EP1174675A1 - Lenkstruktur für Flugkörper - Google Patents
Lenkstruktur für Flugkörper Download PDFInfo
- Publication number
- EP1174675A1 EP1174675A1 EP01730001A EP01730001A EP1174675A1 EP 1174675 A1 EP1174675 A1 EP 1174675A1 EP 01730001 A EP01730001 A EP 01730001A EP 01730001 A EP01730001 A EP 01730001A EP 1174675 A1 EP1174675 A1 EP 1174675A1
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- European Patent Office
- Prior art keywords
- missile
- steering
- model
- target
- vector
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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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Classifications
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- 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
Definitions
- the invention relates to a steering structure for steering with a sensor system provided missile to a target, with a measurement vector of observable quantities can be derived.
- a missile should aim for a target, e.g. an enemy aircraft to be destroyed be performed.
- the missile has a seeker head with or several sensors.
- the sensors detect a target. From the signals of this Sensors are derived from steering signals which guide the missile to the target.
- Known missiles have e.g. an image-resolving infrared detector, which consists of a two-dimensional arrangement of detector elements. Through a imaging optical system becomes an object scene containing the target on the Infrared detector shown. The infrared detector thus observes a thermal image, in which e.g. the hot engines of an airplane and their exhaust gas jet as intense Radiation source appear. Through image processing, the Infrared detector delivered "electronic image" the position of the target in the visual field determined.
- the seeker head is stabilized in space and by the Missile movements decoupled.
- the search head can also with the Infrared detector rigidly sitting in the missile, being also rigid with the cell of the Missile connected (“strap-down") inertial sensors the decoupling of the electronic image of the movement of the missile.
- the search head is usually tracked real or virtual to the target. From this The line rotation rate is determined by tracking. The steering takes place at the "Proportional navigation" so that the lateral acceleration of the missile is proportional this line of sight rotation rate is.
- the goal e.g. an attacked aircraft usually also has sensors which recognize the attack by an approaching missile.
- the target hits "Countermeasures" that seek to disrupt the approaching missile or distract.
- the attacked aircraft is made by suitable maneuvers seek to escape the approaching missile.
- the movement of the Missile itself determined and considered by an inertial measuring unit.
- the Movement quantities of the target are unknown.
- the expected behavior of the Target such as whether the target performs an escape maneuver and how this escape maneuver the missile does not "know”.
- the missile only detects the line of sight to the Target, if necessary distance and change of distance and reacts to their changes.
- the invention is based on the object, the hit probability when used to improve a missile against highly agile targets.
- the invention is based in particular on the task for the control of Missile the "encounter kinematics", ie the movements of the missile and target consulted.
- a model of both the Missile and the sensor system and the target shown.
- the sensor system returns a real measurement vector.
- the measurement vector results from the real one Relative kinematics of missile and target.
- the real measurement vector is shown with a estimated measurement vector, which is derived from the model of missile, target and Sensor system results.
- the real and the estimated measurement vector are usually do not match, especially since the movement states of the target are not known are. Therefore an optimal filter, e.g. a Kalman filter, an extended Kalman filter or the state-dependent Riccati equation SDRE, from the deviations of the estimated measurement vector acted upon by the real measurement vector and modified Missile target model, so changes its parameters, in the sense, this To reduce deviation and to align the measurement vectors with each other.
- the relative kinematics vector is advantageously based on an optimal steering law the missile's autopilot.
- a commanded lateral acceleration a MC is applied to the autopilot.
- the commanded lateral acceleration depends on the relative kinematics vector according to a function f ( x and k ).
- This function f ( x and k ) represents the steering law and is selected so that there is an optimal control behavior.
- the model of the missile and target can be improved in that the signals of an inertial measuring unit of the missile are also applied to the model.
- the movements of the missile in inertial space can be measured.
- the determination of the missile movement states in the relative kinematics x M is improved by the connection of these measured variables to the model.
- a further improvement of the modeling can be achieved by estimating means for estimating the target acceleration to which the measurement vector, image data from a image-resolution detector of the missile and data of the inertial measurement unit of the Missile are activated and by which the model can be corrected.
