CN102819030A - Method for monitoring integrity of navigation system based on distributed sensor network - Google Patents
Method for monitoring integrity of navigation system based on distributed sensor network Download PDFInfo
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Abstract
The invention discloses a method for monitoring the integrity of a navigation system based on a distributed sensor network and belongs to the technical field of navigation positioning. According to the method disclosed by the invention, the integrity of the navigation system based on the distributed sensor network is monitored by utilizing a staged treatment mode of sensor-stage integrity monitoring treatment and system-stage integrity monitoring treatment. During the sensor-stage integrity monitoring stage, the integrity of a GNSS (Global Navigation Satellite System) receiver is monitored by utilizing an RAIM (Receiver Automatic Integrity Monitoring) method, the integrity of k SRIMU network nodes is monitored by utilizing a comprehensive method based on a moving window-parity vector method and a discrete wavelet transform method; and during the system-stage integrity monitoring stage, the integrity is monitored by adopting an innovation-based processing method and a moving window information processing method. According to the method disclosed by the invention, in terms of a distributed sensor network node to a whole distributed navigation system, both phase step fault and slope fault can be effectively monitored, and the integrity performance of the navigation system based on the distributed sensor network can be comprehensively enhanced.
Description
Technical field
The present invention relates to a kind of navigational system completeness monitoring method, belong to the technical field of navigational system integrity monitoring based on distributed sensor networks.
Background technology
Integrity is meant navigational system in use, breaks down or error that performance depreciation caused surpasses can receptible limit value (alarm threshold value) time, provides in time, the effective ability of warning information.In order to ensure the reliability of navigational system, need carry out integrity monitoring to navigational system, its fundamental purpose is to carry out fault detect and isolation.Integrity monitoring carries out processing such as detection statistic, threshold values judgement through the analysis to redundant informations such as hardware, softwares.
At present, both at home and abroad the integrity monitoring of navigational system is carried out more research, be divided into snapshot (snapshot) and continuity method (sequential) usually.Snapshot utilizes the metrical information of single epoch to detect and isolates instantaneous step fault; Be generally used for changing bigger fault; Typical method has least-square residuals method, odd even vector method etc., and GNSS (GLONASS) receiver autonomous integrity monitoring (RAIM) of broad research also belongs to snapshot both at home and abroad in addition.Snapshot can detect the step fault of navigation sensor or GNSS signal, but can not detect the slope fault that becomes slowly that is caused by inertial sensor drift etc.For the detection that becomes the slope fault slowly, usually based on the continuity method of historical cumulative information, like continuous likelihood ratio detection method (SPRT), based on the kinetic model method etc., but present algorithm is more consuming time, even reaches tens of minutes.People such as Brenner have provided based on the Kalman filter bank and have separated partition method (MSS) more; Carry out integrity according to the Kalman filter of all measuring assemblies and different measuring subclass and detect, and be applied to the hybrid navigation system (HIGH) of the IN/GPS/ atmosphere data of Honeywell company; People such as Diesel have provided a kind of autonomous integrity extrapolation method (AIME), are applied in the GPS/IRS combined system of Litton company.For the navigation of GNSS receiver, the RAIM method is a comparatively effectively completeness monitoring method commonly used at present; For the inertial navigation integrity monitoring, adopt GLRT (broad sense feel relieved ratio) method, odd even vector method etc. usually; For the multisensor combination, like GNSS and inertial navigation system combination, MSS and AIME are the completeness monitoring methods that has the practical applications report at present.
