CN108469621A - A kind of inertia/satellite combined guidance system quick fault testing method - Google Patents

A kind of inertia/satellite combined guidance system quick fault testing method Download PDF

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Publication number
CN108469621A
CN108469621A CN201810259614.XA CN201810259614A CN108469621A CN 108469621 A CN108469621 A CN 108469621A CN 201810259614 A CN201810259614 A CN 201810259614A CN 108469621 A CN108469621 A CN 108469621A
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filter
storage location
temporary storage
inertia
formula
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马航帅
王融
孙晓敏
王丹
刘建业
熊智勇
李荣冰
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China Aeronautical Radio Electronics Research Institute
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China Aeronautical Radio Electronics Research Institute
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/23—Testing, monitoring, correcting or calibrating of receiver elements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
    • G01C25/005—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass initial alignment, calibration or starting-up of inertial devices
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
    • G01S19/49—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system whereby the further system is an inertial position system, e.g. loosely-coupled

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Manufacturing & Machinery (AREA)
  • Automation & Control Theory (AREA)
  • Navigation (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The invention discloses a kind of inertia/combinations of satellites navigation quick fault testing methods, structure test statistics is newly ceased by predictive filter, it is run with filter parallel, predictive filter is not influenced in newly ceasing by error tracking, therefore can improve soft fault detection sensitivity.Fault detection method based on the present invention can reduce soft fault detection delay, be of great significance for the detection of inertia/satellite combined guidance system soft fault under complex environment.

Description

A kind of inertia/satellite combined guidance system quick fault testing method
Technical field
The present invention relates to a kind of inertia/satellite combined guidance system quick fault testing methods, belong to integrated navigation technology Field.
Background technology
The detection of inertia/satellite combined guidance system soft fault is navigate reliability and the difficulties that the safety is improved, Wherein detection real-time is an important indicator of soft fault detection.For soft fault, that is, allow to be detected also have Larger time delay seriously affects the performance generation of integrated navigation system, to cause the pole applied to aircraft security Big potential threat.
Inertia/satellite combined guidance system soft fault detection conventional method newly ceases structure event using integrated navigation filter Hinder the test statistics of detection, this method has the following disadvantages：Filter has good failure tracking characteristics, and is adopting With the structure of closed loop correction system state error, although this is advantageous to improving navigation system precision, for fault detect It is harmful.The failure amplitude that above-mentioned reason reflects during filter can be caused newly to cease reduces, therefore as test statistics pair event The susceptibility of barrier detection declines, and detection delay increases.
Invention content
The purpose of the present invention is to provide a kind of quick fault testing method for inertia/satellite combined guidance system, This method newly ceases structure test statistics by predictive filter, is run with filter parallel, predicts in new breath not by error The influence of tracking, therefore soft fault detection sensitivity can be improved, reduce fault detect delay.This method is for inertia/defend The detection of star integrated navigation system soft fault has application value.
In order to achieve the above objectives, the technical solution adopted in the present invention is：
A kind of inertia/combinations of satellites navigation quick fault testing method, includes the following steps：
Step (1)：Inertia/combinations of satellites Navigation Filter is calculated newly to cease
rk=Zk-HkXk/k-1 (1)
In formula, rkIt is newly ceased for k-th of discrete instants filter, ZkFor k-th of discrete instants filter observed quantity, HkFor kth A discrete instants filter observational equation, Xk/k-1For k-th of discrete instants filter status one-step prediction value；
Step (2)：Failure definition detects test statistics：
In formula,
Wherein, m is the discrete instants number needed for test statistics calculates,Newly to cease predicted value, VdIt is newly ceased for filter The variance of d-th of discrete instants：
In formula, Pd/d-1It is quantity of state covariance matrix, RdFor observed quantity covariance matrix, HdFor d-th of discrete instants filter Observational equation；
Step (3)：For given sample data sets D={ (Zc,rc) | c=1,2 ... n }, construct regression function：
Wherein w is weight vector, and b is departure；It is that n dimension state spaces are mapped to a Hilbert feature space Nuclear space mapping function；
Step (4)：Convert the regression function of step (3) to solution extreme-value problem：
In formula, ecFor error variance；γ is regularisation parameter；J is performance function.
