CN104180800A - Correction method and system based on track points of ADS-B (Automatic Dependent Surveillance Broadcast) system - Google Patents

Abstract

The invention relates to a correction method and system based on track points of an ADS-B (Automatic Dependent Surveillance Broadcast) system. The correction method comprises the following steps: arraying the track points according to reception time; predicting a state prediction value of the track point at the current time according to state information of the track points, and calculating a state prediction covariance matrix of the track point at the current time; predicting a measuring prediction value of the track point at the current time according to the state information of the track points, and calculating a residual error vector covariance matrix of the track point at the current time; identifying whether the track point at the current time is an outlier, and determining an attenuation factor of the track point at the current time according to an identifying result; calculating gain of a filter at the current time according to the attenuation factor of the track point at the current time; and correcting the state prediction value and the state prediction covariance matrix of the track point at the current time. According to the technical scheme, outliers in the track points can be effectively identified and dynamically corrected in real time, so that the accuracy of data of the track points is increased.

Description

Modification method and system based on ADS-B system track points

Technical field

Field is supervised in the civil aviation the present invention relates to based on ADS-B in the air, particularly relates to a kind of modification method and system based on ADS-B system track points.

Background technology

ADS-B (Automatic Dependent Surveillance Broadcast, Automatic dependent surveillance broadcast) watch-dog is in the use procedure of the aerial supervision of civil aviation, due to noise, harass, mixed disturb, the impact of the factor such as multipath interference, the track points data that obtain have often comprised larger stochastic error, cause track points data substantial deviation target actual value, become wild value.

At present, the wild value disposal route of traditional track points is to judge that by fuzzy control function whether new flight path value is wild value, if not open country value, does not adjust track estimation value; If wild value is less, adjusts gain and realize the correction to open country value; If wild value is larger, open country value is rejected.Yet the disposal route of this wild value, exists open country value judgement insensitive, cannot realize the dynamic correction in real time of open country value; For open country value in blocks continuously, also likely there is judgement and the larger situation of process errors of wild value, and for larger open country value, directly by this unruly-value rejecting, cannot realize the correction of the wild value of track points, and reduced the accuracy of track points data.

Summary of the invention

Based on this, be necessary in background technology, the wild value of existing track points disposal route occurs that wild value judgement is insensitive, cannot realize the dynamic correction in real time of open country value, reduce the problem of the integrality of track points data, a kind of modification method based on ADS-B system track points is provided, can effectively identify the open country value in track points, realization is carried out dynamic correction in real time to the open country value in track points, improves the accuracy of track points data.

For achieving the above object, the technical scheme that the embodiment of the present invention adopts is as follows:

A modification method based on ADS-B system track points, comprises step:

Track points is arranged by time of reception;

According to the status predication value of the status information prediction current time track points of track points and the status predication covariance matrix that calculates current time track points;

According to the measurement predicted value of the status information prediction current time track points of track points and the residual vector covariance matrix that calculates current time track points;

Whether whether identification current time track points is wild value, and be the decay factor that wild value is determined current time track points according to current time track points;

According to the decay factor of current time track points, calculate current time filter gain;

According to the residual vector covariance matrix of the measurement predicted value of current time track points, current track points and the status predication value of current time filter gain correction current time track points and the status predication covariance matrix of current track points.

A kind of modification method based on ADS-B system track points according to above-mentioned, the invention provides a kind of update the system based on ADS-B system track points, comprises pretreatment unit, calculates amending unit;

Described pretreatment unit is arranged by time of reception track points;

Described calculating amending unit is according to the status predication value of the status information prediction current time track points of track points and the status predication covariance matrix that calculates current time track points; According to the measurement predicted value of the status information prediction current time track points of track points and the residual vector covariance matrix that calculates current time track points; Whether whether identification current time track points is wild value, and be the decay factor that wild value is determined current time track points according to current time track points; According to the decay factor of current time track points, calculate current time filter gain; According to the residual vector covariance matrix of the measurement predicted value of current time track points, current track points and the status predication value of current time filter gain correction current time track points and the status predication covariance matrix of current track points.

