satellite image data completes, the data collected are sent to database and preserve, then to judge in data that the time interval of two data is arbitrarily whether in the sampling interval interval of a setting, before and after arbitrarily the time interval of two data exceed sampling interval interval time, then judge there is loss of data and carries out compensation data.

Further, to judge in data that the time interval of two data is arbitrarily whether in the step in the sampling interval interval of a setting, find the curve that the quadratic sum of the residual error of respective value in location the data obtained and matched curve is minimum, the process of the function of the matched curve described in generation comprises the following steps:

Step one, locates the data (x obtained according to GPS _i, y _i), (i=0,1,2 ..., m), calculate adjacent 2 points

The time interval between GPS locator data; Wherein x, y are coordinate figure;

Step 2, adopts the method matching of least square fitting, generates optimal fitting curvilinear function:

$\left(\begin{array}{ccccc}\Σx_i^0& \Σx_i^1& \Σx_i^2& \mathrm{...}& \Σx_i^m\\ \Σx_i^1& \Σx_i^2& \Σx_i^3& \mathrm{...}& \Σx_i^{m+1}\\ \Σx_i^2& \Σx_i^3& \Σx_i^4& \mathrm{...}& \Σx_i^{m+2}\\ .& .& .& \mathrm{...}& .\\ .& .& .& & .\\ .& .& .& & .\\ \Σx_i^m& \Σx_i^{m+1}& \Σx_i^{m+2}& \mathrm{...}& \Σx_i^{2m}\end{array}\right)\left(\begin{array}{c}{a}_0\\ a_1\\ a_2\\ .\\ .\\ .\\ a_m\end{array}\right)=\left(\begin{array}{c}\Σy_i{x}_i^0\\ \Σy_i{x}_i^1\\ \Σy_i{x}_i^2\\ .\\ .\\ .\\ \Σy_i{x}_i^m\end{array}\right)$

Wherein matched curve function coefficients a ₀, a ₁, a ₂..., a _m, pass through A=X ^-1y calculates.

Vehicle GPS location obliterated data compensation method according to claim 2, is characterized in that, in the step of described compensation data, first choose 1 curvilinear function and carry out curve fitting, judge the residual sum of squares (RSS) of matching if residual sum of squares (RSS) meet error range requirement, then Output rusults, end loop, otherwise the function number of times carried out curve fitting adds 1, proceeds matching.

Principle of work of the present invention is: application least square fitting curve is out called optimum fit curve, matched curve is met: GPS locates respective value on the data obtained and curve and obtains a minimum curve of the quadratic sum of residual error, namely given GPS is located to the data (x obtained _i, y _i) (i=0,1,2 ..., m), find the funtcional relationship y=f (x) between independent variable and dependent variable.If experimental data x _ithe residual error existed is δ _i=f (x _i)-y _i, (i=0,1,2 ..., m), require to find a curve y=s ^*x (), makes the quadratic sum of residual error minimum, namely

$\underset{i=0}{\overset{m}{\Σ}}{\mathrm{\δ}}_i^2=\underset{i=0}{\overset{m}{\Σ}}{\[S^{*}\left(x_i\right)-y_i\]}^2=\min \underset{i=0}{\overset{m}{\Σ}}{\[S^{*}\left(x_i\right)-y_i\]}^2,(i=0,1,2,\mathrm{...},m)$

The quadratic sum of residual error is less, and the effect of matching is better, illustrates that the relation of Two Variables is closer.But residual error not only has relation with raw data, the function also and selected by matching has relation.So suitable function will be chosen, eliminate the residual error that each factor causes.

Existing GPS locator data (X _i, Y _i), (i=1,2,3 ..., n), suppose to want matching curvilinear function be out y=f (x, a), a=(a ₀, a ₁, a ₂..., a _m) be fitting coefficient.

Ask coefficient a, make get fitting of a polynomial, if y=f is (x, a)=a ₀+ a ₁x+a ₂x ²+ ... + a _mx ^m, order then obtain coefficient a ₀, a ₁, a ₂..., a _m, required curvilinear equation namely:

y＝f(x,a)＝a ₀+a ₁x+a ₂x ²+…+a _mx ^m

Utilize the function of matching, solve the functional value that GPS locates obliterated data position.

With GPS sampling data time interval T for standard, the time interval is at interval [nT+t ₀, (n+1) T] in GPS sampled data points, then compensate n data, wherein t ₀adaptation coefficient, can free adjustment according to actual conditions, carry out data fitting to sectional type.N is natural number.

