WO2012123545A1 - Procédé et dispositif pour fusionner des signaux corrélés de manière partitionnée - Google Patents

Procédé et dispositif pour fusionner des signaux corrélés de manière partitionnée Download PDF

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Publication number
WO2012123545A1
WO2012123545A1 PCT/EP2012/054564 EP2012054564W WO2012123545A1 WO 2012123545 A1 WO2012123545 A1 WO 2012123545A1 EP 2012054564 W EP2012054564 W EP 2012054564W WO 2012123545 A1 WO2012123545 A1 WO 2012123545A1
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WO
WIPO (PCT)
Prior art keywords
source signals
navigation
target signal
covariance
partitions
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/EP2012/054564
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German (de)
English (en)
Inventor
Marc-André BEYER
Arne Petersen
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Raytheon Anschuetz GmbH
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Raytheon Anschuetz GmbH
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Application filed by Raytheon Anschuetz GmbH filed Critical Raytheon Anschuetz GmbH
Priority to EP12713901.2A priority Critical patent/EP2686640A1/fr
Publication of WO2012123545A1 publication Critical patent/WO2012123545A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • 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
    • G01C21/20—Instruments for performing navigational calculations
    • 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
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • G06F18/251—Fusion techniques of input or preprocessed data

Definitions

  • the present invention relates to a method, a device, a computer program product and a navigation system for determining navigation information for a, preferably moving, object, in particular vehicle or aircraft, based on source signals of at least two sensors that provide information about a navigation state of the object.
  • the problem of data fusion of multiple source signals is a long-standing research area of signal processing.
  • simple mean values are determined from the sources and passed on as the target signal.
  • An extension to this is represented by the weighted fusion algorithms.
  • weights are specified for all components of the source signals, which are taken into account in the estimation of the target signal.
  • Such algorithms include, for example, the Weighted Least Squares Estimator (WLSE).
  • WLSE Weighted Least Squares Estimator
  • a commonly used data fusion method uses a Kalman filter (see also Greg Welch and Gary Bishop, “An Introduction to the Kalman Filter,” UNC-Chapel Hill, 2006)
  • the filter uses a process model which adapts the internal state of the Kalman filter to the dynamics of the sequence of states to be estimated.This step is called prediction, and then measurements (also called observations) are used. to minimize the error introduced by the process model, this step is called correction or update.
  • the actual step of data fusion - the internal, predicted state and the measurements are fused. It is therefore possible to interpret the predicted state and the measurements as source signals to be fused which contain information about the object system.
  • these source signals are provided with generalized, inverse weight matrices.
  • These covariance matrices determine a Gaussian distribution density (see also J. Chris McGIone, “Manual of Photogrammetry”, 5th edition, American Society for Photogrammetry and Remote Sensing, 2004), which describes the stochastic distribution of the errors in the Thanks to the covariances, the source signals can not only be weighted in a weighted manner, but also their internal stochastic correlations (correlations) of the source signals are considered.
  • the source signals In order to merge a set of source signals, given in the form of mean and covariance pairs ⁇ pwj, the source signals must be transformed into a common coordinate system. Such transformations are well known and are used by default in Kalman filters. For the following explanations, we assume that the source signals have been transformed into a common coordinate system and are in the form of fe P * « ⁇ .
  • the extended form of the Kalman filter see J. Wendel: "Integrated Navigation Systems", Oldenbourg Verlag, 2007), in which correlations ⁇ j ⁇ P * '* « ⁇ may exist between the different source signals, for the fusion of K signals ⁇ x tl x i x i ⁇ in a uniform coordinate space
  • the simplest method is to scale the obtained covariance matrix P z by a factor greater than one.