- the estimating means are expediently a suitably trained neural network or a fuzzy neural network.
- the target is no longer a point target in the distance range of interest here.
- On image-resolving detector "sees" a contour in the image of the target.
- the Airplane With an airplane as The goal is lateral acceleration combined with a change in the contour.
- the Airplane must be in an inclined position and / or changes in position around the Go vertical axis. Taking these contour changes into account, if necessary other observed variables such as the distance to the lateral acceleration getting closed.
- a human pilot can do this based on his experience estimated. Accordingly, a neural network trained on it can do the same except for the image data from the measurement vector and taking into account the Own movement of the missile - from the signals of the inertial measuring unit of the Missile is affected.
- Modeling can also be improved by estimating means for estimating the three-dimensional trajectory of the target and the type of target to which the Measurement vector, image data from an image-resolving detector of the missile and data the inertial measurement unit of the missile and through which the model is correctable.
- the estimation means are expediently a suitably trained neuronal Network, a fuzzy-neural network or in the simplest case a rule-based fuzzy logic unit.
- An observer can from the image of the target with knowledge of the measurands of the Measurement vector and of course taking into account the own movement due to its Experience statements about the three-dimensional path of the target as well as the target type do.
- This can also be a neural network trained on it or a rule-based fuzzy logic unit.
- the Optimal filters provide sizes that indicate how well the after a calculation step Adaptation of the model to reality has taken place.
- an optimal filter that delivers Residual i.e. the difference between the measurement vector resulting from the model and the actually measured measurement vector is a measure of the quality of this adaptation.
- This one from the Sizes supplied to optimal filters, e.g. Residuals, and estimate of relative kinematics from the model are switched to an inference unit.
- the inference unit can be a neural network or a fuzzy neural network.
- the inference unit dials in model closest to reality.
- the inference unit can also have several Select a model with appropriate weights, if several such models are around are equally well adapted to reality.
- the estimate of the relative kinematics of each of the selected models is with an associated optimal steering law on the The missile's autopilot.
- the optimal filter needs less computational effort to model the missile and target to reality adapt.
- the adjustment is faster and more precise.
- the vector of the missile-target relative kinematics faster and more accurately Available.
- a driver who realizes that image of a vehicle driving in front of him suddenly becomes larger on the retina does not consider that the apparent growth of the vehicle on an approach caused by the reduced speed of this vehicle will, accordingly, to avoid bumping up its speed should also decrease and that you can do this by pressing a brake pedal can reach.
- Block 12 designates the flight controller of the target, an enemy aircraft to be attacked.
- the target dynamics 14 provides the change of a state vector x ⁇ T of the target.
- a block 16 represents the autopilot of a missile.
- the autopilot 16 receives a commanded lateral acceleration a MC at an input 18 in a manner to be described.
- the autopilot 16 receives signals from an inertial measurement unit ("IMU").
- IMU inertial measurement unit
- the change x ⁇ M of the state vector of the missile results from the missile dynamics .
- This change x ⁇ M of the state vector of the missile acts on the inertial measurement unit 22 of the missile.
- the changes in the state vectors x ⁇ T and x ⁇ M of the target and the missile result in a change in the relative kinematics, which is represented by a vector x ⁇ R.
- a vector x ⁇ R This is shown schematically in FIG. 1 by a summing point 24.
- the vector x ⁇ R of the change in the relative kinematics is integrated, which is represented by block 26. This returns the vector x R.
- "s" is the Laplace transform variable.
- the relative kinematics identified by vector x R is detected by sensors which form a sensor system 28. These sensors can be passive sensors such as image-resolving infrared sensors, or active sensors such as radar or laser sensors.
- the sensor system delivers signals according to the line of sight ⁇ , the line of sight rotation rate ⁇ ⁇ , the distance R between the missile and the target and its change R ⁇ . These signals are subjected to signal processing 30 and provide a real measurement vector z .
- the missile now contains a model 31 of the real world described above in a computer.
