CN101858748A - Fault-tolerance autonomous navigation method of multi-sensor of high-altitude long-endurance unmanned plane - Google Patents

Abstract

The invention discloses a fault-tolerance autonomous navigation method of a multi-sensor of a high-altitude long-endurance unmanned plane. The method comprises the following steps of: firstly, carrying out theory analysis on work environment and work characteristics of three navigation sensors including GPS (Global Position System), CNS (astronomy) and SAR (Synthetic Aperture Radar), and establishing an observation linearity measurement equation by combining a geography system based on a position combined observation principle of inertia/GPS, inertia/astronomy and inertia/SAR under an airborne geography system; then analyzing error characteristics of a navigation sensor and simulating the output during the fault of GPS, and establishing a corresponding fault detection algorithm unit to carry out fault detection and isolation on a filter; and finally, designing and completing an inertia/GPS combined navigation system mathematic model based on the assistance of astronomy and SAR, and optimally evaluating the error state of the inertia navigation by means of federated filtering. The invention has high navigation precision, and can fully play the role of evaluating the error state quantity of an airborne inertia navigation system by the combined navigation of the multi-sensor under the geography system.

Description

The fault-tolerance autonomous navigation method of multi-sensor of high-altitude long-endurance unmanned plane

Technical field

Invention relates to a kind of fault-tolerance autonomous navigation method of multi-sensor that is used for high-altitude long-endurance unmanned plane, belong to aviation aircraft integrated navigation technical field, can be applicable to determining of the long-time aviation aircraft navigational parameter that flies in high-altitude, be applicable to the navigator fix of the aviation aircraft of the long-time flight in high-altitude.

Background technology

Star sensor more and more widely is applied to the independent navigation field as a kind of high-precision astronomical attitude sensor.Star sensor can be under the prerequisite of outside reference information, and directly accurate the measurement obtains aircraft with respect to the attitude information under the inertial coordinates system, and its measuring accuracy is stable in the omnidistance maintenance of navigation, and existing full accuracy can reach the rad level.Inertia/celestial combined navigation system can all weather operations with it, have that extremely strong independence and navigation error are not dispersed in time and on high-altitude, long boat aircraft, obtained very big attention and development, but under the prior art condition, because the celestial navigation location is subjected to the influence of horizontal attitude benchmark, therefore, inertia/celestial combined navigation precision also can't reach inertia/GPS integrated navigation bearing accuracy.

Synthetic aperture radar (SAR) is a kind of high-resolution imaging radar that World War II grows up later on, utilize it can be round-the-clock, round-the-clock, obtain similar photo-optical high-resolution radar image at a distance.With REAL TIME SAR IMAGES carry out images match location and and inertial navigation constitute the INS/SAR integrated navigation system and be developed in recent years and extensively attention.But, require carrier aircraft to be in comparison smooth flight state during as the SAR imaging because SAR imaging navigational system has its specific (special) requirements under the carrier aircraft applied environment; Need consume the coupling computing time of not waiting when carrying out the SAR images match; In addition, owing to be subjected to the restriction of airborne digital map database capacity, the SAR image-guidance only can carry out work in the part period, is used for terminal guidance at present more.

Inertia/GPS integrated navigation system has wide coverage, round-the-clock, round-the-clock, high precision and is close to continuously characteristics such as real-time, and subscriber equipment has that volume is little, in light weight, power consumption is little and advantage such as easy operating.Therefore inertia/GPS integrated navigation system is a kind of more satisfactory navigational system, and is at present most widely used general.But gps signal is subject to electromagnetic interference (EMI), thereby causes the INS/GPS bearing accuracy to descend; And GPS is under the jurisdiction of the U.S., in case the war situation occurs, gps signal will be lost fully, and have only INS to play a role in the INS/GPS integrated navigation system this moment, and error can constantly accumulate, and influences the navigation accuracy of aircraft.

Therefore, there is not property quietly of the not high and navigation accuracy of reliability in the autonomous navigation method of existing airborne inertia/GPS combination, can not fully satisfy the requirement of high-altitude long-endurance unmanned plane to navigation accuracy stability.

Summary of the invention

The object of the invention is: overcome the deficiency of the navigation stability of airborne inertia/GPS integrated navigation system, providing a kind of is the fault-tolerance autonomous navigation method of multi-sensor of high-altitude long-endurance unmanned plane based on inertia/GPS fault-tolerance autonomous navigation method of multi-sensor astronomical, that SAR is auxiliary.