According to the present invention program, first track points is arranged by time of reception; Then according to the status predication value of the status information prediction current time track points of track points and the status predication covariance matrix that calculates current time track points; And predict the measurement predicted value of current time track points and the residual vector covariance matrix that calculates current time track points; And then whether identification current time track points be wild value, and whether be the decay factor of the definite current time track points of wild value according to current time track points; Further according to the decay factor of current time track points, calculate current time filter gain again; Finally revise the status predication covariance matrix of status predication value and the current track points of current time track points.And then reach the open country value in effective identification track points, and realize the open country value in track points is carried out to dynamic correction in real time, improve the accuracy of track points data.

Accompanying drawing explanation

Fig. 1 is the embodiment process flow diagram that the present invention is based on the modification method of ADS-B system track points;

Fig. 2 is the one dimension realistic model figure that the present invention is based on the modification method of ADS-B system track points;

Fig. 3 is the one dimension simulation modification result figure that the present invention is based on the modification method of ADS-B system track points;

Fig. 4 is the update the system structural drawing that the present invention is based on ADS-B system track points.

Embodiment

For making object of the present invention, technical scheme and advantage clearer, below in conjunction with drawings and Examples, the present invention is described in further detail.Should be appreciated that embodiment described herein, only in order to explain the present invention, does not limit protection scope of the present invention.

Refer to Fig. 1, for the present invention is based on the embodiment process flow diagram of the modification method of ADS-B system track points:

Step S101: track points is arranged by time of reception;

Particularly, describedly by time of reception, arrange track points and refer to the track points receiving is converted into rectangular coordinate system by earth coordinates, then according to the time receiving, be that sequencing is arranged by the data of track points.

Step S102: according to the status predication value of the status information prediction current time track points of track points and the status predication covariance matrix that calculates current time track points;

The status predication value of described current time track points refers to the status information of current time airbound target, comprises position, speed, acceleration of airbound target etc.; The status predication covariance matrix of described current time track points is mainly the error that embodies the status predication value of current time track points, can reduce the error of the status predication value of current time track points by adjusting the status predication covariance matrix of current time track points.

Particularly, in one embodiment, the status predication value of the described status information according to track points prediction current time track points and the status predication covariance matrix that calculates current track points calculate by following equation:

$\hat{X}(k/k-1)=\mathrm{\Φ}(k/k-1)\hat{X}(k-1/k-1)+U(k-1)\stackrel{\‾}{a}(k-1)$

P(k/k-1)＝Φ(k-1)P(k-1/k-1)Φ ^T(k-1)+Q(k-1)

Wherein, for the status predication value of current time track points, P (k/k-1) is the status predication covariance matrix of current time track points, for the status predication value of previous moment track points, P (k-1/k-1) is the status predication covariance matrix of previous moment track points;

Described track points status information comprises previous moment state-transition matrix Φ (k-1), previous moment gating matrix U (k-1), previous moment Maneuver Acceleration average the status predication covariance matrix P (k/k-1) of previous moment track points, the transposed matrix Φ of previous moment state-transition matrix ^t(k-1), previous moment noise variance matrix Q (k-1).

Further:

Previous moment state-transition matrix $\mathrm{\Φ}(k-1)=\left[\begin{array}{ccc}1& T& \frac{1}{a^2}(-1+\mathrm{aT}+e^{-\mathrm{aT}})\\ 0& 1& \frac{1}{a}(1-e^{\mathrm{aT}})\\ 0& 0& e^{-\mathrm{aT}}\end{array}\right]$

α is maneuvering frequency, and maneuvering frequency α=1/20 is escaped, atmospheric disturbance maneuvering frequency α=1 in turning machine dynamic frequency α=1/60; T is the sampling time; E is Euler's numbers;

Previous moment gating matrix $U(k-1)=\left[\begin{array}{c}\frac{1}{a}(-T+\frac{{\mathrm{aT}}^2}{2}+\frac{1-e^{-\mathrm{aT}}}{a})\\ T-\frac{1-e^{-\mathrm{aT}}}{a}\\ 1-e^{-\mathrm{aT}}\end{array}\right]$

Current time noise variance matrix

$q=\left[\begin{array}{ccc}{q}_{11}& q_{12}& q_{13}\\ q_{12}& q_{22}& q_{23}\\ q_{13}& q_{23}& q_{33}\end{array}\right]$