  • the incoming covariances are divided into blocks according to the division. Subsequently, scalings for the potentially correlated blocks P x c m of the source signals are determined and applied to them. The correlations between the blocks within the individual covariances are discarded. In this way, the correlations between the source signals can be further limited. However, since the correlations of the blocks within the covariances of the source signals are discarded, information about the structure of the covariances contained in them is lost.
  • the invention has for its object to provide a method and apparatus for determining a navigation information, which are characterized by high accuracies.
  • the invention provides a corresponding navigation system.
  • Some embodiments of the method and apparatus are adapted to receive source signals, here in the form of mean and covariance pairs, of which at least two may be partitioned into a potentially correlated part and an uncorrelated part, and to transmit the target signal, in the form a mean and covariance pair so as to enable a response of a physical system receiving the target signal.
  • Advantages of embodiments of the invention are that the knowledge about the partitioning can be introduced optimally into the estimation process, whereby the accuracy of the fusion result is increased while at the same time guaranteed consistency.
  • the invention has several alternative or additive embodiments, e.g. a method for data fusion, a device (device) for data fusion, as well as an implementation of the method in the device.
  • Fig. 1 shows a basic flow diagram of an inventive
  • FIG. 2 shows a schematic block diagram of a data fusion system according to the invention
  • Fig. 3 shows a practical embodiment of an inventive
  • Fig. 4 shows a practical embodiment of an inventive
  • FIG. 1 shows a basic flow chart of a data fusion according to the invention with several stages or steps.
  • the fusion used here is done by subdividing the source signals into their correlated and uncorrelated components.
  • the structure of the correlations or partitioning is known or recognizable by analysis or estimation.
  • Each source signal of a uniform coordinate space, present in the form of a mean and covariance pair ⁇ x P x ⁇ , is transformed into an unknown, potentially correlated partition and a known correlated partition according to FIG.
  • stage or step 04 take place in stage or step 02 for all source signals.
  • the composite mean vector and the encryption bundkovarianzmatrix (mean values and covariance matrices of the composite system) are given by p x i, cu x i, cu p x l, cu x i, c k p x i, cu x K, cu p x K, cu i , cu p x K, cu x i, ck p x K, cu x K, cu x K, cu
  • step or step 06 in which the associated optimum weights ⁇ , are determined.
  • the choice of weights is fundamentally not decisive for the consistency of the fusion result, but rather for its quality in the form of a measure for the fused covariance matrix.
  • the task of determining the weights corresponds to a nonlinear optimization problem of the form min / (P zz )
  • the quality function can be any measure of the covariance matrix, such as trace, determinant, Lp norm, etc.
  • the resulting weights ⁇ are passed on after the determination in step or step 06 at step or step 08. There, the outputs of the stages or steps 04 and 06 are used to separate the partitioning and corresponding scaling of the different covariance matrices in the partitions according to step 10.
  • the fused target signal (mean and covariance pair) is transferred to stage or step 14, where it is made available for further processing.
  • Fig. 2 shows a block diagram of the data fusion system according to the present invention. This includes a measuring system 16 for measuring properties of an object system 44.
  • a detector 18 detects signals of the object system 44 which contain information about the states of the object system 44.
  • a connection 20 connects the detector 18 to the measuring system 16 in order to realize a signal transmission from the detector 18 to the measuring system 16. It should be noted that the connection 20 and all of the following connections may represent wired, wireless or other connection types or a combination thereof.
  • a connection 22 connects the measuring system 16 to a signal processing unit 24 for transmitting the information about the object system 44 obtained via the measuring system 16.
  • the signal processing unit 24 includes a central processing unit or CPU (Central Processing Unit) 26 for processing signals supplied from the measurement system 16 via the connection 22.