The present invention adopts following technical scheme for achieving the above object:

The present invention is based on astronomy, the auxiliary inertia/GPS fault-tolerance autonomous navigation method of multi-sensor of SAR, may further comprise the steps:

(1) by setting up the error state amount equation of airborne inertial navigation system INS, obtained the mathematical description to airborne INS errors quantity of state, airborne INS errors quantity of state x is defined as:

φ _E, φ _N, φ _URepresent respectively in the airborne INS errors quantity of state east orientation platform error angle quantity of state, north orientation platform error angle quantity of state and day to platform error angle quantity of state; δ v _E, δ v _N, δ v _URepresent respectively in the airborne INS errors quantity of state east orientation velocity error quantity of state, north orientation velocity error quantity of state and day to the velocity error quantity of state; δ L, δ λ, δ h represent latitude error quantity of state, longitude error quantity of state and the height error quantity of state in the airborne INS errors quantity of state respectively; ε _Bx, ε _By, ε _Bz, ε _Rx, ε _Ry, ε _RzRepresent X-axis, Y-axis, Z-direction gyroscope constant value drift error state amount and X-axis, Y-axis, Z-direction gyro single order markov drift error quantity of state in the airborne INS errors quantity of state respectively;

Represent X-axis, Y-axis and Z-direction accelerometer bias in the airborne INS errors quantity of state respectively, T is a transposition;

(2) adopt airborne Department of Geography upper/lower positions linearization observation principle, set up the linearization measurement equation between the longitude and latitude high level error quantity of state in upper/lower positions observed quantity of airborne Department of Geography and the described airborne INS errors of estimative step (1) quantity of state, comprise the GPS/INS measurement equation, CNS/ pressure altimeter/INS measurement equation and SAR/INS measurement equation;

(3) subsystem adopts residual error χ ²Method of inspection carries out fault detect and isolation;

(4) the described latitude of step (2), longitude and height error quantity of state are carried out Kalman filtering, antithetical phrase systematic perspective measurement simultaneously and quantity of state carry out fault detect, and when the subsystem non-fault, the result sends into federal wave filter with the subsystem Kalman filtering; When subsystem had fault, subsystem will be isolated, and Kalman filtering result can not send into federal wave filter, and other non-fault subsystems constitute new federal filtering system.

(5) federal wave filter data that subsystem is sent here are carried out data fusion, output global optimum estimated value, thus the navigation error of airborne inertial navigation system is revised.

The present invention has compared with prior art overcome the deficiency of the navigation stability of airborne inertia/GPS integrated navigation system, made up a kind of fault-tolerance autonomous navigation method of multi-sensor that is applicable to the HAE unmanned plane, it has the following advantages: (1) each local filter is independent of each other, and just the subsystem to oneself detects.(2) when GPS information is interfered, fault detection unit can in time detect fault, thereby the GPS subsystem is kept apart federal filtering system; (3) after fault sensor is isolated, other local filter still can continue operate as normal, and remaining subsystem reconstitutes new federal filtering system, does not influence the work of senior filter, is convenient to system reconstructing.

Description of drawings

Fig. 1 is the process flow diagram of a kind of examples of implementation of fault-tolerance autonomous navigation method of multi-sensor of the present invention;

Fig. 2 is a flight track of emulation;

Fig. 3 is the emulation comparison diagram of the longitude error before and after adding fault detect of the present invention and the isolation (FDI);

Fig. 4 is adding fault detect of the present invention and the emulation comparison diagram of isolating the latitude error of front and back;

Fig. 5 is adding fault detect of the present invention and the emulation comparison diagram of isolating the longitude error variance of front and back;

Fig. 6 is adding fault detect of the present invention and the emulation comparison diagram of isolating the latitude error variance of front and back;

Fig. 7 is the emulation comparison diagram of the longitude error before and after the terminal SAR of adding of the present invention;

Fig. 8 is the emulation comparison diagram of the latitude error before and after the terminal SAR of adding of the present invention;

Fig. 9 is the emulation comparison diagram of the longitude error variance before and after the terminal SAR of adding of the present invention;

Figure 10 is the emulation comparison diagram of the latitude error variance before and after the terminal SAR of adding of the present invention.

