EP1020699B1 - Missile - Google Patents

Missile Download PDF

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Publication number
EP1020699B1
EP1020699B1 EP99124091A EP99124091A EP1020699B1 EP 1020699 B1 EP1020699 B1 EP 1020699B1 EP 99124091 A EP99124091 A EP 99124091A EP 99124091 A EP99124091 A EP 99124091A EP 1020699 B1 EP1020699 B1 EP 1020699B1
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EP
European Patent Office
Prior art keywords
missile
missile according
sensors
reconfiguration
monitoring
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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.)
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EP99124091A
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German (de)
English (en)
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EP1020699A1 (fr
Inventor
Uwe Dr. Krogmann
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Diehl BGT Defence GmbH and Co KG
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Diehl BGT Defence GmbH and Co KG
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F41—WEAPONS
    • F41F—APPARATUS FOR LAUNCHING PROJECTILES OR MISSILES FROM BARRELS, e.g. CANNONS; LAUNCHERS FOR ROCKETS OR TORPEDOES; HARPOON GUNS
    • F41F3/00—Rocket or torpedo launchers
    • F41F3/04—Rocket or torpedo launchers for rockets
    • F41F3/055—Umbilical connecting means
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F41—WEAPONS
    • F41G—WEAPON SIGHTS; AIMING
    • F41G7/00—Direction control systems for self-propelled missiles
    • F41G7/20—Direction control systems for self-propelled missiles based on continuous observation of target position
    • F41G7/22—Homing guidance systems
    • F41G7/2246—Active homing systems, i.e. comprising both a transmitter and a receiver
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F42—AMMUNITION; BLASTING
    • F42B—EXPLOSIVE CHARGES, e.g. FOR BLASTING, FIREWORKS, AMMUNITION
    • F42B15/00—Self-propelled projectiles or missiles, e.g. rockets; Guided missiles
    • F42B15/01—Arrangements thereon for guidance or control
    • F42B15/04—Arrangements thereon for guidance or control using wire, e.g. for guiding ground-to-ground rockets

Definitions

  • Electrodes may be sensors, signal processing means or actuators.
  • the term “elements” should also include information that is essential to the function of the missile.
  • agents which are e.g. to increase the safety of manned aircraft during their relatively long-term use per se, used for a different purpose, namely to increase the above-defined "availability" of missiles that certainly do not require such measures during their brief deployment would.
  • a high reliability and availability of the missile is achieved by fault tolerance.
  • a fault-tolerant system has the ability to fully perform its specified function even in the event of hardware or software failure.
  • error subsumes: faults in the hardware and software, resulting errors in the information world (“errors”) and, finally, resulting failures or malfunctions in the system (“ failures ").
  • a “fault” can be a defect in a sensor.
  • the fault leads to a false signal ("error").
  • error leads to a malfunction of the missile.
  • Embodiments of the invention are the subject of the dependent claims.
  • Fig.1 10 denotes a missile.
  • the missile 10 includes a seeker head 12 in its nose.
  • the nose has a Radar Radiation Permeable Tip (RADOME) 14 and an Infrared Radiation Permeable Window 16.
  • Behind the radiotransparent tip 14 are an X-band passive array sensor 18 and a K-band phased array sensor 20.
  • the X-band passive array sensor 18 is connected to associated tracking circuitry 22.
  • the K-band phased array sensor 20 is connected to associated tracking electronics 24.
  • the infrared sensor 26 is followed by an associated tracking electronics unit 28.
  • the tracking electronics 22, 24, and 28 receive data from a strapdown finder and navigation signal processor 30.
  • the strapdown seeker and navigation signal processor 30 receives data from an inertial measurement unit and satellite navigation, represented by a block 32.
  • the proper movements of the missile are taken into account in the signals of the "strapdown" sensors.
  • navigation data are obtained, ie data on the position and possibly speed of the missile.
  • the outputs of the tracking electronics 22, 24 and 28 are connected to means 34 for sensor fusion. Become through the sensor fusion Get seeker signals based on the signals from multiple sensors. These seeker signals are applied to a flight control signal processor 36.
  • the flight guidance signal processor 36 also receives data from the strapdown finder and navigation signal processor 30.
  • signals from a data transmission unit 38 may be applied to the flight guidance signal processor 36.