- this model 31 is surrounded by a dashed line in FIG.
- This model includes a model 32 of the missile and a model 34 of the target.
- the signals of the (real) inertial measurement unit 22 are applied to the model 32 of the missile.
- the model 32 of the missile provides an estimate the change in the state vector of the missile.
- the model 34 of the target provides an estimate the change in the state vector of the target. From the two vectors and a vector results which represents the change in the relative kinematics between the missile and the target as an estimate in the model.
- the estimated value z and the measurement vector will initially not match the real measurement vector z .
- This difference is switched to an optimal filter44.
- This optimal filter 44 can be, for example, a Kalman filter, an extended Kalman filter or a solution to the state-dependent Riccati equation (SDRE).
- SDRE state-dependent Riccati equation
- the parameters of the model are modified by the optimal filter in order to adapt the model 31 to reality. This is symbolically represented in FIG. 1 by the connection of a vector ⁇ from the optimal filter 44 to the summing point 36.
- the vector corrected what after integration to a corrected relative kinematics vector leads.
- the estimated value z and of the measurement vector are matched to the real measurement vector z .
- the relative kinematics vector resulting from the model is now connected to an optimal steering controller 46, which applies a commanded lateral acceleration a MC to the autopilot of the missile.
- the steering structure of FIG. 2 additionally contains a neural network 50.
- the measurement vector z is applied to this neural network 50 once.
- the neural network 50 receives image data from an image sensor or image-resolution detector.
- the signals of the inertial measuring unit are also connected to the neural network and take into account the own movement of the missile.
- the neural network 50 is trained in such a way that from these input variables it delivers estimates for the lateral acceleration of the target.
- the neural network 50 exploits, for example, the fact that a lateral acceleration of an aircraft forming the target is accompanied by a change in position of the aircraft in space and thus a characteristic change in the contour, which is "seen" from the missile. Based on experience, in the form of learning processes in the neural network, the size and direction of the lateral acceleration can therefore be inferred from such a change in contour.
- FIG. 3 also essentially corresponds to the steering structure of FIG Fig.1. Corresponding parts are in both figures with the same reference numerals provided and not described again in detail.
- the steering structure of FIG. 3 also contains a neural network 54.
- the measurement vector z is connected to this neural network 54, as in FIG.
- the neural network 54 likewise receives image data from an image sensor or image-resolution detector.
- the signals of the inertial measuring unit are also connected to the neural network 54 and take into account the intrinsic movement of the missile.
- the neural network 54 is trained in such a way that estimated values for the three-dimensional path of the target are obtained from these input variables. These estimates also serve to better model the relative kinematics. This is symbolized in FIG. 3 by the fact that the output of the neural network 54 is connected to the "summing point" 36 via a loop.
- the neural network 54 also delivers a statement from the image data that has been switched on about the type of target.
- FIG. 4 Another embodiment of the steering structure is shown in Fig.4.
- the execution after Fig. 4 assumes that the number of possible destinations is limited in many cases. So there are only a limited number of aircraft types used by an enemy become. The flight characteristics of these aircraft are known. It is also known what optimal escape maneuvers a particular aircraft type can make to one to escape approaching missiles. This knowledge is in the steering structure of Fig.4 realized.
- a model of the target a plurality of models are provided in the steering structure of FIG. 4, of which each model is based on a hypothesis about the target movement.
- These hypotheses correspond, for example, to possible escape movements of one aircraft type or of several aircraft types.
- These hypotheses about the target movement are represented in FIG. 4 by blocks 34.1, 34.2 ... 34.n.
- Each of these hypotheses yields a vector .
- Each of the hypotheses 34.1, 34.2 ... 34.n is assigned an optimal filter 44.1, 44.1 ... 44.n.
- the model of the missile is of course the same in all cases.
- the result is a plurality of parallel models of the missile-target relative kinematics with assigned optimal filters, which are represented by ⁇ in the manner described be adapted to reality.
