Patents by Inventor Kilian RAMBACH
Kilian RAMBACH has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240142573Abstract: A method and device for determining a classification for an object. The device is designed to provide pixels of a radar image that are assigned to the object, wherein the device is designed to provide a point cloud, wherein the point cloud comprises at least one point that represents a radar reflection assigned to the object, through at least one property assigned to the object, wherein the device is designed to extract first features that characterize the object, from the pixels, to extract second features that characterize the object, from the point cloud, and to determine the classification of the object depending on the first features and the second features.Type: ApplicationFiled: October 24, 2023Publication date: May 2, 2024Inventors: Gor Hakobyan, Christian Weiss, Kilian Rambach
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Patent number: 11947625Abstract: A method for training a neural network.Type: GrantFiled: September 20, 2021Date of Patent: April 2, 2024Assignee: ROBERT BOSCH GMBHInventors: Chaithanya Kumar Mummadi, Jan Hendrik Metzen, Kilian Rambach, Robin Hutmacher
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Patent number: 11797858Abstract: A method for training a generator. The generator is supplied with at least one actual signal that includes real or simulated physical measured data from at least one observation of the first area. The actual signal is translated by the generator into a transformed signal that represents the associated synthetic measured data in a second area. Using a cost function, an assessment is made concerning to what extent the transformed signal is consistent with one or multiple setpoint signals, at least one setpoint signal being formed from real or simulated measured data of the second physical observation modality for the situation represented by the actual signal. Trainable parameters that characterize the behavior of the generator are optimized with the objective of obtaining transformed signals that are better assessed by the cost function. A method for operating the generator, and that encompasses the complete process chain are also provided.Type: GrantFiled: September 9, 2020Date of Patent: October 24, 2023Assignee: ROBERT BOSCH GMBHInventors: Gor Hakobyan, Kilian Rambach, Jasmin Ebert
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Patent number: 11585894Abstract: A method for training a trainable module for evaluating radar signals. The method includes feeding actual radar signals and/or actual representations derived therefrom of a scene observed using the actual radar signals to the trainable module and conversion thereof by this trainable module to processed radar signals and/or to processed representations of the respective scene, and using a cost function to assess to what extent the processed radar signals are suited for reconstructing a movement of objects or to what extent the processed representations contain artifacts of moving objects in the scene. Parameters, which characterize the performance characteristics of a trainable module, are optimized with regard to the cost function. A method is also provided for evaluating moving objects from radar signals.Type: GrantFiled: August 3, 2020Date of Patent: February 21, 2023Assignee: Robert Bosch GmbHInventors: Gor Hakobyan, Kilian Rambach
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Publication number: 20220406046Abstract: A computer-implemented method for adapting a pretrained machine learning system, which has been trained on a first training data set, to a second dataset, wherein the second dataset has different characteristics than the first data set. An input transformation module for partly undoing the distribution shift between the first and second training data set is provided.Type: ApplicationFiled: May 18, 2022Publication date: December 22, 2022Inventors: Chaithanya Kumar Mummadi, Evgeny Levinkov, Jan Hendrik Metzen, Kilian Rambach, Robin Hutmacher
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Publication number: 20220101051Abstract: A method for training a neural network.Type: ApplicationFiled: September 20, 2021Publication date: March 31, 2022Inventors: Chaithanya Kumar Mummadi, Jan Hendrik Metzen, Kilian Rambach, Robin Hutmacher
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Patent number: 11269059Abstract: A method is described for locating and/or classifying at least one object, a radar sensor that is used including at least one transmitter and at least one receiver for radar waves. The method includes: the signal recorded by the receiver is converted into a two- or multidimensional frequency representation; at least a portion of the two- or multidimensional frequency representation is supplied as an input to an artificial neural network, ANN that includes a sequence of layers with neurons, at least one layer of the ANN being additionally supplied with a piece of dimensioning information which characterizes the size and/or absolute position of objects detected in the portion of the two- or multidimensional frequency representation; the locating and/or the classification of the object is taken from the ANN as an output.Type: GrantFiled: December 17, 2019Date of Patent: March 8, 2022Assignee: Robert Bosch GmbHInventors: Kanil Patel, Kilian Rambach, Michael Pfeiffer