Step (5)：Enable dimension i=1；
Step (6)：It obtains k-th of discrete instants filter and newly ceases rkTotal dimension dim, judge whether to meet i ＞ dim, It is thened follow the steps (9) as met, it is no to then follow the steps (7)；
Step (7)：Calculate the recurrence partial derivative that k-th of discrete instants filter newly ceases i-th dimension, including following sub-step：
Step (7.1)：Define the recurrence constraints that k-th of discrete instants filter newly ceases i-th dimension：
Step (7.2)：Define the Lagrangian that k-th of discrete instants filter newly ceases i-th dimension：
In formula, α=[α1 α2 … αn], e=[e1 e2 … en]。
Step (7.3)：Recurrence partial derivative is asked to w, b, e, α in the Lagrangian of acquisition in step (7.2) respectively, ：
Step (8)：I=i+1 is enabled, step (6) is gone to；
Step (9)：According to the recurrence partial derivative that step (7) obtains, new breath regression function is calculated, following step is specifically included Suddenly：
Step (9.1)：The recurrence partial derivative for the Lagrangian that step (7.3) obtains is converted into state space shape Formula, and w, e are eliminated, it obtains：
In formula,1=[1 1 ... 1]T, subscript(i)Indicate the i-th of corresponding vector Tie up element；
Step (9.2)：Define transition matrix Φ：
In formula,
Step (9.3)：Judge whether transition matrix Φ is reversible, if reversible, calculating formula (13)：
Step (9.4)：According to regression parameter α, b that step (9.3) obtains, new breath regression function is calculated：
In formula,
Step (10)：The new breath of prediction is calculated using the new breath regression function that step (9) obtains, is defined according to step (2) Fault detect test statistics carries out fault detect calculating, includes the following steps：
Step (10.1)：Enable s=1；
Step (10.2)：Judge whether s≤n, it is no to then follow the steps (10.3) if it is thening follow the steps (10.4)；
Step (10.3)：Current time inertia/combinations of satellites Navigation Filter, which is calculated, according to step (1) newly ceases rk, and deposit Storage goes to step (10.10) to s-th of temporary storage location of navigational computer；
Step (10.4)：It carries out navigational computer temporary storage location and calculates new breath update, including following sub-step：
Step (10.4.1)：Enable u=1；
Step (10.4.2)：Delete the data of the s-n+u navigational computer temporary storage location；
Step (10.4.3)：The data of the s-n+u+1 navigational computer temporary storage location are copied to s-n+u to keep in Unit；
Step (10.4.4)：Judge whether u ＜ n, if it is enables u=u+1, and return to step (10.4.2), otherwise hold Row step (10.4.5)；
Step (10.4.5)：Current inertia/combinations of satellites Navigation Filter, which is calculated, according to step (1) newly ceases rk, and store To n-th of temporary storage location of navigational computer；
Step (10.5)：It is updated using step (10.4) and obtains a new breath compositions of the n in navigational computer temporary storage location Sample data sets calculate new breath regression function according to step (9), and calculate the new breath of prediction：
Step (10.6)：Judge whether s≤m, it is no to then follow the steps (10.7) if it is thening follow the steps (10.8)；
Step (10.7)：Step (10.5) is calculated to the current time inertia/combinations of satellites Navigation Filter prediction obtained New breathIt stores to s-th of temporary storage location of tracer, and goes to step (10.10)；
Step (10.8)：Carry out the new breath update of tracer temporary storage location prediction, including following sub-step：
Step (10.8.1)：Enable v=1
Step (10.8.2)：Delete the data of the s-n+v navigational computer temporary storage location；
Step (10.8.3)：The data of the s-n+v+1 navigational computer temporary storage location are copied to s-n+v to keep in Unit；
Step (10.8.4)：Judge whether v ＜ m, if it is enables v=v+1, and return to step (10.8.2), otherwise hold Row step (10.8.5)；
Step (10.8.5)：It is pre- that step (10.5) is calculated to the current time inertia/combinations of satellites Navigation Filter obtained Survey new breathIt stores to m-th of temporary storage location of tracer；
Step (10.9)：Fault detect test statistics and step (10.8) update defined using step (2) obtains event The test statistics sample data sets for hindering the new breath composition of m prediction in detector temporary storage location, carry out fault detect calculating；
Step (10.10)：Judge whether that navigation terminates, then terminates in this way, otherwise enable s=s+1, return to step (10.2).