- An inference unit 58 receives the vectors once as inputs from the different models to the other the residuals , The inference unit 58 then selects according to the residuals a vector x and R one of the models. This is then given to an assigned optimal steering law, which results in a commanded lateral acceleration a MC . It may be that after the residuals more than one model approximates reality pretty well.
- the inference unit 58 selects more than one vector x and R and the selected vectors and the associated steering laws are weighted overlaid.
- the inference unit 58 can contain a neural network or a fuzzy-neuronal network.
- FIG. 5 shows a further embodiment of a steering structure, in which the relative kinematic vector x and R from the model is reactive on the autopilot, firstly via a first channel according to a knowledge-based, optimal steering law and secondly via a second channel, for example according to an algorithmic steering law can be activated.
- the steering structure with the real world and the model of the real world is the same as in the embodiments according to FIGS. 1 to 4. Corresponding parts are provided with the same reference numerals as in FIGS. 1 to 4 and are no longer described in detail.
- the model 31 provides an estimate x and R for the relative kinematics vector representing the relative kinematics between the missile and the target. The steering is based on this estimated value of the relative kinematics vector x and R.
- two channels 60 and 62 are connected to the autopilot 16. Steering takes place via channel 60, as in the embodiments from FIGS. 1 to 4, according to a knowledge-based, optimal steering law, depending on the relative kinematics vector x and R estimated in model 31.
- Such a steering law can be implemented, for example, in that starting from different simulated, represented by relative kinematics vectors Encounter situations are calculated from control signals, which eventually cause the missile to hit the target. These courses are determined according to Criteria e.g. optimized in terms of flight time and fuel consumption. For any such Encounter situation then results in a kind of "multi-dimensional characteristic". The All of these characteristics provide a multi-dimensional characteristic field. This Characteristic field allows the training of a neural network to display the optimal steering law. This is represented by blocks 64 in FIG.
- An algorithmic or "reactive" steering law is implemented in the second channel 62. This is represented by block 66.
- Processed sensor signals which are represented by a vector z , serve as measured variables.
- the algorithmic steering law can be proportional navigation, whereby the commanded lateral acceleration, for example, is made the target proportional to the rate of rotation of the line of sight.
- the algorithmic steering law can be a modified proportional navigation.
- the algorithmic steering law can also be given by a solution of the state-dependent Ricati equation (SDRE).
- SDRE state-dependent Ricati equation
- the "reactive" steering law can also be implemented by a neural or fuzzy-neuronal network. This neural or fuzzy-neural network is trained offline with data that are generated as optimal trajectories for given encounter situations between the missile and the target as a result of a numerical, genetic or evolutionary optimization calculation.
- a main steering controller 68 controls the activation of the via control units 70 and 72 two channels 60 and 62 on the autopilot 16. The activation of one or the other channels 60, 62 or both channels takes place depending on the aerodynamic State of the missile (speed, angle of attack, flight altitude) and the status of the Missile (e.g. the remaining fuel supply)
- the estimated value of the relative kinematics vector x and R is obtained by an optimal filter 44, for example a Kalman filter.
- filters provide not only an estimate for the relative kinematics vector x and R but also estimates for the "reliability" of the components of this vector in the form of, for example, the covariances.
- the main steering controller can query these reliabilities. If the estimated value of the relative kinematics vector x and R has insufficient "reliability", for example as a result of interference measures by the target, the main steering controller will rely exclusively or primarily on the reactive channel 62 for the steering control.
- the activation of the relative kinematics vector x and R on the one hand via an optimal controller and on the other hand the (processed) sensor signals z in a reactive channel offers significant advantages:
- the optimal controller guides the missile to its destination on an optimal flight path.
- the optimal controller reacts too slowly to rapid and unexpected changes in the relative kinematics, which is caused, for example, by an unexpected maneuver of the target. It takes time for the optimal controller to determine the correct "multidimensional characteristic" for a relative kinematics vector x and R in the first channel.
- the reliability of the estimated value of the relative kinematics vector x and R can also be insufficient.