In formula

$q_{11}=\frac{1}{{2a}^5}[1-e^{-2\mathrm{aT}}+2\mathrm{aT}+\frac{{2a}^3{T}^3}{3}-{2a}^2{T}^2-{4\mathrm{aTe}}^{-\mathrm{aT}}]$

$q_{12}=\frac{1}{{2a}^4}[e^{-2\mathrm{aT}}+1-{2e}^{-\mathrm{aT}}+{2\mathrm{aTe}}^{-\mathrm{aT}}-2\mathrm{aT}+a^2{T}^2]$

$q_{13}=\frac{1}{{2a}^3}[1-e^{-2\mathrm{aT}}-{2\mathrm{aTe}}^{-\mathrm{aT}}]$

$q_{22}=\frac{1}{{2a}^3}[{4e}^{-\mathrm{aT}}-3-e^{-2\mathrm{aT}}+2\mathrm{aT}]$

$q_{23}=\frac{1}{{2a}^2}[e^{-2\mathrm{aT}}+1-{2e}^{-\mathrm{aT}}]$

$q_{33}=\frac{1}{2a}[1-e^{-2\mathrm{aT}}]$

${\mathrm{\&sigma;}}_a^2(k-1)=\left\{\begin{array}{cc}\frac{4-\mathrm{\&pi;}}{\mathrm{\&pi;}}{[a_{\max }-\stackrel{\&OverBar;}{a}(k-1)]}^2& \stackrel{\&OverBar;}{a}(k-1)\&GreaterEqual;0\\ \frac{4-\mathrm{\&pi;}}{\mathrm{\&pi;}}{[a_{-\max }+\stackrel{\&OverBar;}{a}(k-1)]}^2& \stackrel{\&OverBar;}{a}(k-1)<0\end{array}\right.$

$\stackrel{\&OverBar;}{a}(k-1)=\widehat{\stackrel{\&CenterDot;\&CenterDot;}{x}}(k-1|k-2$

for k-2 acceleration predicted value constantly.

Step S103: according to the measurement predicted value of the status information prediction current time track points of track points and the residual vector covariance matrix that calculates current time track points;

The measurement predicted value of described current time track points refers to the positional information of current time track points, the residual vector covariance matrix of described current time track points is mainly the error that embodies the measurement predicted value of current time track points, can reduce the error of the measurement predicted value of current time track points by adjusting the residual vector covariance matrix of current time track points.

Particularly, in one embodiment, the measurement predicted value of the described prediction of the status information according to track points current time track points and the residual vector covariance matrix of calculating current time track points calculate by following equation:

$\hat{Y}(k/k-1)=H\left(k\right)\hat{X}(k/k-1)$

S(k)＝H(k)P(k/k-1)H ^T(k)+R(k)

Wherein, for the measurement predicted value of current time track points, S (k) is the residual vector covariance matrix of current time track points;

Described track points status information also comprises that current time is measured matrix H (k), current time is measured transpose of a matrix matrix H ^t(k), current time observation noise R (k).

More preferably, in one embodiment, the value of current time observation noise R (k) can be 30, and current time is measured matrix H (k)=[100].

Step S104: whether identification current time track points is wild value, and whether be the decay factor that wild value is determined current time track points according to current time track points;

Described wild value refers to this track points data substantial deviation target actual value, is wild value; Owing to will open country value being revised, thus first judge whether this track points is wild value, then determine the decay factor of this track points.

Whether particularly, in one embodiment, whether described identification current time track points is wild value, and be that wild value determines that the decay factor of current time track points calculates by following equation according to current time track points:

$\tilde{Y}\left(k\right)=|Y\left(k\right)-\hat{Y}(k/k-1)|\&le;d\sqrt{S\left(k\right)}$

$r\left(k\right)=\left\{\begin{array}{ccc}1& \left|\tilde{Y}\left(k\right)\right|\&le;& d\sqrt{S\left(k\right)}\\ {\left\{\frac{3\sqrt{S\left(k\right)}}{\left|\tilde{Y}\left(k\right)\right|}\right\}}^{1/2}& \left|\tilde{Y}\left(k\right)\right|>& d\sqrt{S\left(k\right)}\end{array}\right.$

Wherein, for the new breath of current time track points, when time, current time track points is not wild value, when time, current time track points is wild value; Y (k) is the measured value of current time track points, for the measurement predicted value of current time track points, it is the decay factor of current time track points that d is set to 3, r (k).