  • the flight guidance signal processing 36 is in data exchange with a missile-side mission unit 40.
  • the flight guidance signal processing 36 effects flight guidance in the marching phase and in the final phase.
  • the flight guidance signal processing provides commands to an advanced autopilot 42.
  • the autopilot 42 may continue to receive data from the communication unit 38.
  • the autopilot 42 provides commanding commands for the actuation of control surfaces and for the thrust at outputs 44 and 46, respectively a circuit 48 is provided, which is acted upon by data of the means 34 for sensor fusion and by which a seeker-based ignition command is generated, which is connected via an output 50 to a warhead of the missile 10 is before firing via an interface 52 and a feed cable 54 with connected to the missile 10 carrying carrier aircraft, which is shown in Figure 1 by a block 56 in the left part of the figure.
  • the interface 52 communicates with the missile side mission unit 40, the signal processing 30, the flight guidance signal processing 36 and the autopilot 42 in data exchange.
  • the carrier aircraft 56 also has sensors represented by blocks 58, 60, 62, 64, 66 and 68, eg a radar 58, a forward infrared sensor FLIR 62 or sensors for satellite or inertial navigation GPS / INS 68.
  • the sensors are switched to a mission avionics 70 of the carrier aircraft 56. Based on the sensor signals, a target is identified, identified and tracked.
  • the missionary avionics provides data to mission planning means 72, namely means for the fire control solution and the tactical dynamics.
  • the mission avionics 70 provides data about the location, type and movement of the target.
  • the mission planning resources 72 provide data on how to tackle this goal. Via the interface 52 in the launcher, the mission avionics 70 receives information from the missile 10, which already provides information with its sensors while it still hangs in the launcher of the carrier aircraft.
  • the mission avionics 70 and the mission planning means 72 may continue to communicate wirelessly with the missile in two directions via data transmission 38.
  • Block 74 symbolizes other contributors, e.g. a command center or other friendly aircraft through which a target can be made.
  • the "target by a third party" is symbolized by the block 76 in FIG.
  • the target can be transmitted via data transmission 38 to the missile 10 or to the carrier aircraft 56.
  • the sensors of the missile can be subdivided into sensors for measuring the self-propelled state of the missile and sensors for target detection and target tracking.
  • the self-motion of the missile is detected with inertial sensors for measuring acceleration and rotational speed. Furthermore, satellite navigation systems such as GPS ("Global Positioning System”) are used in some cases for accurate positioning. This is the element 32 in Fig.1.
  • GPS Global Positioning System
  • Searchers preferably image-resolving viewfinders whose signals are subjected to image processing, serve for target detection and target tracking. These are e.g. image-processing radar seekers such as element 18 in FIG. 1 or infrared seekers such as element 26 in FIG.
  • the viewfinders can work passively or actively. In the case of an infrared finder, a distance measurement in the final phase can also take place by means of a laser.
  • Fault tolerance is achieved by redundancy of sensors. If errors occur in the sensors, which can be detected and identified as a result of redundancy, the sensors are reconfigured: the signals from defective sensors are ignored and the required information is obtained from the signals of other sensors and possibly combinations of such signals.
  • the inertial sensors are multiplied.
  • the degradation behavior "fail-operational" ie a functionality even after failure of any Sensors, each with a minimum of five (instead of three otherwise) rotational rate and acceleration sensors, which are arranged in a special geometry to the missile axes.
  • the rotation rate sensors can be optical rotation rate sensors. It is also possible to use micro-mechanical sensors (EP 0 686 830 B1) for rotation rates and accelerations. The additional effort for the redundancy is thereby relatively low, so that it is outweighed even with loss objects such as missiles by increasing the availability and lower maintenance.
  • Satellite navigation systems may be multi-channel (multi-channel) GPS systems so that redundant position information is also available from the satellite navigation system that can be used to monitor and select the currently most favorable channels.
  • motion information can also be obtained from the signals of the infrared and radar sensors.
  • FIG. 2 schematically shows the monitoring of the functionally important elements of the missile 10.
  • 80 generally designates the device for detecting and identifying faults and for reconfiguring the elements of the missile 10 (FDIR).
  • Monitored are sensors, represented by a block 82, the information and data processing represented by a block 84 and the actuators, represented by a block 86.