  • the CPU 26 may be a computation unit such as a microprocessor, a multiprocessor, or an analog computer, but is not limited to these examples.
  • the signal processing unit 24 further includes a machine-readable memory 28 for storing signals received from the measuring system 16, for storing intermediate signals. and final results of the CPU 26 and for storing program instructions for the CPU 26.
  • the machine-readable memory 28 consists for example of a combination of optical and electronic storage media and may be, for example, a combination of writable (read-write) and write-protected (read-only) storage media ,
  • a connection 32 connects the signal processing unit 24 to an optional display unit 30 for transmitting signals for display on the display unit 30.
  • a connection 36 connects the signal processing unit 24 to an optional input unit 34 for transmitting signals to the signal processing unit 24 that are input by the user to the input unit 34.
  • the input unit may e.g. a combination of input units such as e.g. Mouse, keyboard, or any other device for manually entering information to the signal processing unit 24.
  • a link 40 connects the signal processing unit 24 to a response system 38 for communicating signals from the signal processing unit 24 to the response system 38.
  • the response system 38 is responsive to signals received from the signal processing unit 24 over the link 40 by performing a function depends on the state of the object system 44. This condition is determined by the signal processing unit 24.
  • the reaction of the reaction system 38 may be e.g. affect the state of the object system 44.
  • the measuring system 16 corresponds to a collection of vehicle-specific sensors, such as inertial navigation systems, GNSS receivers (Global Navigation Satellite System), Compasses, logs, rotation sensors, depth sensors, or other sensors that provide information about the condition of a vehicle.
  • the signal processing unit 24 receives this information in the form of mean value pairs and covance pairs and calculates therefrom a navigation state for the vehicle in FIG Form of a mean and covariance pair and a set of derived navigation information.
  • the results of the signal processing unit 24 are then transmitted to the reaction system 38.
  • the response system 38 may respond to this navigation information.
  • the reaction system 38 may e.g. a system for dynamic positioning of vehicles or a system for the management of weapons for vehicles or a system for course or train or speed or depth or position control for vehicles.
  • Fig. 3 shows a flow chart of a practical embodiment of a data fusion method according to the present invention.
  • Processing instructions according to steps 46 and 48 form a set of source signals consisting of received measurements and stored estimates calculated from previous measurements.
  • step 50 the measurements and estimates are transformed into a uniform coordinate space, so that source signals in the form ⁇ 0 ⁇ ⁇ ⁇ ⁇ ⁇ are present.
  • step 52 the partitions of the source signals are determined.
  • step 54 the optimal weights for scaling the partitions of the source signals are determined.
  • step 56 Scaling and partitioning are done in step 56.
  • the optimal fusion is done in step 58.
  • An output of the estimate is made in step 60.
  • Step 62 generates a physical response based on the signal transmitted in step 60.
  • Step 64 determines whether the calculated target signal should be stored (step 66) for eventual further processing.
  • the calculated target signal is optionally propagated forward in time.
  • the presented implementation describes a system for estimating a state sequence using a Kalman filter. An optimal combination of the multiple measurements and their covariances with previous estimates is made.
  • the method shown in FIG. 3 may be implemented as a software program for controlling a computerized system, wherein code means of the program are configured to generate at least some of the steps shown in FIG. 3 when executed on a processor device of the computerized system.
  • the software program can be stored on a removable and computer-readable medium.
  • FIG. 4 shows a practical exemplary embodiment of a device according to the invention, which is designed here as navigation system 70 for determining navigation information for a preferably moving object, in particular a vehicle or aircraft, based on source signals Qi of at least two sensors. At least two sensors provide information about a navigation state of the object as source signals to a receiving device 71.
  • a detection device 72 connected to the receiving device 71 serves to detect the source signals in the form of mean value and covariance pairs.