The value that it should be pointed out that d also can be arranged to the natural number that other are greater than zero; In the present invention program, it is whether an identification current time track points is that open country is worth preferably embodiment that d is set to 3.

Step S105: calculate current time filter gain according to the decay factor of current time track points;

Described current time filter gain refers to for revising the yield value of the status predication value of current time track points and the status predication covariance matrix of current track points;

Particularly, the described calculating of the decay factor according to current time track points current time filter gain calculates by following equation:

K(k)＝r(k)P(k)H ^T(k)S ^-1(k)

Wherein, K (k) is current time filter gain) S ^-1(k) be the inverse matrix of the residual vector covariance matrix of current time track points.

By adjusting the size (being r (k)) of the filter attenuation factor, change filter gain, realized wild value adaptive correction.By aforesaid equation, can be obtained, the correction of wild value is adjusted realization by decay factor r (k) to gain, and setting range is [0,1].Be that wild value and prediction depart from greatlyr, restriction is also larger, and when very large, gain is almost close to zero, and when in normal value, the value of gain is 1.

Step 106: according to the residual vector covariance matrix of the measurement predicted value of current time track points, current track points and the status predication value of current time filter gain correction current time track points and the status predication covariance matrix of current track points;

Particularly, describedly according to the residual vector covariance matrix of the measurement predicted value of current time track points, current track points and the status predication value of current time filter gain correction current time track points and the status predication covariance matrix of current track points, by following equation, calculate:

$\hat{X}(k/k)=\hat{X}(k/k-1)+K\left(k\right)[Y\left(k\right)-\hat{Y}(k/k-1)]$

P(k/k)＝P(k/k-1)-K(k)S(k)K ^T(k)

Wherein, for the status predication value of revised current time track points, the status predication covariance matrix that P (k/k) is revised current track points, K ^t(k) be current time filter gain transposed matrix.

Can see, the modification method based on ADS-B system track points of the present invention, can effectively identify the open country value in track points, realizes the open country value in track points is carried out to dynamic correction in real time, improves the accuracy of track points data.

More preferably, in one embodiment, after track points is arranged by time of reception, before the status predication covariance matrix of the status predication value of prediction current time track points and calculating current time track points, can also comprise step:

The sampling time interval of the time interval of every two track points and wave filter is compared;

If the time interval is inconsistent, judges between these two track points and exist track points to lose;

By open country value, the track points of losing is filled.

Particularly, the track points of losing can numerical value be 0 open country value is filled, by finding out the track points of loss, by open country value, fills, can guarantee the complete of track points data, after value out of office is corrected, can obtain track points data more accurately.

In order to further illustrate the modification method based on ADS-B system track points of the present invention, set up an one dimension realistic model below:

First, at MATLAB2008 (matrix laboratory, matrix experiment chamber), set up airplane motion equation, then calculate the track points under a dimension coordinate, and those track points are carried out to random noise and missing at random processing, form the one dimension aircraft track of simulation; Particularly, refer to Fig. 2, for the present invention is based on the one dimension realistic model figure of the modification method of ADS-B system track points, the flight path of simulated aircraft is as follows:

If the initial position of aircraft is 10000, [1,20] second does speed is 600m/s uniform motion, does the retarded motion that acceleration is-2g [20,40] second, and do the accelerated motion that acceleration is g (natural acceleration) [40,80] second, and [80,100] second move with uniform velocity.In [10,30], 20% track points adds 200 times of random noises, causes at random 20% flight path disappearance in [30,100] track points simultaneously, and the point of flight path disappearance is filled (in example, with 0, filling) by greatly wild value.

Then, in MATLAB2008, utilize the modification method based on ADS-B system track points of the present invention to revise the flight path of above-mentioned simulated aircraft, obtain simulation result; Refer to Fig. 3, for the present invention is based on the one dimension simulation modification result figure of the modification method of ADS-B system track points; Simulation result has shown that the modification method that the present invention is based on ADS-B system track points can effectively identify the open country value in track points, realizes the open country value in track points is carried out to dynamic correction in real time, the accuracy of raising track points data.