Adopting the navigational system of distributed sensor networks is a kind of new Navigation System Design theory; It is the navigation sensor in low cost of new generation in recent years, small size, lightweight; Like MEMS (MEMS) inertial sensor, MSIS (the solid-state inertial sensor of microminiature), optical fibre gyro, GNSS receiver etc., and new developing technology on the basis of the embedded microprocessor of high-speed high capacity and distributed modular electronic equipment.It is configured in a plurality of Inertial Sensor Systems a plurality of positions of carrier (like aircraft, naval vessel, Large Spacecraft etc.); Constitute distributed inertia network topology structure; Redundant distributed measurement information not only can be provided for the navigation of carrier; And be electronic equipment such as radar tracking, the equipment load etc. of carrier, local measurement system is provided, can also be provided for the local motion compensated inertial states information of carriers electron equipment simultaneously.Based on the navigational structure of distributed sensor networks through reconstruct with share limited computational resource, can improve the failure tolerant level, and can dynamic sensors configured systemic-function.
Present completeness monitoring method; Normally to independent inertial navigation system; Or independent GNSS navigational system, perhaps be suitable for the traditional concentrated filtering or the multisensor navigational system of federal filter structure, but can not directly apply in the distributed navigation system.Inertial sensor is except the step fault that causes owing to electron device, mechanical part; Usually also there is the drift that becomes slowly; And the motion state between each network node is not unified, and common completeness monitoring method can not directly apply to the distributed sensor networks structure.For navigational system based on distributed sensor networks, also there is not effective completeness monitoring method at present, guarantee the overall performance of whole navigational system.
Summary of the invention
The present invention is directed to novel navigational system based on distributed inertial sensor network; A kind of navigational system completeness monitoring method based on distributed sensor networks has been proposed; Overcome the deficiency that existing completeness monitoring method can not be applied directly to distributed navigation system, improve the integrity of distributed navigation system.
The present invention adopts following technical scheme for solving its technical matters:
A kind of navigational system completeness monitoring method based on distributed sensor networks; Adopt the hierarchical processing mode that integrity monitoring is handled and system-level integrity monitoring is handled of sensor-level, the navigational system based on distributed sensor networks is carried out integrity monitoring.Wherein, Navigational system based on distributed sensor networks comprises GNSS (GLONASS) receiver, a k SRIMU (the redundant Inertial Measurement Unit of angle mount) network node, and k is a natural number, and each network node can have identical performance or different performances; The information of in navigation processing, all sharing other network node is carried out information fusion; One of them SRIMU network node also with the information fusion of GNSS receiver, have higher navigation performance, as host node.The treatment step of integrity monitoring and navigation calculation is following:
(1) in the sensor-level integrity monitoring stage; Adopt RAIM (receiver autonomous integrity monitoring) method to carry out integrity monitoring to the GNSS receiver; FDI (fault detect and the isolation) processing unit that the metrical information of k SRIMU network node is sent to k SRIMU network node respectively carries out, and carries out fault detect and isolation processing;
(2) through the inertia information after the FDI processing unit processes of k SRIMU network node; Be input to respectively in k the inertia measurement integrated unit; Inertia information to through the SRIMU of sensor-level integrity monitoring is carried out fusion treatment, obtains the inertia information with respect to the calculating of three orthogonal coordinate systems;
(3), among k local KF (Kalman filter) of input, carry out local navigation information and resolve with the calculating inertia information after k the inertia measurement fusion treatment.Wherein, each local KF receives all inertia measurements of sharing fusion information; In addition, among the local KF of host node, also will merge, have higher performance than the navigation calculation of other wave filter through the GNSS receiver information after the RAIM monitoring.
(4) the new breath with k local KF is input in the system-level integrity monitoring processing unit; The completeness monitoring method that employing is handled based on new breath; Carry out the system-level integrity monitoring of navigational system, and integrity information is sent in k the local navigational state updating block.
(5) last, k local navigational state updating block, the navigational state information of the same type of k local KF of reception is carried out fusion treatment, obtains the navigation information of final renewal.In this k local navigational state upgrades, handle the integrity information that provides according to system-level integrity monitoring, if there is fault in certain local KF, then in fusion treatment, reject the navigational state information of this part KF.
Beneficial effect of the present invention is following:
1, present navigational system based on distributed sensor networks is a kind of new Navigation System Design notion, does not also have effective completeness monitoring method to such navigational system, and the present invention can address this problem.