  • a first setting device 73 effects a determination of partitions for at least two source signals.
  • a second setting device 74 connected to the first setting device 73 is provided for determining optimum weights for scaling the obtained partitions of the at least two source signals in a defined coordinate space.
  • a signal processing device 75 is provided for partitioning and scaling the at least two source signals into potentially correlated components and uncorrelated components using the partitions and weights determined by the first and second setting devices 73, 74.
  • An evaluation device 76 is designed for fusing the partitioned and scaled source signals taking into account the potentially correlated components in order to obtain a fused target signal, and for deriving navigation information Nl from the fused target signal.
  • An example of deriving navigation information is the conversion of a speed of Cartesian coordinates in polar coordinates. In the merged target signal, velocities are typically in Cartesian coordinates (eg, velocity to the north and east), from which polar coordinates (eg, velocity and heading over ground) are derived.
  • data such as, in particular, navigation information data may be present in the fused target signal, which need no further conversion, but can be used directly for the desired purposes, so that in this case the derivation is ultimately limited to removal from the target signal.
  • An embodiment of the invention provides a method for determining a fused target signal from a set of K> 2 source signals, wherein each source signal / ' , 1 ⁇ i ⁇ K carries information about a physical object system in the form of a mean and covariance pair.
  • Each source signal / is decomposable into a potentially correlated and known correlated partition, according to
  • the fused target signal is defined by a mean and covariance pair ⁇ z, P zz ⁇ which passes through
  • the parameters ⁇ can be determined so that the covariance matrix P zz or a sub-block of P zz is of minimum size, based on the arithmetic measures determinant, track, weighted Lp norm and largest eigenvalue.
  • the parameters ⁇ can be determined so that the covariance matrix P zz or a sub-block of P zz is of maximum size, based on the arithmetic measures determinant, track, weighted Lp norm and largest eigenvalue.
  • the merged target signal may correspond to the navigation state of a vehicle, in particular a watercraft.
  • One or more source signals may correspond to position and / or velocity measurements from inertial navigation systems and / or GNSS receivers.
  • a programmed signal process unit may be provided for determining a fused target signal from the set of K> 2 source signals.
  • the parameters ⁇ are calculated as a function of the covariance matrix P zz , whereby the programmed signal process unit can be designed by means of mathematical algorithms for determining the parameters ⁇ , such that the covariance matrix P zz or a sub-block of P zz is of minimum size, relative to the arithmetic measures determinant, lane, weighted Lp norm and largest eigenvalue.
  • the programmed signal processing unit including mathematical algorithms for determining the parameters ⁇ , can be designed so that the covariance matrix P zz or a sub-block of P zz is of maximum size, based on the arithmetic measures determinant, track, weighted Lp norm and largest eigenvalue ,
  • the merged target signal may correspond to the navigation state of a vehicle, in particular a watercraft, and one or more source signals may correspond to position and / or speed measurements from inertial navigation systems and / or GNSS receivers.
  • This matrix can be rotated with a rotation matrix R (similarity transformation) without changing the eigenvalues, so that
  • This matrix is positive semidefinite if the upper left block matrix is positively semidefinite, ie it must be shown that - -pXiXK
  • a method, a device, a computer program product and a navigation system have been described for determining navigation information for a preferably moving object, in particular a vehicle or aircraft, based on source signals of at least two sensors which provide information about a navigation state of the object.
  • Source signals are received in the form of mean and covariance pairs, of which at least two can be partitioned into a potentially correlated part and an uncorrelated part.
  • the target signal is transmitted, for example in the form of a mean and covariance pair, so as to enable a response of a physical system receiving the target signal.