  • a block 88 symbolizes a built-in test procedure (BIT), by which the physical function of the individual components is checked. It is checked, for example, whether a motor winding of a gyro receives power. Subsequently, the test is for errors by the FDIR.
  • BIT built-in test procedure
  • inference and status means 90 "If the signal from sensor A is detected as faulty, discard the signal from sensor A and process only the signals from sensors B and C". Status: failure of sensor A.
  • the built-in test procedure can additionally show that sensor X is not receiving power. This also gives a status signal: "Failure of Sensor X, working with sensor Y, which provides similar information. "The status signals are applied to the missile mission unit 40.
  • FIG. 3 schematically shows the structure of the FDIR device 80 for error detection, identification and reconfiguration.
  • 90A to 90N sensors of the missile 10 are designated, whose signals or data are applied to the device 80.
  • the connection is represented by double arrows.
  • the FDIR device 80 effects monitoring of the hardware and subsystems of the missile 10. This is represented by block 92. This is done once by "voting" and plausibility checks: If a sensor is designed to be triple-redundant and one measured value deviates significantly from the measured values of the other two, then this deviating measured value is considered to be incorrect. It counts the majority. A plausibility check checks whether a received measured value is physically meaningful. This type of monitoring is shown in FIG. 3 by a block 94. Furthermore, the built-in test procedures (BIT), which are represented by a block 88. This is a way of checking the hardware and subsystems.
  • BIT built-in test procedures
  • a block 98 Another type of hardware and subsystem verification is represented by a block 98.
  • the signals are checked for the occurrence of errors by means of knowledge, model, pattern or parity-based processing.
  • the errors are detected and identified. This is shown in Fig. 3 by block 100 (FDI).
  • effectors and actuators 108 are actuated. Again, the transfer of information takes place in two directions, which indicated by a double arrow. The function of the effectors and actuators is monitored in the manner described by the FDIR device.
  • Block 110 represents the communication of FDIR device 80 e.g. with the missile mission unit 40, this communication also being in two directions.
  • Block 112 symbolizes the human / machine interface.
  • 90 generally designate the sensors of the missile.
  • These sensors include redundant inertial sensors 114, a multi-channel receiver for satellite navigation (multichannel GPS with 6 -10 channels) 116, and infrared and radar sensors 118.
  • the multi-channel GPS provides redundant GPS information that can be used to monitor and select the currently most affordable GPS channels.
  • the infrared and radar sensors 118 also provide motion information.
  • Block 120 represents the formation of location and velocity data, which are collectively obtained from the GPS data and the data from the inertial sensors. It can be a Kalman filter or a State Dependent Riccati Equation (SDRE) filter that optimally integrates GPS and inertial data. The output of this block is the measured (not the calculated) covariance signals of the Kalman filter or SDRE filter.
  • SDRE State Dependent Riccati Equation
  • the output signals of the sensors 114, 116 and 118 are applied in a measurement vector m to an array 122 of neural networks 124 and 126.
  • the measuring vector m comprises the output signals of the sensors 114, 116, 118.
  • the error vector ⁇ may include jump or ramp-shaped as well as stochastic errors and continue to map total failures such as zero measurement signal or constant full scale deflection.
  • the measuring vector m is applied to the first neural network 124.
  • a separation of the useful signal m (x) and the error m ( ⁇ ) is performed by projecting the measuring vector m into the error space (parity space) orthogonal to the measuring space. This generates a feature or validation vector v .
  • the validation vector v is subject to errors. This is an error detection.
  • the feature vector v is applied to the second neural network 126.
  • This second neural network 126 effects the location of the error.
  • the second neural network 126 thus identifies the defective sensor or channel.
  • this neural network 126 has the function of a classifier related to the error space in which the feature vectors point to certain error clusters.
  • the networks 124 and 126 are provided with prior knowledge of an analytical solution to the problem of error detection and identification and then trained in a training phase either by simulation of the sensor array and typical errors or with the real sensor array 90 with simulated errors. It has been found that the networks 124 and 126 can detect and locate not only single errors but also simultaneous errors and short consecutive errors. Moreover, they detect and locate the disappearance of transient errors, e.g. As a result of strong maneuvers occur, so that the sensor assembly 90 can work self-regenerating.
  • Output of the second network 126 is a location, identification and classification vector. If necessary, this vector is supplied to a decision network 128 together with the feature vector v . This is illustrated by connections 130 and 132, respectively.