- the "reactive" channel 62 initially reacts relatively quickly to sensor signals, for example to a change in the line of sight to the target, and guides the missile according to the guidance law of proportional navigation. Which of the two channels 60 or 62 is activated by the main steering controller depends on the aerodynamic state and the other status of the missile and the reliability of the relative kinematics vector x and R.
- the target model 34 is characterized by additional information the sensor image and inertia measurement data supplemented, as in Figures 2, 3 and 4 is shown. This is through blocks 50A and 50B that correspond to block 50 in FIG correspond and block 54 is shown.
- Block 74 provides information about common ones Escape maneuvers of a type-recognized target, e.g. of a certain, recognized Aircraft type. The various information provided in this way becomes one Subjected to inference processing and the target model 34 is then modified.
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Abstract
Description
- Fig.1
- ist ein Blockdiagramm einer Lenkstruktur zum Lenken eines mit einem Sensorsystem versehenen Flugkörpers zu einem Ziel, wobei die Anpassung des Modells der Begegnungssituation an die reale Welt ausschließlich durch ein Optimalfilter erfolgt.
- Fig.2
- ist ein Blockdiagramm ähnlich Fig.1, wobei zusätzlich eine Schätzung der Zielbeschleunigung mittels eines neuronalen Netzes erfolgt.
- Fig.3
- ist ein Blockdiagramm ähnlich Fig.1, wobei zusätzlich eine Schätzung der dreidimensionalen Flugbahn des Ziels mittels eines neuronalen Netzes erfolgt.
- Fig.4
- ist ein Blockdiagramm einer Lenkstruktur mit einer Mehrzahl von Zielmodellen und einer Mehrzahl von Optimalfiltern, wobei jedem Zielmodell eine Hypothese über die Zielbewegung zu Grunde liegt und mittels eines neuronalen oder fuzzy-neuronalen Netzes deine Auswahl des für die Lenkung verwendeten Zielmodells erfolgt.
- Fig.5
- ist ein Blockdiagramm einer Lenkstruktur ähnlich Fig. 1 bis 4, wobei auf den Autopiloten des Flugkörpers zusätzlich Sensorsignale reaktiv, z.B. nach einem algorithmischen Lenkgesetz aufschaltbar sind.
Claims (15)
- Lenkstruktur zum Lenken eines mit einem Sensorsystem versehenen Flugkörpers zu einem Ziel, wobei aus dem Sensorsystem ein Meßvektor (z) von beobachtbaren Größen ableitbar ist, dadurch gekennzeichnet, daß(a) in dem Flugkörper ein Modell (31) von Flugkörper-Ziel-Relativkinematik und Sensorsystem (28) dargestellt ist,(b) das Modell durch ein Optimalfiltermittel (44) modifizierbar ist, welches von der Abweichung eines sich aus dem Modell ergebenden Meßvektors ( z and ) von dem realen Meßvektor (z) beaufschlagt sind, wobei durch Die Modifikation eine Anpassung des Modells (31) an die Realität angestrebt wird, und(c) der Flugkörper in Abhängigkeit von einem in dem Modell erzeugten, die relative Kinematik von Flugkörper und Ziel darstellenden Relativkinematik-Vektor ( x and R ) lenkbar ist.
- Lenkstruktur nach Anspruch 1, dadurch gekennzeichnet, daß der Relativkinematik-Vektor ( x and R ) nach einem optimalen Lenkgesetz (46) auf den Autopiloten (16) des Flugkörpers aufgeschaltet ist.
- Lenkstruktur nach Anspruch 1 oder 2, dadurch gekennzeichnet, daß die Signale einer inertialen Meßeinheit (22) des Flugkörpers auch auf das Modell (31) aufgeschaltet sind.
- Lenkstruktur nach einem der Ansprüche 1 bis 3, gekennzeichnet durch Schätzermittel (50) zum Schätzen der Zielbeschleunigung, auf welche der Meßvektor, Bilddaten von einem bildauflösenden Detektor des Flugkörpers und Daten der Trägheitsmeßeinheit (22) des Flugkörpers aufgeschaltet sind und durch welche das Modell (31) korrigierbar ist.