The beneficial effects of the invention are as follows：The present invention proposes a kind of inertia/combinations of satellites navigation quick fault testing method, The method this method can utilize support vector regression to improve the new breath failure tracking characteristics under soft fault, reduce fault detect Delay improves inertia/combinations of satellites navigation fault detect performance.
Description of the drawings
Fig. 1 is the principle of the present invention schematic diagram.
Specific implementation mode
Below in conjunction with the accompanying drawings, to a kind of inertia/satellite combined guidance system quick fault testing method proposed by the present invention It is described in detail：
A kind of inertia/satellite combined guidance system quick fault testing method, principle is as shown in Figure 1, include following step Suddenly：
Step (1)：Inertia/combinations of satellites Navigation Filter is calculated newly to cease
rk=Zk-HkXk/k-1 (1)
In formula, rkIt is newly ceased for k-th of discrete instants filter, ZkFor k-th of discrete instants filter observed quantity, HkFor kth A discrete instants filter observational equation, Xk/k-1For k-th of discrete instants filter status one-step prediction value；
Step (2)：Failure definition detects test statistics：
In formula,
Wherein, m is the discrete instants number needed for test statistics calculates,Newly to cease predicted value, VdIt is newly ceased for filter The variance of d-th of discrete instants：
In formula, Pd/d-1It is quantity of state covariance matrix, RdFor observed quantity covariance matrix, HdFor d-th of discrete instants filter Observational equation；
Step (3)：For given sample data sets D={ (Zc,rc) | c=1,2 ... n }, construct regression function：
Wherein w is weight vector, and b is departure；It is that n dimension state spaces are mapped to a Hilbert feature space Nuclear space mapping function；
Step (4)：Convert the regression function of step (3) to solution extreme-value problem：
In formula, ecFor error variance；γ is regularisation parameter；J is performance function.
Step (5)：Enable dimension i=1；
Step (6)：It obtains k-th of discrete instants filter and newly ceases rkTotal dimension dim, judge whether to meet i ＞ dim, It is thened follow the steps (9) as met, it is no to then follow the steps (7)；
Step (7)：Calculate the recurrence partial derivative that k-th of discrete instants filter newly ceases i-th dimension, including following sub-step：
Step (7.1)：Define the recurrence constraints that k-th of discrete instants filter newly ceases i-th dimension：
Step (7.2)：Define the Lagrangian that k-th of discrete instants filter newly ceases i-th dimension：
In formula, α=[α1 α2 … αn], e=[e1 e2 … en]。
Step (7.3)：Recurrence partial derivative is asked to w, b, e, α in the Lagrangian of acquisition in step (7.2) respectively, ：
Step (8)：I=i+1 is enabled, step (6) is gone to；
Step (9)：According to the recurrence partial derivative that step (7) obtains, new breath regression function is calculated, following step is specifically included Suddenly：
Step (9.1)：The recurrence partial derivative for the Lagrangian that step (7.3) obtains is converted into state space shape Formula, and w, e are eliminated, it obtains：
In formula,1=[1 1 ... 1]T, subscript(i)Indicate the i-th of corresponding vector Tie up element；
Step (9.2)：Define transition matrix Φ：
In formula,
Step (9.3)：Judge whether transition matrix Φ is reversible, if reversible, calculating formula (13)：
Step (9.4)：According to regression parameter α, b that step (9.3) obtains, new breath regression function is calculated：
In formula,
Step (10)：The new breath of prediction is calculated using the new breath regression function that step (9) obtains, is defined according to step (2) Fault detect test statistics carries out fault detect calculating, includes the following steps：
Step (10.1)：Enable s=1；
Step (10.2)：Judge whether s≤n, it is no to then follow the steps (10.3) if it is thening follow the steps (10.4)；
Step (10.3)：Current time inertia/combinations of satellites Navigation Filter, which is calculated, according to step (1) newly ceases rk, and deposit Storage goes to step (10.10) to s-th of temporary storage location of navigational computer；
Step (10.4)：It carries out navigational computer temporary storage location and calculates new breath update, including following sub-step：