In order to preferably optimum fitting function, first choose 1 function when matching and carry out matching, judge the residual sum of squares (RSS) of matching make error in allowed limits then end loop, otherwise the function number of times of matching adds 1, continues above-mentioned steps, until search out optimum fitting function.

The present invention and prior art compare, and its effect is actively with obvious.The present invention adopts the GPS based on least square fitting to locate the method for obliterated data compensation, the data point isolated by GPS, fit to a curve model, pass through compensation data, add the continuity of gps data, for subsequent data analysis process is laid a good foundation, solve vehicle cannot receive GPS locator data problem when GPS locator data is lost.

Accompanying drawing illustrates:

Fig. 1 is the schematic diagram of vehicle GPS of the present invention location obliterated data compensation method.

Fig. 2 is the process flow diagram of vehicle GPS of the present invention location obliterated data compensation method.

Embodiment:

Embodiment 1:

As depicted in figs. 1 and 2, a kind of vehicle GPS location obliterated data compensation method of the present invention, comprise a step from satellite image data, wherein, described from after the step of satellite image data completes, the data collected are sent to database and preserve, then to judge in data that the time interval of two data is arbitrarily whether in the sampling interval interval of a setting, when the time interval of two data exceeds sampling interval interval before and after arbitrarily, then judge there is loss of data and carries out compensation data.

Further, to judge in data that the time interval of two data is arbitrarily whether in the step in the sampling interval interval of a setting, find the curve that the quadratic sum of the residual error of respective value in location the data obtained and matched curve is minimum, the process of the function of the matched curve described in generation comprises the following steps:

Step one, locates the data (x obtained according to GPS _i, y _i), (i=0,1,2 ..., m), calculate adjacent 2 points

The time interval between GPS locator data; Wherein x, y are coordinate figure;

Step 2, adopts the method matching of least square fitting, generates optimal fitting curvilinear function:

$\left(\begin{array}{ccccc}\Σx_i^0& \Σx_i^1& \Σx_i^2& \mathrm{...}& \Σx_i^m\\ \Σx_i^1& \Σx_i^2& \Σx_i^3& \mathrm{...}& \Σx_i^{m+1}\\ \Σx_i^2& \Σx_i^3& \Σx_i^4& \mathrm{...}& \Σx_i^{m+2}\\ .& .& .& \mathrm{...}& .\\ .& .& .& & .\\ .& .& .& & .\\ \Σx_i^m& \Σx_i^{m+1}& \Σx_i^{m+2}& \mathrm{...}& \Σx_i^{2m}\end{array}\right)\left(\begin{array}{c}{a}_0\\ a_1\\ a_2\\ .\\ .\\ .\\ a_m\end{array}\right)=\left(\begin{array}{c}\Σy_i{x}_i^0\\ \Σy_i{x}_i^1\\ \Σy_i{x}_i^2\\ .\\ .\\ .\\ \Σy_i{x}_i^m\end{array}\right)$

Wherein matched curve function coefficients a ₀, a ₁, a ₂..., a _m, pass through A=X ^-1y calculates.

Vehicle GPS location obliterated data compensation method according to claim 2, is characterized in that, in the step of described compensation data, first choose 1 curvilinear function and carry out curve fitting, judge the residual sum of squares (RSS) of matching if residual sum of squares (RSS) meet error range requirement, then Output rusults, end loop, otherwise the function number of times carried out curve fitting adds 1, proceeds matching.

Principle of work of the present invention is: application least square fitting curve is out called optimum fit curve, matched curve is met: GPS locates respective value on the data obtained and curve and obtains a minimum curve of the quadratic sum of residual error, namely given GPS is located to the data (x obtained _i, y _i) (i=0,1,2 ..., m), find the funtcional relationship y=f (x) between independent variable and dependent variable.If experimental data x _ithe residual error existed is δ _i=f (x _i)-y _i, (i=0,1,2 ..., m), require to find a curve y=s ^*x (), makes the quadratic sum of residual error minimum, namely

$\underset{i=0}{\overset{m}{\Σ}}{\mathrm{\δ}}_i^2=\underset{i=0}{\overset{m}{\Σ}}{\[S^{*}\left(x_i\right)-y_i\]}^2=\min \underset{i=0}{\overset{m}{\Σ}}{\[S^{*}\left(x_i\right)-y_i\]}^2,(i=0,1,2,\mathrm{...},m)$

The quadratic sum of residual error is less, and the effect of matching is better, illustrates that the relation of Two Variables is closer.But residual error not only has relation with raw data, the function also and selected by matching has relation.So suitable function will be chosen, eliminate the residual error that each factor causes.