2, adopt the hierarchical processing mode that integrity monitoring is handled and system-level integrity monitoring is handled of sensor-level, to whole navigational system aspect, strengthen the integrity performance comprehensively from the distributed sensor networks node.
3, good, the reliable height of integrity monitoring algorithm real-time, the calculated amount that are designed are little, the step fault that can not only detect and isolate fast change, and can detect and isolate the slope fault that becomes slowly.
4, in the sensor-level integrity monitoring; Adopt the integrated approach of MW-PV method and wavelet transform method, not only can detect successively and isolate, overcome independent odd even vector method simultaneously for 4 sensors to a plurality of faults; The shortcoming that can only detect and can not isolated fault is (when having only 1 redundant observed quantity; The odd even vector method can not tracing trouble is to appear on which sensor), when having only 1 redundant observation, still can effectively detect and isolated fault.Simultaneously, adopt the method for moving window (MV), can further detect and isolate the slope fault that becomes slowly.
5, on main network node, adopt the external auxiliary navigation sensor of GNSS receiver.New breath and the GNSS actual measurement that utilizes local KF is characteristics independently, and the local KF through monitoring all-network node newly ceases, and realizes system-level integrity monitoring.
6, in system-level integrity monitoring, adopt residual test, and newly cease the moving window method and handle based on the new breath of filtering, not only can carry out system-level in the fast step fault detect that becomes, can also carry out the slope fault detect that becomes slowly in system-level.
Description of drawings
Fig. 1 is that the navigational system based on distributed sensor networks of the present invention is implemented synoptic diagram.
Fig. 2 is the navigational system integrity monitoring process flow diagram based on distributed sensor networks of the present invention.
Fig. 3 is the SRIMU treatment scheme synoptic diagram of sensor-level integrity monitoring of the present invention.
Fig. 4 is a system-level integrity monitoring treatment scheme synoptic diagram of the present invention.
Embodiment
Below in conjunction with accompanying drawing the invention is explained further details.
Navigational system implementation route based on distributed sensor networks
As shown in Figure 1; With the airplane motion carrier is example (other motion carrier such as naval vessel, Large Spacecraft etc. are used similar with it), and by k the sensor network nodes that SRIMU constitutes, k is a natural number; The distributed a plurality of positions that are configured in aircraft; Constitute the distributed sensor networks topological structure, redundant distributed measurement information not only can be provided for the navigation of carrier, and be electronic equipment such as radar tracking, the equipment load etc. of carrier; Local measurement system is provided, can also be provided for the local motion compensated inertial states information of carriers electron equipment simultaneously.Based on the navigational structure of distributed sensor networks through reconstruct with share limited computational resource, can improve the failure tolerant level, and can dynamic sensors configured systemic-function.Usually the main navigator of aircraft is positioned at its center; Also dispose like other navaids such as GNSS receivers, be regarded as main network node in the present invention, therefore in local KF; Except making full use of each inertia measurement fusion information, also increased the GNSS observation information in the observed quantity.