  • Decision network 128 is further provided with the output of Kalman filter or SDRE filter 120.
  • Kalman filter or SDRE filter 120 In the covariances of the measuring difference form both measurement errors and sensor failures. The progressions of these signals can therefore be usefully included in the decision as to whether and where errors have occurred.
  • Denoted 130 is a Kalman filter intended for initialization and calibration of the Missile Inertial System (MLS-IRS).
  • the Kalman filter processes information from the aircraft inertial system as well as the missile inertial system.
  • the covariance signals provided by the Kalman filter 130 are a measure of the function and momentary quality of the MLS-IRS missile inertial system. These covariance signals are also applied to the decision network 128 via connection 132.
  • Block 134 symbolizes rule-based heuristic knowledge about the sensors 114, 116, 118 and their interaction in the system. This is also switched to the decision network 128.
  • the decision network 128 includes a fuzzification layer 136 on the input side. This is followed by a control layer 138 and an inference layer 140. On the output side, a defuzzification layer 142 is provided. Decision network 128 provides a sensor status vector.
  • the task of the decision network is to extract from the analytic sympetome signals, e.g. identify the identification vector and rule-based heuristic knowledge of the sensors concerned and their interaction in the system with possible "faults" according to the type, location and time of occurrence. For this a uniform presentation of the symptoms is important. This is made possible by the fuzzy logic by uniformly representing both analytical and heuristic symptoms through membership functions to fuzzy sets for decision making.
  • the signals supplied to decision network 128 are stochastic variables with mean and variance.
  • An example of the time course of such a variable S is shown by curve 144 in the left part of FIG.
  • the variable S usually fluctuates around the Line 146 with the variance ⁇ .
  • ⁇ S the waveform shifts by ⁇ S on line 148.
  • the function 154 shows a ramp 156 which starts from the S value of the line 148 and from S max up constant "1" remains.
  • the functions intersect at a function value of 0.5.
  • the function 150 is associated with the linguistic variable "normal”, the function 152 with the linguistic variable "low” (fault) and the function 154 with the linguistic variable "increased”.
  • a particular value S of the variable leaves may then be assigned to a certain percentage, eg the linguistic variable "normal” and to a different percentage of the linguistic variable "low”.
  • FIG. 6 shows an example in which the increase or decrease of a symptom or feature forms a criterion for the occurrence of a "faults" and is described by introducing linguistic value ranges (fuzzy sets), wherein each of these value ranges is assigned a "membership function” is.
  • FIG. 6 shows the membership functions as degree ⁇ (S) of the membership of a value S to a range of values. ⁇ (S) is again one maximum.
  • the ranges of values overlap, so that a particular value S may belong to different adjacent value ranges in different degrees determined by the membership functions.
  • the "fuzzification” gives the decision network 128 at the various inputs from the identification network 126, the Kalman filters 120 and 130 and the heuristic knowledge 134 comparable inputs, namely degrees of membership between zero and one to linguistic value ranges.
  • FIG 7 shows in another illustration the decision network 128 and the signal processing then taking place.
  • Block 172 symbolizes an input interface for the fuzzification of the input variables that are applied to inputs 174. These are operations as described in connection with FIGS. 5 and 9.
  • the fuzzification is indicated at the bottom of Fig. 7 by block 176.
  • the linguistic quantities thus obtained are displayed in block 178.
  • Block 178 contains a rule database 180.
  • the rule database contains rules of the form. "If ..., then ", according to which the linguistic quantities are linked in a inference stage 182. This is indicated at the bottom in FIG. 7 by block 184.
  • Block 188 represents an output interface for defuzzification. This output interface also receives the linguistic value ranges and membership functions stored in database 170 via connection 190. The defuzzification is indicated at the bottom of FIG. The output interface supplies a sensor status vector at an output 194 as a "hard" output, with the aid of which the signals of the respectively intact sensors can be reconfigured for further processing in the system.
  • the FDIR sensor concept described here is a parity-vector-based, feature-based, knowledge-based method. It does not require any more or less complex sensor or subsystem modeling. It also recognizes the ability to detect and locate simultaneous or rapid errors, with the potential for self-regeneration.