According to above-mentioned a kind of modification method based on ADS-B system track points, the present invention also provides a kind of update the system based on ADS-B system track points, refers to Fig. 4, for the present invention is based on the update the system structural drawing of ADS-B system track points:

A update the system based on ADS-B system track points, comprises pretreatment unit 10, calculates amending unit 20;

10 pairs of track points of described pretreatment unit are arranged by time of reception;

Described calculating amending unit 20 is according to the status predication value of the status information prediction current time track points of track points and the status predication covariance matrix that calculates current time track points; According to the measurement predicted value of the status information prediction current time track points of track points and the residual vector covariance matrix that calculates current time track points; Whether whether identification current time track points is wild value, and be the decay factor that wild value is determined current time track points according to current time track points; According to the decay factor of current time track points, calculate current time filter gain; According to the residual vector covariance matrix of the measurement predicted value of current time track points, current track points and the status predication value of current time filter gain correction current time track points and the status predication covariance matrix of current track points.

Particularly, describedly by time of reception, arrange track points and refer to the track points receiving is converted into rectangular coordinate system by earth coordinates, then according to the time receiving, be that sequencing is arranged by the data of track points.

Particularly, described calculating amending unit 20 calculates by following equation:

$\hat{X}(k/k-1)=\mathrm{\&Phi;}(k/k-1)\hat{X}(k-1/k-1)+U(k-1)\stackrel{\&OverBar;}{a}(k-1)$

P(k/k-1)＝Φ(k-1)P(k-1/k-1)Φ ^T(k-1)+Q(k-1)

$\hat{Y}(k/k-1)=H\left(k\right)\hat{X}(k/k-1)$

S(k)＝H(k)P(k/k-1)H ^T(k)+R(k)

$\tilde{Y}\left(k\right)=|Y\left(k\right)-\hat{Y}(k/k-1)|\&le;d\sqrt{S\left(k\right)}$

$r\left(k\right)=\left\{\begin{array}{ccc}1& \left|\tilde{Y}\left(k\right)\right|\&le;& d\sqrt{S\left(k\right)}\\ {\left\{\frac{3\sqrt{S\left(k\right)}}{\left|\tilde{Y}\left(k\right)\right|}\right\}}^{1/2}& \left|\tilde{Y}\left(k\right)\right|>& d\sqrt{S\left(k\right)}\end{array}\right.$

K(k)＝r(k)P(k)H ^T(k)S ^-1(k)

$\hat{X}(k/k)=\hat{X}(k/k-1)+K\left(k\right)[Y\left(k\right)-\hat{Y}(k/k-1)]$

P(k/k)＝P(k/k-1)-K(k)S(k)K ^T(k)

Wherein, for the status predication value of current time track points, Φ (k-1) is previous moment state-transition matrix, and U (k-1) is previous moment gating matrix, for previous moment Maneuver Acceleration average, status predication value for previous moment track points; P (k/k-1) is the status predication covariance matrix of current time track points, and Q (k-1) is noise variance matrix, and P (k-1/k-1) is the status predication covariance matrix of previous moment track points, Φ ^t(k-1) be the transposed matrix of previous moment state-transition matrix; for the measurement predicted value of current time track points, H (k) is current time measurement matrix; S (k) is the residual vector covariance matrix of current time track points, R (k) current time observation noise, H ^t(k) for current time is measured transpose of a matrix matrix; for the new breath of current time track points, Y (k) is the measured value of current time track points, for the measurement predicted value of current time track points, it is the decay factor of current time track points that d is set to 3, r (k); K (k) is current time filter gain, S ^-1(k) be the inverse matrix of the residual vector covariance matrix of current time track points; for the status predication value of revised current time track points, the status predication covariance matrix that P (k/k) is revised current track points, K ^t(k) be current time filter gain transposed matrix.