- Lenkstruktur nach einem der Ansprüche 1 bis 4, gekennzeichnet durch Schätzermittel (54) zum Schätzen der dreidimensionalen Bahn des Ziels, auf welche der Meßvektor, Bilddaten von einem bildauflösenden Detektor des Flugkörpers und Daten der Trägheitsmeßeinheit (22) des Flugkörpers aufgeschaltet sind und durch welche das Modell (31) korrigierbar ist.
- Lenkstruktur nach Anspruch 4 oder 5, dadurch gekennzeichnet, daß die Schätzermittel (50,54) ein entsprechend trainiertes neuronales Netz sind.
- Lenkstruktur nach einem der Ansprüche 1 bis 6, dadurch gekennzeichnet, daß(a) das das Modell (31) der Flugkörper-Ziel-Relativkinematik eine Mehrzahl von Hypothesen (34.1, 34.2 ... 34.n) über die Bewegung des Ziels enthält.(b) das Modell (31) parallel für jede dieser Hypothesen (34.1, 34.2 ... 34.n) durch ein zugehöriges Optimalfilter (44.1, 44.2... 44.n) im Sinne einer Anpassung des Modells (31) an die Realität modifizierbar ist,(c) die dabei erhaltenen geschätzten Zustandsvektoren ( x and R ) und eine Ausgangsgröße () des zugehörigen Optimalfilters (44.1, 44.2... 44.n) auf eine Inferenzeinheit (58) aufgeschaltet ist, die einen oder mehrere Zustandsvektoren ( x and R ) auswählt,(d) durch die Inferenzeinheit (58) wenigstens ein ausgewählter Zustandsvektor ( x and R ) auf einen zugehörigen Lenkregler (46.1, 46.2 ... 46.n) aufschaltbar ist, der nach einem zugehörigen optimalen Lenkgesetz arbeitet.
- Lenkstruktur nach Anspruch 2, dadurch gekennzeichnet, daß auf den Autopiloten (16) des Flugkörpers zusätzlich Sensorsignale (z) in einem reaktiven Kanal (62) aufgeschaltet oder aufschaltbar sind.
- Lenkstruktur nach Anspruch 8, dadurch gekennzeichnet, daß die Aufschaltung der Sensorsignale nach einem algorithmischen Lenkgesetz erfolgt.
- Lenkstruktur nach Anspruch 9, dadurch gekennzeichnet, daß das algorithmische Lenkgesetz das Lenkgesetz der Proportionalnavigation ist.
- Lenkstruktur nach Anspruch 9, dadurch gekennzeichnet, daß das algorithmische Lenkgesetz ein Lenkgesetz einer modifizierten Proportionalnavigation ist.
- Lenkstruktur nach Anspruch 9, dadurch gekennzeichnet, daß das algorithmische Lenkgesetz eine Lösung der zustandsabhängigen Ricatigleichung beinhaltet.
- Lenkstruktur nach Anspruch 9, dadurch gekennzeichnet, daß der reaktive Kanal ein neuronales oder fuzzy-neuronales Netz enthält.
- Lenkstruktur nach Anspruch 13, dadurch gekennzeichnet, daß das neuronale oder fuzzy-neuronale Netz offline mit Daten trainiert ist, die als optimale Trajektorien für gegebene Begegnungssituationen zwischen Flugkörper und Ziel als Ergebnis einer Optimierungsrechnung erzeugt werden.
- Lenkstruktur nach einem der Ansprüche 8 bis 14, dadurch gekennzeichnet, daß(a) die Aufschaltung des Relativkinematik-Vektors (x and R) nach einem wissensbasierten optimalen Lenkgesetz über einen ersten Kanal und die Aufschaltung von Sensorsignalen über einen zweiten, reaktiven Kanal erfolgt und(b) ein Hauptlenkregler vorgesehen ist, der in Abhängigkeit von dem aerodynamischen Zustand und/oder dem Status des Flugkörpers und/oder der Zuverlässigkeit des Relativkinematik-Vektors (x and R) den ersten Kanal oder den zweiten Kanal oder beide auf den Autopiloten des Flugkörpers aufschaltet.