  • the three networks necessary for the realization can be implemented in hardware as ASICS, so that problems with software reliability can be avoided at this point. Due to the hardware realization, the networks can be produced quickly and, because of the parallel structures, fault-tolerant as well as cost-effectively. They are adaptable to change through learning, not reprogramming.
  • the described FDIR concept requires similar or dissimilar sensor redundancy. In its basic structure, it can also be used for multi-sensor systems for time recording and for non-redundant sensor configurations. However, the latter requires analytical redundancy with model-based approaches, which in turn can be based on knowledge-based representations.
  • the processor and memory hardware is monitored using standard, proven BIT resources. Fault tolerance can basically be implemented by multiplication with mechanical and electrical segregation in conjunction with voting / monitoring techniques. Due to the reliability of the relevant hardware components even during long storage, this measure can be dispensed with to ensure availability.
  • Block 196 symbolizes the mission control software 40 (Fig.1), i. determines what the missile should do, e.g. Detect, identify and track a specific destination.
  • Block 198 symbolizes the software of the sensor subsystem viewfinder. This is a signal processing of the viewfinder and possibly inertial signals through which the viewfinder follows the specific destination.
  • Block 200 represents the software of the midcourse and end-phase flight guidance 36 (Fig. 1) through which the missile is guided to the destination in accordance with the seeker signals.
  • block 202 represents the software of the autopilot 42, which steers the missile in accordance with the flight guidance 36.
  • FIG. 9 shows a circuit which, with the outlay for missiles, achieves a tolerance for software errors and thus improves the availability of the missile.
  • Block 206 symbolizes other input data.
  • the data records are connected in parallel to at least two computer channels 208 and 210.
  • the computer channels 208 and 210 deliver the output data p n and p n-1 respectively in the right cycle.
  • Each computer channel operates with a nominal software N and a monitor version M of the nominal software.
  • the two computers work with a time offset of one right clock.
  • the computer 208 processes in the time clock n the data accumulating in this time cycle.
  • the computer 210 processes at the same time the data that had been incurred in the previous time clock n-1. However, these data have already been processed by the computer 208 in the preceding time clock and once by the nominal software N and secondly by the monitor software M. If the computer 208 has come to the same result in the two channels with different software N and M. which is referred to here as p n ; then the software is "validated" for this set of input variables. If this set of input variables is switched one clock further on the computer 210, then this calculator with nominal software and monitor software must deliver the same output. This allows control of the function of computers 208 and 210. One has the certainty that the software is working properly. Deviations are then due to an error of one of the computers.
  • the software It is also possible to control the software. If the output variables calculated with the nominal software N and the monitor software M deviate in the two channels of the computers 208 and 210, then there is a software error. However, it can not be said which software encountered this error.
  • the monitoring of the software is based on the consideration that the output variables of the computer, for example, a commanded control surface position, constantly changing and make no jumps. From the history of an output in the past, the value of the output in the next clock can be predicted with some accuracy. A significant deviation of the output from the predicted value in one channel while the output in the other channel close to the predicted value, indicates a software error in this channel.
  • Fig.10 This is shown in Fig.10 as a flow chart.
  • An oval 212 indicates the input data for the nth clock cycle.
  • the vector p at time nT, ⁇ p n N calculated. This is represented by the rectangle 214.
  • the vector p at time nT ⁇ p n M ⁇ calculated. This is represented by the rectangle 216. From the vectors of the output variables thus calculated with the two programs N and M, the difference is formed ⁇ p ⁇ n p ⁇ n N - p ⁇ n M ,
  • an output vector p n is formed. This is represented by a rectangle 228.
  • This output vector p n is the vector which is switched to an output 230 in consideration of the software monitoring and is used further.
  • the monitor or monitoring software M should use simple algorithmic and logical elements (standard modules), which can be arranged in generally validatable structures arranged a variety of (software) problem solutions. Thus, simple algorithmic and logical elements are provided. These elements will be combined to a specific problem solving in the problem adapted structures. These conditions ideally fulfill neural networks and fuzzy neural networks. These networks define a given standard structure with uniform, simple processor units.
  • the free parameters of the structure are set in a training phase.
  • input and output vectors of the input or output spaces of the respectively associated nominal software module are used as training data. It is possible by means of genetic evolutionary algorithms to optimize the structure and / or parameters according to predetermined criteria. This allows automatic generation of the monitor software. For the likewise automatic test of the generated software, other than the training vectors from the input and output spaces are used.