Navigational system integrity monitoring overall plan based on distributed sensor networks
As shown in Figure 2, for navigational system integrity monitoring, adopt the hierarchical processing mode that integrity monitoring is handled and system-level integrity monitoring is handled of sensor-level based on distributed sensor networks.Wherein, Navigational system based on distributed sensor networks comprises GNSS (GLONASS) receiver, a k SRIMU (the redundant Inertial Measurement Unit of angle mount) network node, and k is a natural number, and each network node can have identical performance or different performances; The information of in navigation processing, all sharing other network node is carried out information fusion; One of them SRIMU network node also with the information fusion of GNSS receiver, have higher navigation performance, as host node.In order to obtain best system health property monitoring effect, the SRIMU network node 1 of choosing the carrier center position in this embodiment is as host node.In the sensor-level integrity monitoring stage; Adopt RAIM (receiver autonomous integrity monitoring) method to carry out integrity monitoring to the GNSS receiver; FDI (fault detect and the isolation) processing unit that the metrical information of k SRIMU network node is sent to k SRIMU network node respectively carries out, and carries out fault detect and isolation processing; Through the inertia information after the FDI processing unit processes of k SRIMU network node; Be input to respectively in k the inertia measurement integrated unit; Inertia information to through the SRIMU of sensor-level integrity monitoring is carried out fusion treatment, obtains the inertia information with respect to the calculating of three orthogonal coordinate systems; With the calculating inertia information after k the inertia measurement fusion treatment; Among k local KF (Kalman filter) of input; Carry out local navigation information and resolve, each local KF receives all inertia measurements of sharing and merges information, wherein in the local KF of host node (being local KF1 in this embodiment); Also will merge through the GNSS receiver information after the RAIM monitoring, have higher performance than the navigation calculation of other wave filter; The new breath of k local KF is input in the system-level integrity monitoring processing unit; The completeness monitoring method that employing is handled based on new breath; Carry out the system-level integrity monitoring of navigational system, and integrity information is sent in k the local navigational state updating block; At last, k local navigational state updating block, the navigational state information (being position, speed, attitude information) of the same type of k local KF of reception; Carry out fusion treatment; Obtain the navigation information of final renewal, in this k local navigational state upgrades, handle the integrity information that provides according to system-level integrity monitoring; If there is fault in certain local KF, then in fusion treatment, reject the navigational state information of this part KF.
The sensor-level integrity monitoring is handled
As shown in Figure 3, in the sensor-level integrity monitoring was handled, the GNSS receiver adopted common RAIM method to carry out integrity monitoring.Emphasis of the present invention adopts following steps to the integrity monitoring of SRIMU:
(1) general steps
A. set up observation equation
The number of sensors of remembering the redundant Inertial Measurement Unit of a angle mount is n; Wherein,
; N is for (being minimal configuration when the n=3 greater than 3 natural number; Do not possess fault detect and isolating power this moment); At first n sensor information sent in the fault detect processing unit based on MW-PV (moving window-odd even vector) method, it is following to set up observation equation
Wherein,
measures vector for the n dimension;
is real state vector (three axis angular rates, 3-axis acceleration etc.);
is the installation matrix of n sensor;
is the fault vectors of n dimension; As i (
) when individual sensor breaks down; I the element
of
is nonzero value, otherwise is zero;
is the measurement noise of sensor.
Calculating odd even vector
The odd even vector of formula (1) can be expressed as
Wherein,
is n-3 dimension odd even vector, and it has directly reflected the deviation information of fault;
is (n-3) * n dimension odd even space matrix; Have following character:
;
; Wherein
ties up null matrix for n-3,
be n-3 dimension unit matrix.Therefore;
obtains through the svd (SVD) of
being changeed the order matrix for the kernel matrix of matrix
is installed in the present invention
Where,
is the 3 × 3-dimensional unitary matrix; Σ is positive semi-definite 3 × n-dimensional diagonal matrix;
is the n × n-dimensional unitary matrix,
its conjugate transpose;
is a diagonal matrix whose diagonal elements that is
singular values;
is
The first three rows (ie
The former three);
is
after the n-3 lines, that is, from
null space of Zhang Cheng.Therefore, odd even space matrix
does
C. calculate detection statistic
Can know by formula (5); Odd even vector
is the function of fault
and noise
, and is irrelevant with quantity of state
.When the sensor non-fault
;
is the n-3 dimension normal distribution white noise sequence of zero-mean, and its variance does
Wherein,
is that noise criteria is poor.When certain sensor breaks down;
no longer is the white noise of zero-mean; Its average is
, and variance is
.Therefore, the definable detection statistic does
When the sensor non-fault,
obeys centralization
distribution that degree of freedom is n-3; When breaking down;
obeys decentralization
and distributes, and establishes the decentralization parameter for
.