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Data Mining & Analysis (AREA)
  • Theoretical Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)
  • Navigation (AREA)

Abstract

La présente invention concerne un procédé, un dispositif, un produit de programme d'ordinateur et un système de navigation pour déterminer une information de navigation pour un objet, en particulier un véhicule ou aéronef, de préférence en mouvement, sur la base de signaux sources (1... K) d'au moins deux capteurs qui fournissent des informations sur une position de navigation de l'objet. Des signaux sources sont reçus sous la forme de paires de valeurs moyennes et paires de covariances dont au moins deux peuvent être partitionnées en une partie potentiellement corrélée et une partie non corrélée. Le signal cible est transmis, par exemple sous la forme d'une paire de valeurs moyennes et d'une paire de covariances ce qui permet une réaction d'un système physique qui reçoit le signal cible.
PCT/EP2012/054564 2011-03-15 2012-03-15 Procédé et dispositif pour fusionner des signaux corrélés de manière partitionnée Ceased WO2012123545A1 (fr)

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EP12713901.2A EP2686640A1 (fr) 2011-03-15 2012-03-15 Procédé et dispositif pour fusionner des signaux corrélés de manière partitionnée

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DE102011005584A DE102011005584A1 (de) 2011-03-15 2011-03-15 Verfahren und Vorrichtung zur Fusion partitioniert korrelierter Signale
DE102011005584.3 2011-03-15

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US12061277B1 (en) * 2023-08-10 2024-08-13 Beihang University Airborne positioning method in aviation navigation network based on multi-source information fusion

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
RU2559820C1 (ru) * 2014-06-30 2015-08-10 Российская Федерация, от имени которой выступает Государственная корпорация по атомной энергии "Росатом" Способ навигации движущихся объектов
DE102017108107A1 (de) 2017-04-13 2018-10-18 Volkswagen Aktiengesellschaft Verfahren, vorrichtung und computerlesbares speichermedium mit instruktionen zur schätzung einer pose eines kraftfahrzeugs
CN112229406B (zh) * 2020-09-29 2024-06-18 中国航空工业集团公司沈阳飞机设计研究所 一种多余度引导全自动着陆信息融合方法及系统
CN114279432B (zh) * 2021-12-13 2024-10-15 阿里云计算有限公司 融合定位方法、计算设备及存储介质
CN120176673B (zh) * 2025-03-11 2026-04-28 中国矿业大学 一种外部干扰下的封闭空间无人系统定位方法

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2068165A2 (fr) * 2007-12-07 2009-06-10 Honeywell International Inc. Système de navigation doté d'un appareil pour détecter les défauts de précision

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0993658B1 (fr) * 1997-07-02 2004-10-06 Raytheon Company Procede et appareil permettant d'obtenir une vision au moyen d'une image virtuelle
FR2866423B1 (fr) * 2004-02-13 2006-05-05 Thales Sa Dispositif de surveillance de l'integrite des informations delivrees par un systeme hybride ins/gnss
DE102010011415B3 (de) * 2010-03-15 2011-03-31 Deutsches Zentrum für Luft- und Raumfahrt e.V. Verfahren zur Ermittlung und zum Tracking von Eigenschaften eines Funkübertragungskanals

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2068165A2 (fr) * 2007-12-07 2009-06-10 Honeywell International Inc. Système de navigation doté d'un appareil pour détecter les défauts de précision

Non-Patent Citations (8)

* Cited by examiner, † Cited by third party
Title
GREG WELCH; GARY BISHOP: "An Introduction to the Kalman Filter", UNC-CHAPEL HILL, 2006
J. CHRIS MCGLONE: "Manual of Photogrammetry", 2004, AMERICAN SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING, article "Weighted Least Squares Estimator'' (WLSE). Eine genauere Beschreibung der WLSE kann"
J. CHRIS MCGLONE: "Manual of Photogrammetry", 2004, article "American Society for Photogrammetry and Remote Sensing"
J. WENDEL: "Integrierte Navigationssysteme", 2007, VERLAG
J.K. UHLMANN: "Covariance consistency methods for fault-tolerant distributed data fusion", INFORMATION FUSION, 2003
JULIER S J ET AL: "An empirical study into the robustness of split covariance addition (SCA) for human motion iracking", AMERICAN CONTROL CONFERENCE, 2004. PROCEEDINGS OF THE 2004 BOSTON, MA, USA JUNE 30-JULY 2, 2004, PISCATAWAY, NJ, USA,IEEE, vol. 3, 30 June 2004 (2004-06-30), pages 2190 - 2195, XP010761146, ISBN: 978-0-7803-8335-7 *
S. BOYD; L. VANDENBERGHE: "Convex Optimization", 2008, CAMBRIDGE UNIVERSITY PRESS
S.J. JULIER; J.K. UHLMANN: "A Non-divergent Estimation Algorithm in the Presence of Unknown Correlations", PROCEEDINGS OF THE AMERICAN CONTROL CONFERENCE, ALBUQUERQUE NEW MEXICO, 1997

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US12061277B1 (en) * 2023-08-10 2024-08-13 Beihang University Airborne positioning method in aviation navigation network based on multi-source information fusion

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DE102011005584A1 (de) 2012-09-20

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