Further:

Previous moment state-transition matrix $\mathrm{\&Phi;}(k-1)=\left[\begin{array}{ccc}1& T& \frac{1}{a^2}(-1+\mathrm{aT}+e^{-\mathrm{aT}})\\ 0& 1& \frac{1}{a}(1-e^{\mathrm{aT}})\\ 0& 0& e^{-\mathrm{aT}}\end{array}\right]$

α is maneuvering frequency, and maneuvering frequency α=1/20 is escaped, atmospheric disturbance maneuvering frequency α=1 in turning machine dynamic frequency α=1/60; T is the sampling time; E is Euler's numbers;

Previous moment gating matrix $U(k-1)=\left[\begin{array}{c}\frac{1}{a}(-T+\frac{{\mathrm{aT}}^2}{2}+\frac{1-e^{-\mathrm{aT}}}{a})\\ T-\frac{1-e^{-\mathrm{aT}}}{a}\\ 1-e^{-\mathrm{aT}}\end{array}\right]$

Current time noise variance matrix

In formula

$q_{11}=\frac{1}{{2a}^5}[1-e^{-2\mathrm{aT}}+2\mathrm{aT}+\frac{{2a}^3{T}^3}{3}-{2a}^2{T}^2-{4\mathrm{aTe}}^{-\mathrm{aT}}]$

$q_{12}=\frac{1}{{2a}^4}[e^{-2\mathrm{aT}}+1-{2e}^{-\mathrm{aT}}+{2\mathrm{aTe}}^{-\mathrm{aT}}-2\mathrm{aT}+a^2{T}^2]$

$q_{13}=\frac{1}{{2a}^3}[1-e^{-2\mathrm{aT}}-{2\mathrm{aTe}}^{-\mathrm{aT}}]$

$q_{22}=\frac{1}{{2a}^3}[{4e}^{-\mathrm{aT}}-3-e^{-2\mathrm{aT}}+2\mathrm{aT}]$

$q_{23}=\frac{1}{{2a}^2}[e^{-2\mathrm{aT}}+1-{2e}^{-\mathrm{aT}}]$

$q_{33}=\frac{1}{2a}[1-e^{-2\mathrm{aT}}]$

${\mathrm{\&sigma;}}_a^2(k-1)=\left\{\begin{array}{cc}\frac{4-\mathrm{\&pi;}}{\mathrm{\&pi;}}{[a_{\max }-\stackrel{\&OverBar;}{a}(k-1)]}^2& \stackrel{\&OverBar;}{a}(k-1)\&GreaterEqual;0\\ \frac{4-\mathrm{\&pi;}}{\mathrm{\&pi;}}{[a_{-\max }+\stackrel{\&OverBar;}{a}(k-1)]}^2& \stackrel{\&OverBar;}{a}(k-1)<0\end{array}\right.$

$\stackrel{\&OverBar;}{a}(k-1)=\widehat{\stackrel{\&CenterDot;\&CenterDot;}{x}}(k-1|k-2$

The value that it should be pointed out that d also can be arranged to the natural number that other are greater than zero; In the present invention program, it is whether an identification current time track points is that open country is worth preferably embodiment that d is set to 3.

More preferably, in one embodiment, the value of current time observation noise R (k) can be 30, and current time is measured matrix H (k)=[100].

By adjusting the size (being r (k)) of the filter attenuation factor, change filter gain, realized wild value adaptive correction.By aforesaid equation, can be obtained, the correction of wild value is adjusted realization by decay factor r (k) to gain, and setting range is [0,1].Be that wild value and prediction depart from greatlyr, restriction is also larger, and when very large, gain is almost close to zero, and when in normal value, the value of gain is 1.

Can see, the update the system based on ADS-B system track points of the present invention, can effectively identify the open country value in track points, realizes the open country value in track points is carried out to dynamic correction in real time, improves the accuracy of track points data.

More preferably, in one embodiment, the update the system based on ADS-B system track points of the present invention, also comprises filler cells 30;

Described filler cells compares the sampling time interval of the time interval of every two track points and wave filter; If the time interval is inconsistent, judges between these two track points and exist track points to lose; By open country value, the track points of losing is filled.

Particularly, described filler cells can numerical value be 0 open country value is filled to the track points of losing, by finding out the track points of loss, by open country value, fills, the complete of track points data can be guaranteed, after value out of office is corrected, track points data more accurately can be obtained.

The above embodiment has only expressed several embodiment of the present invention, and it describes comparatively concrete and detailed, but can not therefore be interpreted as the restriction to the scope of the claims of the present invention.It should be pointed out that for the person of ordinary skill of the art, without departing from the inventive concept of the premise, can also make some distortion and improvement, these all belong to protection scope of the present invention.Therefore, the protection domain of patent of the present invention should be as the criterion with claims.