Calculate detection threshold
By odd even vector sum detection statistic, make hypothesis as follows:
By assumed condition, SRIMU is in normal detected state when non-fault, if alarm is then alert for mistake.As the alert rate PFA of given mistake, then have
Can obtain detection threshold
by following formula.Through comparing detection statistic
and detection threshold
; if
then show and have fault, otherwise non-fault.
Moving window is handled
If adopt the integrity monitoring of above-mentioned odd even Vector Processing separately, be very effective for the step fault of fast change, but for the slope fault detect DeGrain that becomes slowly.The method that the present invention further adopts moving window to handle detects the slope fault, on the basis of odd even vector detection, and the odd even vector stack architecture of the first in first out that to set up a length be L
Wherein, The odd even that
is
constantly is vectorial;
is
constantly odd even vector, the odd even that
be
constantly is vectorial.At this moment, detection statistic does
And then through comparing detection statistic
and detection threshold
; if
then show and have fault, otherwise non-fault.
Fault diagnosis is isolated
When detecting fault; Further metrical information is sent in the resultant fault diagnosis isolation processing unit based on MW-PV method and wavelet analysis method; Handle according to odd even vector sum moving window, diagnose out step fault or slope fault respectively, fault-signal is isolated; And in observation equation, reject fault sensor information, construction observation equation again.Wherein,, when fault diagnosis is isolated, do not need wavelet analysis method,,, adopt the wavelet transform method this moment because the odd even vector method can not be diagnosed for n-3 fault (being when diagnosing isolated fault in last 4 sensors) for the 1st to n-4 fault.The fault diagnosis function that designs i sensor does
，
（11）
Wherein,
is i the column vector of odd even space matrix
.If the equal non-fault of all the sensors, then all fault diagnosis functions all are 0; If fault
has appearred in i sensor, then i fault diagnosis function is
.Therefore, can think corresponding to i sensor of maximum fault diagnosis function fault to have occurred, need isolate it.When fault isolation, the measuring amount of i sensor is rejected from observation equation (1).
(2) treatment scheme
A. the 1st fault
When the 1st fault occurs, calculate n-3 dimension odd even vector by observation equation, can effectively detect the step fault according to this n-3 dimension odd even vector; Then adopt moving window to handle, can detect the slope fault effectively; When detecting fault, further handle according to this n-3 dimension odd even vector sum moving window, diagnose out step fault or slope fault respectively, fault-signal is isolated, in observation equation, reject fault sensor information, construction observation equation again.
B. the 2nd fault
When the 2nd fault occurs; On the observation equation basis after the 1st fault isolation, calculate n-4 dimension odd even vector, adopt the aforementioned processing step; Directly, handle further the detection and isolation slope fault by moving window by n-4 dimension odd even vector detection and isolation step fault.
C. the 3rd fault is to n-4 fault
Adopt above-mentioned identical step, carry out fault detect and isolation successively
D. n-3 fault
When n-3 fault occurred, the odd even vector of this moment had only 1 dimension.Because the odd even vector according to 1 dimension can only detection failure, is that fault has appearred in which sensor and can not diagnose.Therefore, when according to 1 dimension odd even vector detection after fault, in the fault diagnosis isolation processing, adopt based on the multiple dimensioned signal decomposition of wavelet transform method to come tracing trouble and isolate.
Compare with Fourier conversion, quick Fourier transformation, wavelet transformation is the partial transformation of a kind of time and frequency domain, has the characteristic of multiresolution analysis; It has utilized the resolution on the non-uniform Distribution; Variable window through translation is observed non-stationary signal, uses narrow window in signal transients or sudden change place (high frequency), uses wide window at the gradual place of signal (low frequency); Can extract the signal waveform characteristic effectively, be described as digital microscope.Wavelet analysis is suitable for analyzing and handling non-stationary signal especially with the good characteristic of its time-frequency multiresolution analysis, has obtained widespread use at aspects such as signal denoising, Flame Image Process.The present invention will adopt wavelet analysis method that SRIMU output signal is carried out multiple dimensioned decomposition, make under the situation that the odd even vector method can't be used, also can the effective diagnosis isolated fault.