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Publication number: 20220011403Abstract: A method for classifying objects based on measured data recorded by at least one radar sensor. In the method, a frequency spectrum of time-dependent measured data of the radar sensor is provided; from this frequency spectrum, locations from which reflected radar radiation has reached the radar sensor are ascertained; at least one group of such locations belonging to one and the same object is ascertained; for each location in this group, a portion of the frequency spectrum that corresponds to the radar radiation reflected from this location is ascertained; all these portions for the object are aggregated and are fed to a classifier; the object is assigned by the classifier to one or multiple classes of a predefined classification.Type: ApplicationFiled: July 2, 2021Publication date: January 13, 2022Inventors: Kilian Rambach, Lisa-Kristina Morgan, Adriana-Eliza Cozma
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Publication number: 20210357750Abstract: A system and method are provided for classifying objects in spatial data using a machine learned model, as well as a system and method for training the machine learned model. The machine learned model may comprise a content sensitive classifier, a location sensitive classifier and at least one outlier detector. Both classifiers may jointly distinguish between objects in spatial data being in-distribution or marginal-out-of-distribution. The outlier detection part may be trained on inlier examples from the training data, while the presence of actual outliers in the input data of the machine learnable model may be mimicked in the feature space of the machine learnable model during training. The combination of these parts may provide a more robust classification of objects in spatial data with respect to outliers, without having to increase the size of the training data.Type: ApplicationFiled: April 19, 2021Publication date: November 18, 2021Inventors: Chaithanya Kumar Mummadi, Anna Khoreva, Kaspar Sakmann, Kilian Rambach, Piyapat Saranrittichai, Volker Fischer
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Publication number: 20210223388Abstract: A method for reconstructing elevation information from measured data that were recorded with the aid of at least one radar device and include a two-dimensional spatial distribution of at least one physical measured variable. The measured data are fed as input variables to at least one generator module that is designed as a neural network. At least one output variable is retrieved from the generator module that represents a measure of the elevation angles from which radar radiation was reflected to the radar device from at least one object. A method for training a generator module, and a method including a complete active chain up to activating a vehicle, are also described.Type: ApplicationFiled: January 12, 2021Publication date: July 22, 2021Inventors: Gor Hakobyan, Kilian Rambach
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Publication number: 20210081762Abstract: A method for training a generator. The generator is supplied with at least one actual signal that includes real or simulated physical measured data from at least one observation of the first area. The actual signal is translated by the generator into a transformed signal that represents the associated synthetic measured data in a second area. Using a cost function, an assessment is made concerning to what extent the transformed signal is consistent with one or multiple setpoint signals, at least one setpoint signal being formed from real or simulated measured data of the second physical observation modality for the situation represented by the actual signal. Trainable parameters that characterize the behavior of the generator are optimized with the objective of obtaining transformed signals that are better assessed by the cost function. A method for operating the generator, and that encompasses the complete process chain are also provided.Type: ApplicationFiled: September 9, 2020Publication date: March 18, 2021Inventors: Gor Hakobyan, Kilian Rambach, Jasmin Ebert