Claims (10)

1. the modification method based on ADS-B system track points, is characterized in that, comprises step:

Track points is arranged by time of reception;

According to the status predication value of the status information prediction current time track points of track points and the status predication covariance matrix that calculates current time track points;

According to the measurement predicted value of the status information prediction current time track points of track points and the residual vector covariance matrix that calculates current time track points;

Whether whether identification current time track points is wild value, and be the decay factor that wild value is determined current time track points according to current time track points;

According to the decay factor of current time track points, calculate current time filter gain;

According to the residual vector covariance matrix of the measurement predicted value of current time track points, current track points and the status predication value of current time filter gain correction current time track points and the status predication covariance matrix of current track points.

2. the modification method based on ADS-B system track points according to claim 1, it is characterized in that, the status predication value of the described status information according to track points prediction current time track points and the status predication covariance matrix that calculates current track points calculate by following equation:

P(k/k-1)＝Φ(k-1)P(k-1/k-1)Φ ^T(k-1)+Q(k-1)

Wherein, for the status predication value of current time track points, P (k/k-1) is the status predication covariance matrix of current time track points, for the status predication value of previous moment track points, P (k-1/k-1) is the status predication covariance matrix of previous moment track points;

Described track points status information comprises previous moment state-transition matrix Φ (k-1), previous moment gating matrix U (k-1), previous moment Maneuver Acceleration average the status predication covariance matrix P (k/k-1) of previous moment track points, the transposed matrix Φ of previous moment state-transition matrix ^t(k-1), previous moment noise variance matrix Q (k-1).

3. the modification method based on ADS-B system track points according to claim 2, it is characterized in that, the measurement predicted value of the described prediction of the status information according to track points current time track points and the residual vector covariance matrix of calculating current time track points calculate by following equation:

$\hat{Y}(k/k-1)=H\left(k\right)\hat{X}(k/k-1)$

S(k)＝H(k)P(k/k-1)H ^T(k)+R(k)

Wherein, for the measurement predicted value of current time track points, S (k) is the residual vector covariance matrix of current time track points;

Described track points status information also comprises that current time is measured matrix H (k), current time is measured transpose of a matrix matrix H ^t(k), current time observation noise R (k).

4. the modification method based on ADS-B system track points according to claim 3, it is characterized in that, whether whether described identification current time track points is wild value, and be that wild value determines that the decay factor of current time track points calculates by following equation according to current time track points:

$\tilde{Y}\left(k\right)=|Y\left(k\right)-\hat{Y}(k/k-1)|\&le;d\sqrt{S\left(k\right)}$

$r\left(k\right)=\left\{\begin{array}{ccc}1& \left|\tilde{Y}\left(k\right)\right|\&le;& d\sqrt{S\left(k\right)}\\ {\left\{\frac{3\sqrt{S\left(k\right)}}{\left|\tilde{Y}\left(k\right)\right|}\right\}}^{1/2}& \left|\tilde{Y}\left(k\right)\right|>& d\sqrt{S\left(k\right)}\end{array}\right.$

Wherein, for the new breath of current time track points, when time, current time track points is not wild value, when time, current time track points is wild value; Y (k) is the measured value of current time track points, for the measurement predicted value of current time track points, it is the decay factor of current time track points that d is set to 3, r (k).

5. the modification method based on ADS-B system track points according to claim 4, is characterized in that, the described decay factor according to current time track points is calculated current time filter gain and calculated by following equation:

K(k)＝r(k)P(k)H ^T(k)S ^-1(k)

Wherein, K (k) is current time filter gain, S ^-1(k) be the inverse matrix of the residual vector covariance matrix of current time track points.

6. the modification method based on ADS-B system track points according to claim 5, it is characterized in that, describedly according to the residual vector covariance matrix of the measurement predicted value of current time track points, current track points and the status predication value of current time filter gain correction current time track points and the status predication covariance matrix of current track points, by following equation, calculate:

$\hat{X}(k/k)=\hat{X}(k/k-1)+K\left(k\right)[Y\left(k\right)-\hat{Y}(k/k-1)]$

P(k/k)＝P(k/k-1)-K(k)S(k)K ^T(k)

Wherein, for the status predication value of revised current time track points, the status predication covariance matrix that P (k/k) is revised current track points, K ^t(k) be current time filter gain transposed matrix.