The sensor discrete burst that document is executed fault diagnosis does
, wherein
Expression signal decompose the
The level yardstick,
NExpression the
NIn the individual discrete time step, it can be broken down into the approximate signal part
With detailed signal section
Wherein,
and
is respectively low pass Hi-pass filter and Hi-pass filter coefficient, can be obtained by 2 scaling relations of scaling function
and wavelet function
，
（13）
Wherein,
.Wavelet function of the present invention adopts the Daubechies small echo, and concrete decomposition algorithm step is following:
The first step for given length does
KOriginal signal
, produce two groups of data according to formula (12), one group is the effect low-pass filter
The approximate signal that obtains
, another group is the effect Hi-pass filter
The detail signal that obtains
, these two signals all are original signal down-samplings with yardstick 2 under the wave filter effect.The characteristic of low frequency part characterization signal own, the nuance of HFS characterization signal.
Same way of second step; The low frequency part signal
that obtains the first step; Utilize above-mentioned method to decompose once more, up to the needed number of plies.For signal is done down-sampling, then signal length remains unchanged in decomposable process.
For length do
KSignal, whole algorithm is at the most
Accomplish in step.To the signal after the wavelet decomposition, diagnose according to the diagnosis threshold values, if surpass diagnosis valve system, think that then this sensor breaks down.Wherein diagnose threshold values to do
Wherein,
For the measurement noise criteria of this sensor poor;
KLength for the discrete signal sequence;
Be safety coefficient, its selection can be confirmed according to system performance and navigational system running environment.
Inertia measurement merges and local KF
At the inertia measurement fusing stage; Each SRIMU network node is to the sensor measurement information after handling through integrity monitoring; Rebuild observation equation:
; Be to have rejected fault
in the equation (1); Adopt weighted least-squares method to find the solution, obtain the calculating inertia measurement estimated information
of each network node.
the true state
estimate.
In the local KF stage, the inertia measurement estimated information of each network node, the inertia measurement estimated information in conjunction with other node makes up local Kalman filter, resolves the local navigational state of this network node and estimates.
Local KF for k node; Make its quantity of state for
; Wherein
representes the local navigational state of this node, and promptly the position of 3 dimensions, speed, attitude error totally 9 are tieed up state vectors;
is sensor error; Be gyro and accelerometer error, the clock that also comprises GNSS among the local KF (the local KF of host node is KF1 in this embodiment) for host node floats with frequency and floats error.Observed quantity is
; Difference residual vector for other node inertia measurement estimated information and network node also comprises the pseudorange difference vector for local KF1.Then local KF model does
Wherein,
is
to
constantly state-transition matrix;
and
is respectively system noise and measures noise vector.Based on Kalman filtering recurrence equation group, carry out local navigational state and estimate.
System-level integrity monitoring is handled
As shown in Figure 4, through monitoring the new breath of distributed local KF, carry out system-level integrity monitoring.The inertia measurement fusion information that local KF receives the all-network node is carried out Kalman filtering and is resolved, and wherein local KF1 also receives the pseudo range measurement information of GNSS except inertia measurement fusion information.
Local KF1, local KF2 ..., the resolving in the process of local KFk; Their wave filter is newly ceased
,
...,
; And the variance
of new breath,
...,
send to the integrity monitoring unit of handling based on new breath, carries out system-level integrity monitoring.
New breath for arbitrary local KF is handled, and the new breath of wave filter of t epoch does
Wherein,
is the t measurement of epoch;
is for measuring matrix;
is the one-step prediction value.
is similar to the odd even vector in the equation (5).During Where topical KF system non-fault;
is the n dimension normal distribution white noise sequence (n is the dimension of observation vector) of zero-mean, and its variance does
Wherein,
is the one-step prediction square error;
is for measuring noise variance matrix.When Where topical KF system breaks down,
will no longer be the white noise of zero-mean.The definition detection statistic does
During Where topical KF system non-fault;
obeys centralization
distribution that degree of freedom is n;
obeys decentralization
and distributes when breaking down, and establishes that the decentralization parameter is
.The calculating of detection threshold
and the odd even vector method integrity monitoring of aforesaid sensor-level are similar; Shown in equation (8), different is that degree of freedom is revised as n by n-3.Through comparing detection statistic
and detection threshold
; if
then show and have fault, otherwise non-fault.