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Publication number: 20210063535Abstract: A method for training a trainable module for evaluating radar signals. The method includes feeding actual radar signals and/or actual representations derived therefrom of a scene observed using the actual radar signals to the trainable module and conversion thereof by this trainable module to processed radar signals and/or to processed representations of the respective scene, and using a cost function to assess to what extent the processed radar signals are suited for reconstructing a movement of objects or to what extent the processed representations contain artifacts of moving objects in the scene. Parameters, which characterize the performance characteristics of a trainable module, are optimized with regard to the cost function. A method is also provided for evaluating moving objects from radar signals.Type: ApplicationFiled: August 3, 2020Publication date: March 4, 2021Inventors: Gor Hakobyan, Kilian Rambach
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Publication number: 20200200871Abstract: A method is described for locating and/or classifying at least one object, a radar sensor that is used including at least one transmitter and at least one receiver for radar waves. The method includes: the signal recorded by the receiver is converted into a two- or multidimensional frequency representation; at least a portion of the two- or multidimensional frequency representation is supplied as an input to an artificial neural network, ANN that includes a sequence of layers with neurons, at least one layer of the ANN being additionally supplied with a piece of dimensioning information which characterizes the size and/or absolute position of objects detected in the portion of the two- or multidimensional frequency representation; the locating and/or the classification of the object is taken from the ANN as an output.Type: ApplicationFiled: December 17, 2019Publication date: June 25, 2020Inventors: Kanil Patel, Kilian Rambach, Michael Pfeiffer
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Patent number: 9730219Abstract: For providing time multiplexing sequences for transmit antennas of a linear or two-dimensional MIMO radar unit that includes antennas situated close to one another (collocated MIMO radar), a method that enables a precise angle estimation includes implementing an algorithm with which a transmit sequence of transmitters and their transmit times are determined so that object movements have essentially no influence on the angle estimation. In this way, as a function of previously known quantities, optimal time multiplexing sequences can be determined in each case.Type: GrantFiled: March 24, 2015Date of Patent: August 8, 2017Assignee: Robert Bosch GmbHInventors: Kilian Rambach, Markus Karl Vogel
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Patent number: 9448302Abstract: In a method for operating a MIMO radar, an influence of an object motion on an angle estimate is substantially eliminated, and a time multiplex schema having a transmission sequence and transmission instants of transmitters of the MIMO radar is identified by optimizing the following mathematical relationship: d _ pulses , opt = arg ? ? max d _ pulses ? [ Var S ? ( d _ pulses ) - ( Cov S ? ( d _ pulses , t ) ) 2 / Var S ? ( t _ ) ] in which: dpulses,opt is optimized positions of the transmitters in the sequence in which they transmit; dpulses is positions of the transmitters in the sequence in which they transmit; t is transmission instants; VarS is sample variance; and CovS is sample covariance.Type: GrantFiled: May 22, 2014Date of Patent: September 20, 2016Assignee: ROBERT BOSCH GMBHInventors: Michael Schoor, Goetz Kuehnle, Kilian Rambach, Benedikt Loesch
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Publication number: 20150295628Abstract: For providing time multiplexing sequences for transmit antennas of a linear or two-dimensional MIMO radar unit that includes antennas situated close to one another (collocated MIMO radar), a method that enables a precise angle estimation includes implementing an algorithm with which a transmit sequence of transmitters and their transmit times are determined so that object movements have essentially no influence on the angle estimation. In this way, as a function of previously known quantities, optimal time multiplexing sequences can be determined in each case.Type: ApplicationFiled: March 24, 2015Publication date: October 15, 2015Inventors: Kilian RAMBACH, Markus Karl VOGEL
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Publication number: 20140347211Abstract: In a method for operating a MIMO radar, an influence of an object motion on an angle estimate is substantially eliminated, and a time multiplex schema having a transmission sequence and transmission instants of transmitters of the MIMO radar is identified by optimizing the following mathematical relationship: d _ pulses , opt = arg ? ? max d _ pulses ? [ Var S ? ( d _ pulses ) - ( Cov S ? ( d _ pulses , t ) ) 2 / Var S ? ( t _ ) ] in which: dpulses,opt is optimized positions of the transmitters in the sequence in which they transmit; dpulses is positions of the transmitters in the sequence in which they transmit; t is transmission instants; VarS is sample variance; and CovS is sample covariance.Type: ApplicationFiled: May 22, 2014Publication date: November 27, 2014Applicant: ROBERT BOSCH GMBHInventors: Michael SCHOOR, Goetz KUEHNLE, Kilian RAMBACH, Benedikt LOESCH