Existing GPS locator data (X _i, Y _i), (i=1,2,3 ..., n), suppose to want matching curvilinear function be out y=f (x, a), a=(a ₀, a ₁, a ₂..., a _m) be fitting coefficient.

Ask coefficient a, make get fitting of a polynomial, if y=f is (x, a)=a ₀+ a ₁x+a ₂x ²+ ... + a _mx ^m, order then obtain coefficient a ₀, a ₁, a ₂..., a _m, required curvilinear equation namely:

y＝f(x,a)＝a ₀+a ₁x+a ₂x ²+…+a _mx ^m

Utilize the function of matching, solve the functional value that GPS locates obliterated data position.

With GPS sampling data time interval T for standard, the time interval is at interval [nT+t ₀, (n+1) T] in GPS sampled data points, then compensate n data, wherein t ₀adaptation coefficient, can free adjustment according to actual conditions, carry out data fitting to sectional type.N is natural number.

In order to preferably optimum fitting function, first choose 1 function when matching and carry out matching, judge the residual sum of squares (RSS) of matching make error in allowed limits then end loop, otherwise the function number of times of matching adds 1, continues above-mentioned steps, until search out optimum fitting function.

Claims (3)

1. a vehicle GPS location obliterated data compensation method, comprise a step from satellite image data, it is characterized in that: described from after the step of satellite image data completes, the data collected are sent to database and preserve, then to judge in data that the time interval of two data is arbitrarily whether in the sampling interval interval of a setting, when the time interval of two data exceeds sampling interval interval before and after arbitrarily, then judge there is loss of data and carries out compensation data.

2. vehicle GPS location obliterated data compensation method according to claim 1, it is characterized in that: to judge in data that the time interval of two data is arbitrarily whether in the step in the sampling interval interval of a setting, find the curve that the quadratic sum of the residual error of respective value in location the data obtained and matched curve is minimum, the process of the function of the matched curve described in generation comprises the following steps:

Step one, locates the data (x obtained according to GPS _i, y _i), (i=0,1,2 ..., m), calculate the time interval between adjacent 2 GPS locator datas; Wherein x, y are coordinate figure;

Step 2, adopts the method matching of least square fitting, generates optimal fitting curvilinear function:

$\left(\begin{array}{ccccc}{\mathrm{\Σx}}_i^0& {\mathrm{\Σx}}_i^1& {\mathrm{\Σx}}_i^2& ...& {\mathrm{\Σx}}_i^m\\ {\mathrm{\Σx}}_i^1& {\mathrm{\Σx}}_i^2& {\mathrm{\Σx}}_i^3& ...& {\mathrm{\Σx}}_i^{m+1}\\ {\mathrm{\Σx}}_i^2& {\mathrm{\Σx}}_i^3& {\mathrm{\Σx}}_i^4& ...& {\mathrm{\Σx}}_i^{m+2}\\ \begin{array}{c}.\\ .\\ .\end{array}& \begin{array}{c}.\\ .\\ .\end{array}& \begin{array}{c}.\\ .\\ .\end{array}& \begin{array}{c}\\ \\ \end{array}& \begin{array}{c}.\\ .\\ .\end{array}\\ {\mathrm{\Σx}}_i^m& {\mathrm{\Σx}}_i^{m+1}& {\mathrm{\Σx}}_i^{m+2}& ...& {\mathrm{\Σx}}_i^{2m}\end{array}\right)\left(\begin{array}{c}{a}_0\\ a_1\\ a_2\\ \begin{array}{c}.\\ .\\ .\end{array}\\ a_m\end{array}\right)=\left(\begin{array}{c}{\mathrm{\Σy}}_i{x}_i^0\\ {\mathrm{\Σy}}_i{x}_i^1\\ {\mathrm{\Σy}}_i{x}_i^2\\ \begin{array}{c}.\\ .\\ .\end{array}\\ {\mathrm{\Σy}}_i{x}_i^m\end{array}\right)$

Wherein matched curve function coefficients a ₀, a ₁, a ₂..., a _m, pass through A=X ^-1y calculates.

3. vehicle GPS location obliterated data compensation method according to claim 2, is characterized in that, in the step of described compensation data, first choose 1 curvilinear function and carry out curve fitting, judge the residual sum of squares (RSS) of matching if residual sum of squares (RSS) meet error range requirement, then Output rusults, end loop, otherwise the function number of times carried out curve fitting adds 1, proceeds matching.