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
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DE2000133368 DE10033368A1 (de) | 2000-07-08 | 2000-07-08 | Lenkstruktur für Flugkörper |
DE10033368 | 2000-07-08 |
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EP1174675A1 true EP1174675A1 (de) | 2002-01-23 |
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EP01730001A Withdrawn EP1174675A1 (de) | 2000-07-08 | 2001-06-05 | Lenkstruktur für Flugkörper |
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DE (1) | DE10033368A1 (de) |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP1241431A1 (de) | 2001-03-16 | 2002-09-18 | BODENSEEWERK GERÄTETECHNIK GmbH | Lenkstruktur für Flugkörper |
EP4261491A1 (de) * | 2022-04-13 | 2023-10-18 | Diehl Defence GmbH & Co. KG | Verfahren zum lenken eines abfangflugkörpers |
EP4261493A1 (de) * | 2022-04-13 | 2023-10-18 | Diehl Defence GmbH & Co. KG | Verfahren zum lenken eines flugkörpers |
EP4261492A1 (de) * | 2022-04-13 | 2023-10-18 | Diehl Defence GmbH & Co. KG | Verfahren zur midcourse-lenkung eines im schub steuerbaren flugkörpers |
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US5253823A (en) * | 1983-10-07 | 1993-10-19 | The Secretary Of State For Defence In Her Britannic Majesty's Government Of The United Kingdom Of Great Britain And Northern Ireland | Guidance processor |
DE4218600A1 (de) * | 1992-06-05 | 1993-12-09 | Bodenseewerk Geraetetech | Einrichtung zur Bestimmung von Bewegungsgrößen eines Flugkörpers |
GB2309068A (en) * | 1985-01-30 | 1997-07-16 | Secr Defence | Missile guidance system |
DE19704279A1 (de) * | 1997-02-05 | 1998-08-06 | Diehl Stiftung & Co | Verfahren zur Flugsteuerung eines Gleitschirms |
WO1999035460A1 (en) * | 1998-01-09 | 1999-07-15 | Raytheon Company | Neural network trajectory command controller |
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2000
- 2000-07-08 DE DE2000133368 patent/DE10033368A1/de not_active Withdrawn
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- 2001-06-05 EP EP01730001A patent/EP1174675A1/de not_active Withdrawn
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US5253823A (en) * | 1983-10-07 | 1993-10-19 | The Secretary Of State For Defence In Her Britannic Majesty's Government Of The United Kingdom Of Great Britain And Northern Ireland | Guidance processor |
US4589610A (en) * | 1983-11-08 | 1986-05-20 | Westinghouse Electric Corp. | Guided missile subsystem |
GB2309068A (en) * | 1985-01-30 | 1997-07-16 | Secr Defence | Missile guidance system |
DE4218600A1 (de) * | 1992-06-05 | 1993-12-09 | Bodenseewerk Geraetetech | Einrichtung zur Bestimmung von Bewegungsgrößen eines Flugkörpers |
DE19704279A1 (de) * | 1997-02-05 | 1998-08-06 | Diehl Stiftung & Co | Verfahren zur Flugsteuerung eines Gleitschirms |
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Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
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EP1241431A1 (de) | 2001-03-16 | 2002-09-18 | BODENSEEWERK GERÄTETECHNIK GmbH | Lenkstruktur für Flugkörper |
EP4261491A1 (de) * | 2022-04-13 | 2023-10-18 | Diehl Defence GmbH & Co. KG | Verfahren zum lenken eines abfangflugkörpers |
EP4261493A1 (de) * | 2022-04-13 | 2023-10-18 | Diehl Defence GmbH & Co. KG | Verfahren zum lenken eines flugkörpers |
EP4261492A1 (de) * | 2022-04-13 | 2023-10-18 | Diehl Defence GmbH & Co. KG | Verfahren zur midcourse-lenkung eines im schub steuerbaren flugkörpers |
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DE10033368A1 (de) | 2002-01-17 |
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