Embodiment

Be elaborated below in conjunction with the technical scheme of accompanying drawing to invention:

As shown in Figure 1, principle of the present invention is: start with from the angle of airborne Department of Geography navigation, set up the position linearity measurement equation under the Department of Geography, comprise the GPS/INS measurement equation, CNS/ pressure altimeter/INS measurement equation and SAR/INS measurement equation.Specific implementation method is as follows:

One, sets up the error state amount equation of airborne inertial navigation system

Selecting navigation coordinate is the geographical horizontal coordinates (O in sky, northeast _nX _nY _nZ _n), adopt linear kalman filter to make up, the state equation of system is the error state amount equation of inertial navigation system, by to the performance of inertial navigation system and the analysis of error source, the error state amount equation that can obtain inertial navigation system is:

$\stackrel{\·}{X}\left(t\right)=F\left(t\right)X\left(t\right)+G\left(t\right)W\left(w\right)---\left(8\right)$

In the formula

φ wherein _E, φ _N, φ _UBe the platform error angle; δ v _E, δ v _N, δ v _UBe velocity error; δ L, δ λ, δ h are latitude, longitude and height error; ε _Bx, ε _By, ε _Bz, ε _Rx, ε _Ry, ε _RzBe respectively gyroscope constant value drift sum of errors single order markov drift error;

Be accelerometer bias.

Two, set up the linearization measurement equation of airborne Department of Geography upper/lower positions observed quantity

1.GPS/INS measurement equation

${\mathrm{<figure-callout\; id="1"\; label="Z"\; filenames="HSA00000136860800012.png"\; state="\{\{state\}\}">Z</figure-callout>}}_1=\left[\begin{array}{c}(L_I-L_G)R_M\\ ({\mathrm{\&lambda;}}_I-{\mathrm{\&lambda;}}_G)R_N\cos L\\ h_I-h_G\\ {\mathrm{\&upsi;}}_{\mathrm{IE}}-{\mathrm{\&upsi;}}_{\mathrm{GE}}\\ {\mathrm{\&upsi;}}_{\mathrm{IN}}-{\mathrm{\&upsi;}}_{\mathrm{GN}}\\ {\mathrm{\&upsi;}}_{\mathrm{IU}}-{\mathrm{\&upsi;}}_{\mathrm{GU}}\end{array}\right]=\left[\begin{array}{c}{R}_M\mathrm{\&delta;L}\\ R_N\cos \mathrm{L\&delta;\&lambda;}\\ \mathrm{\&delta;h}\\ {\mathrm{\&delta;\&upsi;}}_E\\ {\mathrm{\&delta;\&upsi;}}_N\\ {\mathrm{\&delta;\&upsi;}}_U\end{array}\right]+\left[\begin{array}{c}{v}_1\\ v_2\\ v_3\\ v_4\\ v_5\\ v_6\end{array}\right]=\left[\begin{array}{ccc}{0}_{3\&times;6}& \mathrm{diag}\left[\begin{array}{ccc}{R}_M& R_{\mathrm{<figure-callout\; id="1"\; label="N\; cos\; L"\; filenames="HSA00000136860800012.png"\; state="\{\{state\}\}">N</figure-callout>}}\cos L& 1\end{array}\right]& 0_{3\&times;9}\\ 0_{3\&times;3}& \mathrm{<figure-callout\; id="1"\; label="diag"\; filenames="HSA00000136860800012.png"\; state="\{\{state\}\}">diag</figure-callout>}\left[\begin{array}{ccc}1& 1& 1\end{array}\right]& 0_{3\&times;12}\end{array}\right]X+\left[\begin{array}{c}{v}_1\\ v_2\\ v_3\\ v_4\\ v_5\\ v_6\end{array}\right]---\left(9\right)$

In the formula (1), v ₁, v ₂, v ₃, v ₄, v ₅, v ₆Be respectively north orientation, east orientation, short transverse site error and east orientation, the north orientation of GPS output, day to velocity error, all be thought of as white noise.

2.CNS/ pressure altimeter/INS measurement equation

${\mathrm{<figure-callout\; id="1"\; label="Z"\; filenames="HSA00000136860800012.png"\; state="\{\{state\}\}">Z</figure-callout>}}_1=\left[\begin{array}{c}(L_I-L_B)R_M\\ ({\mathrm{\&lambda;}}_I-{\mathrm{\&lambda;}}_B)R_N\cos L\\ h_I-h_B\end{array}\right]=\begin{array}{c}\left[\begin{array}{c}{R}_M\mathrm{\&delta;L}\\ R_N\cos \mathrm{L\&delta;\&lambda;}\\ \mathrm{\&delta;h}\end{array}\right]+\left[\begin{array}{c}{v}_1\\ v_2\\ v_3\end{array}\right]=\left[\begin{array}{ccc}{0}_{3\&times;6}& \mathrm{diag}\left[\begin{array}{ccc}{R}_M& R_{\mathrm{<figure-callout\; id="1"\; label="N\; cos\; L"\; filenames="HSA00000136860800012.png"\; state="\{\{state\}\}">N</figure-callout>}}\cos L& 1\end{array}\right]& 0_{3\&times;9}\end{array}\right]X+\left[\begin{array}{c}{v}_7\\ v_8\\ v_9\end{array}\right]---\left(10\right)\end{array}$