  • Block 232 symbolizes a neural network usable in the manner described above.
  • Block 234 symbolizes a correspondingly usable fuzzy-neural network.
  • block 236 symbolizes a genetic algorithm.
  • the neural network 232 and the fuzzy neural network 234 provide a standard representation. This results in training of structure parameters by training with input and output data (“data driven") represented by a memory 238. This type of structuring also has potential for genetic / evolutionary optimization of the structure and / or parameters. This is illustrated in FIG. 11 by a block 240. This results in an automatic generation of the monitor software. This is represented by block 242 in FIG. For testing the monitor software thus obtained, input and output vectors of the input and output spaces, which are different from the training vectors and also stored in memory 238, are used.
  • This method is quite generally applicable to the automatic generation of software modules.
  • the monitor software is generated on the basis of a neural or fuzzy-neural network structure.
  • the monitor software M can be implemented as a hardware module, for example in the form of an ASIC. This has several advantages: The reliability of the module can be assessed according to the laws of hardware failure. It does not use software to monitor software. The parallel information processing in the hardware provides - within certain limits - an inherent fault tolerance of the module.
  • the nominal home goods can also be replaced by hardware modules that are identical in terms of the input and output. These hardware modules can be multiply where the inherent fault tolerance to achieve the required reliability and thus availability is insufficient. This would be omitted monitor modules.
  • the software is modeled on hardware modules of high reliability, whereby the adaptation to different tasks is done by learning.
  • a missile may comprise a plurality of actuators. This is shown in FIG. 12.
  • the missile 10 may include a transverse thruster 244 that exerts transverse thrust on the missile 10 as shown. This causes a lateral acceleration of the missile 10.
  • Other actuators may be control surfaces or rudder 246.
  • a rudder deflection, as shown, causes an angle of attack, which also leads to a lateral acceleration.
  • the thrust vector of the engine may be varied by adjusting the engine nozzle or by jet vanes projecting into the engine jet, as shown.
  • a missile may have all three types of actuators. But it is then not possible in case of failure of an actuator to influence the movements of the missile by the remaining actuators in the desired manner.
  • the positioning system of the missile is therefore a "simplex configuration". There is no possibility for a reconfiguration, as in the manner described above, for example at
  • External faults are e.g. Damage due to collision or fault in the power supply.
  • Internal faults are e.g. Failure of gearbox, bearings, insufficient lubrication or component failure.
  • the first monitoring may be performed on the actuator 250 by measuring by measuring certain signals, e.g. of the input current or the deflection, and comparison of these signals with fuzzy predetermined tolerance thresholds in the manner of Figure 5 is made a general statement about the function of the actuator.
  • FIG. 13 An improvement of the error detection and above all the possibility of a localization can be realized with the help of knowledge-based elements.
  • FIG. 13 neuronal or fuzzy-neuronal nonlinear dynamic state and parameter models are assumed.
  • 252 denotes a neural network representing such a model.
  • 254 denotes a fuzzy neural network, which also represents such a model.
  • the models from the networks 252 and 254 are connected to a knowledge-based nonlinear estimator 256 which receives data from the actuator 250 for comparison.
  • the estimator 256 may also include further inputs from the flight guidance and control system of the missile 10 switched as indicated by input 258.
  • the estimator 256 provides in real time estimates of the state variables x and the parameter p of the actuator. If errors occur, these estimated values contain the error components ⁇ x and ⁇ p .
  • a statement about the status of the actuators can be continuously made by means of a fuzzy decision and inference unit 160.
  • an inference and status unit 90 is provided to which the results of sensor, information, data processing and actuator monitoring as well as the results of the built-in test (BIT) are fed.
  • the unit 90 generates therefrom by inference and inference processes with fuzzy logic information regarding the functional status of the missile and its essential functional parts and thus about its availability.