Similar with sensor level integrity monitoring; Based on above-mentioned handling based on new breath; Belong to snapshot in essence, promptly handle according to the new breath of current epoch, therefore very effective for the step fault of fast change; But for the slope fault that becomes slowly; Because local KF is the Recursive Filtering system of equations, can follow the tracks of fault and cause
always very little, therefore detect insensitive.Therefore, the present invention has also adopted the moving window processing in system-level integrity monitoring, the slope fault is detected, and on new breath processing basis, the vectorial stack architecture of new breath of the first in first out that to set up a length be L
Wherein,
is t odd even vector constantly;
is
constantly odd even vector, the odd even that
be
constantly is vectorial.At this moment, detection statistic does
And then through comparing detection statistic
and detection threshold
; if
then show and have fault, otherwise non-fault.Integrity monitoring through this step is handled, and can obtain the integrity information of each local KF, and integrity information is sent in the local navigational state updating block.
Local navigational state update processing
For each network node, further design a local message fused filtering device, fully merge the local KF information of other network node, carry out local navigational state and upgrade, thereby obtain more high performance navigational system result.Local navigational state with network node 1, network node 2, network node 3 is updated to example, and the local navigational state renewal equation of network node 1 is following
Where,
and
Node 1 were updated state and local navigation are covariance matrix;
and
, respectively KF local node 1 are estimated values and their covariance matrix;
and
, respectively KF local node 1 are estimated values and their covariance matrix;
and
Local node 2, respectively KF estimate its average covariance matrix;
and
Local node 3, respectively KF estimate its average covariance matrix;
and
, respectively node 2 and node 3 to node a local coordinate system of the local coordinate system attitude transformation matrix;
and
are respectively the node 1 to node 2 and node 3 of the posture converting matrix.
When in system-level integrity monitoring is handled; Detect certain local KF and break down, then in local navigational state renewal equation, reject the information of this part KF; Thereby guaranteed the integrity of final local message fused filtering device, improved the integrity of whole navigational system.
Claims (3)
1. navigational system completeness monitoring method based on distributed sensor networks; It is characterized in that: adopt the integrity monitoring processing (11) of sensor-level and system-level integrity monitoring to handle the hierarchical processing mode of (22), the navigational system based on distributed sensor networks is carried out integrity monitoring; Wherein, Navigational system based on distributed sensor networks comprises GNSS receiver, a k SRIMU network node, and k is a natural number, and each network node has identical performance or different performances; The information of in navigation processing, all sharing other network node is carried out information fusion; One of them SRIMU network node also with the information fusion of GNSS receiver, have higher navigation performance, as host node; The treatment step of integrity monitoring and navigation calculation is following:
1) in the sensor-level integrity monitoring stage, adopt the RAIM method to carry out integrity monitoring to the GNSS receiver, the FDI processing unit that the metrical information of k SRIMU network node is sent to k SRIMU network node respectively carries out fault detect and isolation processing;
2) through the inertia information after the FDI processing unit processes of k SRIMU network node; Be input to respectively in k the inertia measurement integrated unit; Inertia information to through the SRIMU of sensor-level integrity monitoring is carried out fusion treatment, obtains the calculating inertia information with respect to three orthogonal coordinate systems;
3), among k local KF of input, carry out local navigation information and resolve with the calculating inertia information after k the inertia measurement fusion treatment; Wherein, each local KF receives all inertia measurements of sharing fusion information; Among the local KF of host node, also merge, have higher performance than the navigation calculation of other wave filter through the GNSS receiver information after the RAIM monitoring;
4) the new breath with k local KF is input in the system-level integrity monitoring processing unit (22); The completeness monitoring method that employing is handled based on new breath; Carry out the system-level integrity monitoring of navigational system, and integrity information is sent in k the local navigational state updating block;
5) last, k local navigational state updating block, the navigational state information of the same type of k local KF of reception is carried out fusion treatment, obtains the navigation information of final renewal; In this k local navigational state updating block, handle the integrity information that provides according to system-level integrity monitoring, if there is fault in certain local KF, then in fusion treatment, reject the navigational state information of this part KF.