V in the formula (2) ₇, v ₈, v ₉Be respectively north orientation, east orientation, the short transverse site error of the output of CNS and pressure altimeter, all be thought of as white noise.

3.SAR/INS measurement equation

V in the formula (3) ₁₀Course heading error when exporting for the SAR images match, v ₁₁Be north orientation site error, v ₁₂Be the east orientation site error, its size depends on the precision of the images match location algorithm that is adopted.Error all is thought of as white noise.

Three, fault detect and isolated algorithm judge whether navigation sensor has fault

4. the realization of fault detection algorithm

In the fault-tolerance combined navigation system, must determine the validity of the measurement information of each subsystem filter process in real time, this just requires in the design of subfilter, should be equipped with real-time fault detect and isolated algorithm, the characteristics (this system adopts closed-loop corrected) of the integrated navigation system that proposes at this paper, subfilter adopts residual error χ ²Method of inspection carries out fault detect and isolation.

Calculate fault detect function lambda (k):

$\left.\begin{array}{c}{\mathrm{\&gamma;}}_i\left(k\right)=Z_i\left(k\right)-H_i\left(k\right){\hat{X}}_i(k/k-1)\\ U_i\left(k\right)=H_i\left(k\right)P_i(k/k-1)H_i^T\left(k\right)+R_i\left(k\right)\\ {\mathrm{\&lambda;}}_i\left(k\right)={\mathrm{\&gamma;}}_i^T\left(k\right)U_i^{-1}\left(k\right){\mathrm{\&gamma;}}_i\left(k\right)\end{array}\right\}---\left(12\right)$

In the formula: H _iIt is the observed differential matrix of i subsystem;

P _i(k/k-1) be the optimum prediction estimated value and the optimum prediction estimated value error covariance matrix of i subsystem; R _i(k) be the observation noise variance matrix of i subsystem.

It is the χ of m that λ (k) function is obeyed degree of freedom ²Distribute, m is for measuring the dimension of matrix Z.The criterion of fault judgement is

In the formula, T _DBe predefined thresholding, by early warning rate P _FaPre-determine.If judge the subsystem non-fault, then its filter value is delivered to senior filter; If detecting subsystem has fault, then it is isolated, and lost efficacy unlikely the barrier for some reason of total system by system reconfiguration, after detecting subsystem recovery normally, again its filter value is sent into federal wave filter again.

Four, carry out KF (Kalman Filter) filtering, estimate the error state amount of airborne inertial navigation system

5. the discretize of state equation and measurement equation and Kalman filter

When adopting linear kalman filter, need carry out discretize to system state equation (6) and measurement equation (7), (8), (9) of top conitnuous forms, thereby obtain the system equation of discrete form.Its discrete form is as follows:

$\left\{\begin{array}{c}{X}_k={\mathrm{\&Phi;}}_{k,k-1}{X}_{k-1}+{\mathrm{\&Gamma;}}_{k-1}{W}_{k-1}\\ Z_k=H_k{X}_k+V_k\end{array}\right.$

In the formula

T is an iteration cycle.

Thereby it is as follows to obtain system linearity Kalman filter equation:

${\hat{X}}_{k|k-1}={\mathrm{\&Phi;}}_{k,k-1}{\hat{X}}_{k-1}$

${\hat{X}}_k={\hat{X}}_{k|k-1}+K_k[Z_k-H_k{\hat{X}}_{k|k-1}]$

P _k|k-1＝Φ _k，k-1P _k-1Φ _k，k-1 ^T+Γ _k-1Q _k-1Γ _k-1 ^T

$K_k=P_{k|k-1}{H}_k^T{(H_k{P}_{k|k-1}{H}_k^T+R_k)}^{-1}$

$P_k=(I-K_k{H}_k)P_{k|k-1}{(I-K_k{H}_k)}^T+K_k{R}_k{K}_k^T$

Q in the following formula, R are respectively the noise variance matrix of system and measure variance matrix.