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  • Engineering & Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Aiming, Guidance, Guns With A Light Source, Armor, Camouflage, And Targets (AREA)
  • Feedback Control In General (AREA)

Claims (23)

  1. Corps volant qui forme une partie d'un système d'armement et qui est acheminé jusqu'à une cible à l'aide de signaux de capteurs et/ou de récepteurs en un temps de vol court en comparaison des temps de fonctionnement des aéronefs ou des vaisseaux spatiaux, avec lequel
    a) les éléments du corps volant sont prévus redondants et
    b) le corps volant présente des moyens de détection des défauts,
    caractérisé en ce que
    c) pour augmenter la disponibilité du corps volant (10), les éléments importants pour le fonctionnement du corps volant (10) sont prévus redondants et le corps volant (10) présente des moyens internes (80) pour la détection et la localisation des défauts et pour la reconfiguration desdits éléments de telle sorte qu'en cas de défaut de l'un des éléments, le guidage du corps volant est automatiquement réalisé par les éléments reconfigurés.
  2. Corps volant selon la revendication 1, caractérisé en ce que les moyens de détection et de localisation des défauts et de reconfiguration contiennent des moyens basés sur des connaissances (98)..
  3. Corps volant selon la revendication 1 ou 2, caractérisé en ce que les moyens de détection et de localisation des défauts et de reconfiguration contiennent des moyens basés sur des modèles (98).
  4. Corps volant selon la revendication 1 ou 2, caractérisé en ce que les moyens de détection et de localisation des défauts et de reconfiguration contiennent des moyens basés sur des exemples (98).
  5. Corps volant selon la revendication 1 ou 2, caractérisé en ce que les moyens de détection et de localisation des défauts et de reconfiguration contiennent des moyens basés sur la parité (98).
  6. Corps volant selon l'une des revendications 1 à 5, caractérisé en ce que les moyens de détection et de localisation des défauts et de reconfiguration contiennent des moyens (94) de surveillance des matériels redondants par observation multiple.
  7. Corps volant selon l'une des revendications 1 à 6, caractérisé en ce que les moyens de détection et de localisation des défauts et de reconfiguration contiennent des moyens (88) de surveillance des matériels redondants par un contrôle direct du fonctionnement.
  8. Corps volant selon l'une des revendications 1 à 7, caractérisé en ce que les moyens de détection et de localisation des défauts et de reconfiguration contiennent des moyens (94) de surveillance des matériels par observation des plausibilités.
  9. Corps volant selon l'une des revendications 1 à 8, caractérisé en ce que les moyens de détection et de localisation des défauts et de reconfiguration contiennent des moyens (102) de surveillance des logiciels opérationnels.
  10. Corps volant selon l'une des revendications 1 à 9, caractérisé en ce que
    a) sont prévus des capteurs (114 ; 116) destinés à mesurer l'état de déplacement propre du corps volant (10) lui-même et des capteurs (118) pour la détection et le suivi de la cible de telle sorte que les informations sur l'état de déplacement propre du corps volant (10) soient obtenues de manière redondante,
    b) les moyens de surveillance du fonctionnement contiennent des moyens (106) de reconfiguration de telle sorte que lorsqu'une information d'un capteur devient inutilisable, cette information est obtenue à partir des informations des autres capteurs.
  11. Corps volant selon la revendication 10, caractérisé en ce que sont prévus des capteurs inertiels (114) multiples.
  12. Corps volant selon la revendication 10 ou 11, caractérisé en ce que le corps volant contient un récepteur de navigation par satellite à plusieurs canaux (116).
  13. Corps volant selon l'une des revendications 10 à 12, caractérisé en ce que sont prévus des moyens (36) pour générer des informations de mouvement à partir des données des capteurs à infrarouge et radar détectant la cible.
  14. Corps volant selon l'une des revendications 1 à 13, caractérisé en ce qu'un réseau neuronal à logique floue (128) est prévu pour surveiller les erreurs des capteurs.
  15. Corps volant selon l'une des revendications 1 à 14, caractérisé en ce que
    a) un vecteur de mesure (m) formé par les signaux des capteurs redondants est connecté à un réseau neuronal de projection et de détection (124) par le biais duquel est effectué une séparation du signal utile et du défaut par projection du vecteur de mesure (m) dans l'espace de défaut (espace de parité) orthogonal par rapport à l'espace de mesure et un vecteur caractéristique (v) est ainsi produit sur lequel sont représentées les erreurs,
    b) le vecteur caractéristique (v) est connecté en tant que grandeur d'entrée à un réseau neuronal d'identification (126) par le biais duquel a lieu une localisation de l'erreur survenue.