2. the navigational system completeness monitoring method based on distributed sensor networks according to claim 1; It is characterized in that; Described sensor-level integrity monitoring is handled in (11); Suppose that its a SRIMU network node is made up of the redundant configuration of n inertial sensor through angle mount; Wherein,
, n is a natural number; And n＞3, the integrity monitoring treatment step of SRIMU comprises:
2-1) n sensor information of the redundant Inertial Measurement Unit of angle mount; At first send in the fault detect processing unit based on the MW-PV method; Set up observation equation; Calculate odd even vector, detection statistic, detection threshold successively, the fault of n sensor is detected, detect the step fault effectively; Then adopt moving window to handle, detect the slope fault effectively; When detecting fault; Further metrical information is sent in the resultant fault diagnosis isolation processing unit based on MW-PV method and wavelet analysis method; Handle according to odd even vector sum moving window, diagnose out step fault or slope fault respectively, fault-signal is isolated; And in observation equation, reject fault sensor information, construction observation equation again;
2-2) when the 1st fault occurs, according to step 2-1), calculate n-3 dimension odd even vector by observation equation, can effectively detect the step fault according to this n-3 dimension odd even vector, further adopt moving window to handle, detect the slope fault effectively; When detecting fault, further handle according to this n-3 dimension odd even vector sum moving window, diagnose out step fault or slope fault respectively, fault-signal is isolated, in observation equation, reject fault sensor information, construction observation equation again;
2-3) when the 2nd fault occurs, calculate n-4 dimension odd even vector by observation equation, with step 2-2) similar, directly tie up the odd even vector detection and isolate the step fault by n-4, handle further detection and isolate the slope fault by moving window;
2-3) when the 3rd fault, and follow-up fault adopts similar method to carry out fault detect and isolation when occurring successively;
2-4) when n-3 fault occurs, the odd even vector of this moment has only 1 dimension, and when detecting fault, in the fault diagnosis isolation processing, employing wavelet transform method is come tracing trouble and isolated.
3. the navigational system completeness monitoring method based on distributed sensor networks according to claim 1; It is characterized in that; Described system-level integrity monitoring is handled in (22); Handle based on the new breath of each local KF wave filter of distributed navigation system and to carry out integrity monitoring, treatment step comprises:
3-1) will pass through the k set of calculated inertia measurement information that the inertia measurement fusion treatment obtains; Be input among k the local KF and carry out the local message fusion treatment; Adopt the mode of differential filtering; Observation information among each local KF is the difference processing of all k set of calculated inertia measurement information, has also merged GNSS pseudorange information in the observed quantity of the local KF of said host node;
3-2) resolve in the process, their new breath is sent in the integrity monitoring unit of handling based on new breath at k local KF; At first carry out the step fault detect according to the residual test of the new breath of filtering; If the fault of not detecting then further adopts new breath moving window method to carry out the slope fault detect; When two kinds of integrity detections are all passed through, think that the result of this part KF is believable, otherwise show that this part KF breaks down;
The integrity information that 3-3) will obtain based on the integrity monitoring that new breath is handled; Be input in the local navigational state updating block based on local message fused filtering device; Further merge the local navigation information of the same type of k local KF, improve the navigation performance of each network node; When the new breath of certain local KF does not satisfy the integrity requirement, in local message fused filtering device, it is isolated.
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