6. federal senior filter information fusion equation and blending algorithm

$P_g={\left(\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{P}_i^{-1}(k/k)\right)}^{-1}$

${\hat{X}}_g=P_g\left(\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{P}_i^{-1}(k/k){\hat{X}}_i(k/k)\right)$

N=1 in the formula, 2,3 are the number of this subfilter that works constantly, are determined by fault detection algorithm.

The simulation result of Fig. 2～Fig. 6 shows that when system did not add fault detection unit (FDI), the resume speed of system was slow, and bearing accuracy is subjected to the GPS fault effects bigger, the actual change of the tracking error that the systematic error covariance matrix can not be correct; After adding FDI, the resume speed of system accelerates, and bearing accuracy is determined that by INS/CNS bearing accuracy improves greatly, and the systematic error covariance matrix is the correct actual change of tracking error also.

The simulation result of Fig. 7～Figure 10 shows, after system inserted the SAR backup system endways, the longitude of system and latitude error were little a lot of when not inserting SAR, and terminal bearing accuracy improves greatly, and error covariance matrix is the correct actual change of tracking error also.The bearing accuracy and the stability of system are significantly improved.

Claims (3)

1. the fault-tolerance autonomous navigation method of multi-sensor of a high-altitude long-endurance unmanned plane is characterized in that may further comprise the steps:

(1) by setting up the error state amount equation of airborne inertial navigation system INS, obtain the mathematical description to airborne INS errors quantity of state, airborne inertial navigation system INS error state amount X is:

φ _E, φ _N, φ _URepresent respectively in the airborne INS errors quantity of state east orientation platform error angle quantity of state, north orientation platform error angle quantity of state and day to platform error angle quantity of state; δ v _E, δ v _N, δ v _URepresent respectively in the airborne INS errors quantity of state east orientation velocity error quantity of state, north orientation velocity error quantity of state and day to the velocity error quantity of state; δ L, δ λ, δ h represent latitude error quantity of state, longitude error quantity of state and the height error quantity of state in the airborne INS errors quantity of state respectively; ε _Bx, ε _By, ε _Bz, ε _Rx, ε _Ry, ε _RzRepresent X-axis, Y-axis, Z-direction gyroscope constant value drift error state amount and X-axis, Y-axis, Z-direction gyro single order markov drift error quantity of state in the airborne INS errors quantity of state respectively;

Represent X-axis, Y-axis and Z-direction accelerometer bias in the airborne INS errors quantity of state respectively, T is a transposition;

(2) adopt airborne Department of Geography upper/lower positions linearization observation procedure, set up the linearization measurement equation between latitude error quantity of state, longitude error quantity of state and the height error quantity of state in upper/lower positions observed quantity of airborne Department of Geography and the described airborne INS errors of estimative step (1) quantity of state, comprise the GPS/INS measurement equation, CNS/ pressure altimeter/INS measurement equation and SAR/INS measurement equation;

(3) subsystem adopts residual error χ ²Method of inspection carries out fault detect and isolation;

(4) the described latitude of step (2), longitude and height error quantity of state are carried out Kalman filtering, measurement of (3) antithetical phrase systematic perspective and quantity of state carry out fault detect set by step simultaneously, when the subsystem non-fault, the result sends into federal wave filter with the subsystem Kalman filtering; When subsystem had fault, subsystem will be isolated, and Kalman filtering result can not send into federal wave filter, and other non-fault subsystems constitute new federal filtering system;

(5) federal wave filter data that subsystem is sent here are carried out data fusion, output global optimum estimated value, thus the navigation error of airborne inertial navigation system is revised.

2. the fault-tolerance autonomous navigation method of multi-sensor of high-altitude long-endurance unmanned plane according to claim 1, it is characterized in that: the linearization measurement equation between longitude, latitude and the height error quantity of state in airborne Department of Geography upper/lower positions observed quantity described in the step (2) and the described airborne INS errors of estimative step (1) quantity of state is as follows:

1) GPS/INS measurement equation

$Z_1=\left[\begin{array}{c}(L_I-L_G)R_M\\ ({\mathrm{\&lambda;}}_I-{\mathrm{\&lambda;}}_G)R_N\cos L\\ h_I-h_G\\ {\mathrm{\&upsi;}}_{\mathrm{IE}}-{\mathrm{\&upsi;}}_{\mathrm{GE}}\\ {\mathrm{\&upsi;}}_{\mathrm{IN}}-{\mathrm{\&upsi;}}_{\mathrm{GN}}\\ {\mathrm{\&upsi;}}_{\mathrm{IU}}-{\mathrm{\&upsi;}}_{\mathrm{GU}}\end{array}\right]=\left[\begin{array}{c}{R}_M\mathrm{\&delta;L}\\ R_N\cos \mathrm{L\&delta;\&lambda;}\\ \mathrm{\&delta;h}\\ \mathrm{\&delta;}{\mathrm{\&upsi;}}_E\\ \mathrm{\&delta;}{\mathrm{\&upsi;}}_N\\ \mathrm{\&delta;}{\mathrm{\&upsi;}}_U\end{array}\right]+\left[\begin{array}{c}{v}_1\\ v_2\\ v_3\\ v_4\\ v_5\\ v_6\end{array}\right]=\left[\begin{array}{ccc}{0}_{3\&times;6}& \mathrm{diag}\left[\begin{array}{cccc}{R}_M& R_N& \cos L& 1\end{array}\right]& 0_{3\&times;9}\\ 0_{3\&times;3}& \mathrm{diag}\left[\begin{array}{ccc}1& 1& 1\end{array}\right]& 0_{3\&times;12}\end{array}\right]X+\left[\begin{array}{c}{v}_1\\ v_2\\ v_3\\ v_4\\ v_5\\ v_6\end{array}\right]---\left(1\right)$

In the formula (1), v ₁, v ₂, v ₃, v ₄, v ₅, v ₆Be respectively north orientation, east orientation, short transverse site error and east orientation, the north orientation of GPS output, day to velocity error, all be thought of as white noise;

2) CNS/ pressure altimeter/INS measurement equation

$Z_1=\left[\begin{array}{c}(L_I-L_B)R_M\\ ({\mathrm{\&lambda;}}_I-{\mathrm{\&lambda;}}_B)R_N\cos L\\ h_I-h_B\end{array}\right]=\left[\begin{array}{c}{R}_M\mathrm{\&delta;L}\\ R_N\cos \mathrm{L\&delta;\&lambda;}\\ \mathrm{\&delta;h}\end{array}\right]+\left[\begin{array}{c}{v}_1\\ v_2\\ v_3\end{array}\right]=\left[\begin{array}{ccc}{0}_{3\&times;6}& \mathrm{diag}\left[\begin{array}{cccc}{R}_M& R_N& \cos L& 1\end{array}\right]& 0_{3\&times;9}\end{array}\right]X+\left[\begin{array}{c}{v}_7\\ v_8\\ v_9\end{array}\right]---\left(2\right)$

V in the formula (2) ₇, v ₈, v ₉Be respectively north orientation, east orientation, the short transverse site error of the output of CNS and pressure altimeter, all be thought of as white noise;

3) SAR/INS measurement equation

V in the formula (3) ₁₀Course heading error when exporting for the SAR images match, v ₁₁Be north orientation site error, v ₁₂Be the east orientation site error, error all is thought of as white noise.

3. the fault-tolerance autonomous navigation method of multi-sensor of high-altitude long-endurance unmanned plane according to claim 1, it is characterized in that: the fault detection method described in the step (4) is as follows:

Calculate fault detect function lambda (k):

$\left.\begin{array}{c}{\mathrm{\&gamma;}}_i\left(k\right)=Z_i\left(k\right)-H_i\left(k\right){\hat{X}}_i(k/k-1)\\ U_i\left(k\right)=H_i\left(k\right)P_i(k/k-1)H_i^T\left(k\right)+R_i\left(k\right)\\ {\mathrm{\&lambda;}}_i\left(k\right)={\mathrm{\&gamma;}}_i^T\left(k\right)U_i^{-1}\left(k\right){\mathrm{\&gamma;}}_i\left(k\right)\end{array}\right\}---\left(4\right)$

In the formula: H _iIt is the observed differential matrix of i subsystem; P _i(k/k-1) be the optimum prediction estimated value and the optimum prediction estimated value error covariance matrix of i subsystem; R _i(k) be the observation noise variance matrix of i subsystem;

It is the residual error χ of m that λ (k) function is obeyed degree of freedom ²Distribute, m is for measuring the dimension of matrix Z; The criterion of fault judgement is:

In the formula, T _DBe predefined thresholding, by early warning rate P _FaPre-determine; If judge the subsystem non-fault, then its filter value is delivered to senior filter; If detecting subsystem has fault, then the fault subsystem is isolated, and lost efficacy unlikely the barrier for some reason of total system by system reconfiguration, after detecting subsystem recovery normally, again its filter value is sent into federal wave filter again.

Images