Step (10.4.1)：Enable u=1；
Step (10.4.2)：Delete the data of the s-n+u navigational computer temporary storage location；
Step (10.4.3)：The data of the s-n+u+1 navigational computer temporary storage location are copied to s-n+u to keep in Unit；
Step (10.4.4)：Judge whether u ＜ n, if it is enables u=u+1, and return to step (10.4.2), otherwise hold Row step (10.4.5)；
Step (10.4.5)：Current inertia/combinations of satellites Navigation Filter, which is calculated, according to step (1) newly ceases rk, and store To n-th of temporary storage location of navigational computer；
Step (10.5)：It is updated using step (10.4) and obtains a new breath compositions of the n in navigational computer temporary storage location Sample data sets calculate new breath regression function according to step (9), and calculate the new breath of prediction：
Step (10.6)：Judge whether s≤m, it is no to then follow the steps (10.7) if it is thening follow the steps (10.8)；
Step (10.7)：Step (10.5) is calculated to the current time inertia/combinations of satellites Navigation Filter prediction obtained New breathIt stores to s-th of temporary storage location of tracer, and goes to step (10.10)；
Step (10.8)：Carry out the new breath update of tracer temporary storage location prediction, including following sub-step：
Step (10.8.1)：Enable v=1
Step (10.8.2)：Delete the data of the s-n+v navigational computer temporary storage location；
Step (10.8.3)：The data of the s-n+v+1 navigational computer temporary storage location are copied to s-n+v to keep in Unit；
Step (10.8.4)：Judge whether v ＜ m, if it is enables v=v+1, and return to step (10.8.2), otherwise hold Row step (10.8.5)；
Step (10.8.5)：It is pre- that step (10.5) is calculated to the current time inertia/combinations of satellites Navigation Filter obtained Survey new breathIt stores to m-th of temporary storage location of tracer；
Step (10.9)：Fault detect test statistics and step (10.8) update defined using step (2) obtains event The test statistics sample data sets for hindering the new breath composition of m prediction in detector temporary storage location, carry out fault detect calculating；
Step (10.10)：Judge whether that navigation terminates, then terminates in this way, otherwise enable s=s+1, return to step (10.2).
The method of the present invention newly ceases structure test statistics by predictive filter, is run with filter parallel, predicts new breath In do not influenced by error tracking, therefore soft fault detection sensitivity can be improved.To support vector regression for combining Soft fault has carried out deriving analysis, and has carried out simulating, verifying.Fault detection method based on the present invention can reduce gradual Fault detect is delayed, and is of great significance for the detection of inertia/satellite combined guidance system soft fault under complex environment.

Claims (1)

  1. The quick fault testing method 1. a kind of inertia/combinations of satellites is navigated, it is characterised in that include the following steps：
    Step (1)：Inertia/combinations of satellites Navigation Filter is calculated newly to cease
    rk=Zk-HkXk/k-1 (1)
    In formula, rkIt is newly ceased for k-th of discrete instants filter, ZkFor k-th of discrete instants filter observed quantity, HkFor k-th from Dissipate moment filter observational equation, Xk/k-1For k-th of discrete instants filter status one-step prediction value；
    Step (2)：Failure definition detects test statistics：
    In formula,
    Wherein, m is the discrete instants number needed for test statistics calculates,Newly to cease predicted value, VdIt is newly ceased in d for filter The variance of a discrete instants：
    In formula, Pd/d-1It is quantity of state covariance matrix, RdFor observed quantity covariance matrix, HdFor d-th of discrete instants filter observation side Journey；
    Step (3)：For given sample data sets D={ (Zc,rc) | c=1,2 ... n }, construct regression function：
    Wherein w is weight vector, and b is departure；It is the core sky that n dimension state spaces are mapped to a Hilbert feature space Between mapping function；
    Step (4)：Convert the regression function of step (3) to solution extreme-value problem：
    In formula, ecFor error variance；γ is regularisation parameter；J is performance function.