7. according to the modification method based on ADS-B system track points described in claim 1 to 6 any one, it is characterized in that, after track points is arranged by time of reception, before the status predication covariance matrix of the status predication value of prediction current time track points and calculating current time track points, also comprise step:

The sampling time interval of the time interval of every two track points and wave filter is compared;

If the time interval is inconsistent, judges between these two track points and exist track points to lose;

By open country value, the track points of losing is filled.

8. the update the system based on ADS-B system track points, is characterized in that, comprises pretreatment unit, calculates amending unit;

Described pretreatment unit is arranged by time of reception track points;

Described calculating amending unit is according to the status predication value of the status information prediction current time track points of track points and the status predication covariance matrix that calculates current time track points; According to the measurement predicted value of the status information prediction current time track points of track points and the residual vector covariance matrix that calculates current time track points; Whether whether identification current time track points is wild value, and be the decay factor that wild value is determined current time track points according to current time track points; According to the decay factor of current time track points, calculate current time filter gain; According to the residual vector covariance matrix of the measurement predicted value of current time track points, current track points and the status predication value of current time filter gain correction current time track points and the status predication covariance matrix of current track points.

9. the update the system based on ADS-B system track points according to claim 8, is characterized in that, described calculating amending unit calculates by following equation:

$\hat{X}(k/k-1)=\mathrm{\&Phi;}(k/k-1)\hat{X}(k-1/k-1)+U(k-1)\stackrel{\&OverBar;}{a}(k-1)$

P(k/k-1)＝Φ(k-1)P(k-1/k-1)Φ ^T(k-1)+Q(k-1)

$\hat{Y}(k/k-1)=H\left(k\right)\hat{X}(k/k-1)$

S(k)＝H(k)P(k/k-1)H ^T(k)+R(k)

$\tilde{Y}\left(k\right)=|Y\left(k\right)-\hat{Y}(k/k-1)|\&le;d\sqrt{S\left(k\right)}$

$r\left(k\right)=\left\{\begin{array}{ccc}1& \left|\tilde{Y}\left(k\right)\right|\&le;& d\sqrt{S\left(k\right)}\\ {\left\{\frac{3\sqrt{S\left(k\right)}}{\left|\tilde{Y}\left(k\right)\right|}\right\}}^{1/2}& \left|\tilde{Y}\left(k\right)\right|>& d\sqrt{S\left(k\right)}\end{array}\right.$

K(k)＝r(k)P(k)H ^T(k)S ^-1(k)

$\hat{X}(k/k)=\hat{X}(k/k-1)+K\left(k\right)[Y\left(k\right)-\hat{Y}(k/k-1)]$

P(k/k)＝P(k/k-1)-K(k)S(k)K ^T(k)

Wherein, for the status predication value of current time track points, Φ (k-1) is previous moment state-transition matrix, and U (k-1) is previous moment gating matrix, for previous moment Maneuver Acceleration average, status predication value for previous moment track points; P (k/k-1) is the status predication covariance matrix of current time track points, and Q (k-1) is noise variance matrix, and P (k-1/k-1) is the status predication covariance matrix of previous moment track points, Φ ^t(k-1) be the transposed matrix of previous moment state-transition matrix; for the measurement predicted value of current time track points, H (k) is current time measurement matrix; S (k) is the residual vector covariance matrix of current time track points, R (k) current time observation noise, H ^t(k) for current time is measured transpose of a matrix matrix; for the new breath of current time track points, Y (k) is the measured value of current time track points, for the measurement predicted value of current time track points, it is the decay factor of current time track points that d is set to 3, r (k); K (k) is current time filter gain, S ^-1(k) be the inverse matrix of the residual vector covariance matrix of current time track points; for the status predication value of revised current time track points, the status predication covariance matrix that P (k/k) is revised current track points, K ^t(k) be current time filter gain transposed matrix.

10. the update the system based on ADS-B system track points according to claim 8, is characterized in that, also comprises filler cells;

Described filler cells compares the sampling time interval of the time interval of every two track points and wave filter; If the time interval is inconsistent, judges between these two track points and exist track points to lose; By open country value, the track points of losing is filled.