  16. Corps volant selon les revendications 14 et 15, caractérisé en ce que
    a) le vecteur caractéristique (v) est connecté à un réseau de décision (128),
    b) le réseau de décision (128) est un réseau neuronal à logique floue comprenant une couche de conversion en logique floue (136), une couche de régulation (138), une couche d'interférence (140) et une couche de conversion depuis la logique floue (142) et
    c) le réseau de décision (128) délivre un vecteur d'état de capteur.
  17. Corps volant selon la revendication 16, caractérisé en ce que le vecteur caractéristique (m) est lui aussi connecté au réseau de décision (128).
  18. Corps volant selon l'une des revendications 1 à 17, caractérisé en ce que pour surveiller le logiciel
    a) les données à calculer sont calculées une fois avec un programme principal (N) et une fois avec un programme de surveillance (M),
    b) les données prédits sont calculées par extrapolation à partir des données déjà calculées,
    c) la différence entre les données prédits et les données réellement fournies par le programme est déterminée et
    d) cette différence est connectée à une logique de décision à logique floue (224) qui délivre un vecteur d'état de logiciel.
  19. Corps volant selon la revendication 1, caractérisé en ce que
    a) les données d'entrée sont traitées en parallèle par deux canaux de calcul, à chaque fois au moyen d'un programme principal et d'un programme de surveillance,
    b) les deux canaux de calcul fonctionnent avec un décalage dans le temps d'au moins un cycle de calcul et
    c) sont prévus des moyens de comparaison des valeurs de sortie calculées obtenues dans les deux canaux de calcul, un défaut du matériel dans l'un des canaux de calcul étant supposé en cas de concordance des valeurs de sortie obtenues avec le programme principal et de surveillance dans le canal de calcul direct et d'écart dans les valeurs de sortie obtenue avec les mêmes valeurs d'entrée dans les deux canaux de calcul.
  20. Corps volant selon la revendication 18 ou 19, caractérisé en ce que le programme de surveillance (M) est réalisé sous la forme d'une logique de décision floue dont la structure et/ou les paramètres sont générés par des algorithmes génétiques ou évolutifs.
  21. Corps volant selon la revendication 20, caractérisé en ce que la logique de décision floue est réalisée sous la forme d'un module matériel.
  22. Corps volant selon l'une des revendications 1 à 21, caractérisé en ce que sont prévus des moyens (86) de détection des défauts pour les éléments de réglage.
  23. Corps volant selon l'une des revendications 1 à 22, caractérisé par des moyens (90) de production d'un signal d'état du corps volant.
EP99124091A 1998-12-15 1999-12-13 Missile Expired - Lifetime EP1020699B1 (fr)

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DE19857894 1998-12-15
DE1998157894 DE19857894A1 (de) 1998-12-15 1998-12-15 Flugkörper

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DE102017210151A1 (de) * 2017-06-19 2018-12-20 Zf Friedrichshafen Ag Vorrichtung und Verfahren zur Ansteuerung eines Fahrzeugmoduls in Abhängigkeit eines Zustandssignals
EP3428578A1 (fr) * 2017-07-12 2019-01-16 MBDA Deutschland GmbH Système capteur inertiel pour engin balistique

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DE10158666A1 (de) * 2001-11-28 2003-06-18 Lfk Gmbh Vorrichtung und Verfahren zur autarken Zielführung eines Flugkörpers mit Hilfe außerhalb des Zielpunktes liegender Orientierungsmerkmale
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DE102008041571B4 (de) 2008-08-26 2019-12-05 Robert Bosch Gmbh Verfahren und Vorrichtung zur Fehlerbehandlung beim Betrieb eines Verbrennungsmotors
WO2014012776A1 (fr) * 2012-07-17 2014-01-23 Siemens Aktiengesellschaft Reconfiguration automatisée d'un circuit de réglage à événements discrets
DE102014213171A1 (de) * 2014-04-09 2015-10-15 Continental Automotive Gmbh System zur autonomen Fahrzeugführung und Kraftfahrzeug
CN113360841B (zh) * 2021-05-19 2022-05-03 电子科技大学 一种基于监督学习的分布式mimo雷达目标定位性能计算方法
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DE102017210151A1 (de) * 2017-06-19 2018-12-20 Zf Friedrichshafen Ag Vorrichtung und Verfahren zur Ansteuerung eines Fahrzeugmoduls in Abhängigkeit eines Zustandssignals
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DE59913732D1 (de) 2006-09-14
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