    Step (5)：Enable dimension i=1；
    Step (6)：It obtains k-th of discrete instants filter and newly ceases rkTotal dimension dim, judge whether to meet i ＞ dim, such as meet It thens follow the steps (9), it is no to then follow the steps (7)；
    Step (7)：Calculate the recurrence partial derivative that k-th of discrete instants filter newly ceases i-th dimension, including following sub-step：
    Step (7.1)：Define the recurrence constraints that k-th of discrete instants filter newly ceases i-th dimension：
    Step (7.2)：Define the Lagrangian that k-th of discrete instants filter newly ceases i-th dimension：
    In formula, α=[α1 α2 … αn], e=[e1 e2 … en]。
    Step (7.3)：Recurrence partial derivative is asked to w, b, e, α in the Lagrangian of acquisition in step (7.2) respectively, is obtained：
    Step (8)：I=i+1 is enabled, step (6) is gone to；
    Step (9)：According to the recurrence partial derivative that step (7) obtains, new breath regression function is calculated, following steps are specifically included：
    Step (9.1)：The recurrence partial derivative for the Lagrangian that step (7.3) obtains is converted into state space form, and W, e are eliminated, is obtained：
    In formula,Subscript(i)Indicate the i-th dimension member of corresponding vector Element；
    Step (9.2)：Define transition matrix Φ：
    In formula,
    Step (9.3)：Judge whether transition matrix Φ is reversible, if reversible, calculating formula (13)：
    Step (9.4)：According to regression parameter α, b that step (9.3) obtains, new breath regression function is calculated：
    In formula,
    Step (10)：The new breath of prediction, the failure defined according to step (2) are calculated using the new breath regression function that step (9) obtains It detects test statistics and carries out fault detect calculating, include the following steps：
    Step (10.1)：Enable s=1；
    Step (10.2)：Judge whether s≤n, it is no to then follow the steps (10.3) if it is thening follow the steps (10.4)；
    Step (10.3)：Current time inertia/combinations of satellites Navigation Filter, which is calculated, according to step (1) newly ceases rk, and store to leading Navigate s-th of temporary storage location of computer, and goes to step (10.10)；
    Step (10.4)：It carries out navigational computer temporary storage location and calculates new breath update, including following sub-step：
    Step (10.4.1)：Enable u=1；
    Step (10.4.2)：Delete the data of the s-n+u navigational computer temporary storage location；
    Step (10.4.3)：The data of the s-n+u+1 navigational computer temporary storage location are copied into s-n+u temporary lists Member；
    Step (10.4.4)：Judge whether u ＜ n, if it is enables u=u+1, and return to step (10.4.2), otherwise execute step Suddenly (10.4.5)；
    Step (10.4.5)：Current inertia/combinations of satellites Navigation Filter, which is calculated, according to step (1) newly ceases rk, and store to navigation N-th of temporary storage location of computer；
    Step (10.5)：The sample for obtaining the n in navigational computer temporary storage location new breath composition is updated using step (10.4) Data acquisition system calculates new breath regression function according to step (9), and calculates the new breath of prediction：
    Step (10.6)：Judge whether s≤m, it is no to then follow the steps (10.7) if it is thening follow the steps (10.8)；
    Step (10.7)：Step (10.5) is calculated to the current time inertia/new breath of combinations of satellites Navigation Filter prediction obtained It stores to s-th of temporary storage location of tracer, and goes to step (10.10)；
    Step (10.8)：Carry out the new breath update of tracer temporary storage location prediction, including following sub-step：
    Step (10.8.1)：Enable v=1
    Step (10.8.2)：Delete the data of the s-n+v navigational computer temporary storage location；
    Step (10.8.3)：The data of the s-n+v+1 navigational computer temporary storage location are copied into s-n+v temporary lists Member；
    Step (10.8.4)：Judge whether v ＜ m, if it is enables v=v+1, and return to step (10.8.2), otherwise execute step Suddenly (10.8.5)；
    Step (10.8.5)：It is new that step (10.5) is calculated to the current time inertia/combinations of satellites Navigation Filter prediction obtained BreathIt stores to m-th of temporary storage location of tracer；
    Step (10.9)：Fault detect test statistics and step (10.8) update defined using step (2) obtains failure inspection The test statistics sample data sets for surveying the new breath composition of m prediction in device temporary storage location, carry out fault detect calculating；
    Step (10.10)：Judge whether that navigation terminates, then terminates in this way, otherwise enable s=s+1, return to step (10.2).
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