Mitsubishi Electric Research Laboratories, Inc.

Analog Devices,inc.

Mitsubishi Electric Corporation

Milutin Pajovic

Philip Orlik

Toshiaki Koike-Akino

Pu Wang

Kyeong Jin Kim

David Millar

Zafer Sahinoglu

Kieran Parsons

Yebin Wang

Keisuke Kojima

Petros T Boufounos

Ye Wang

Tao Yu

Toshiaki Koike Akino

Yacong Ding

Nevena Rakuljic

Omer Tanovic

Gal Shulkind

Gregory Patrick Davis

Xiao Yu Wang

Toshiaki Akino

Ivan Sanz Gorrachategui

Raj Tejas Suryaprakash

Gozde Ozcan

Koike Akino Toshiaki

Vikrant Shah

Nathaniel Moore Glaser

Millar David

Non-idealities of input circuitry of a receiver signal chain can significantly degrade the overall performance of the receiver signal chain. A digital nonlinearity correction (NLC) can be implemented in a receiver signal chain having an ADC. Digital NLC can be designed as a drop in signal preconditioner for existing RF ADC wideband receiver signal chains. A unique equalizer can compensate for a variety of mixer spurs. Accordingly, digital NLC can correct nonlinearities due to mixers and any amplifiers preceding or following the ADC, potentially improving receive chains performance by 15-25 dB. Such a digital NLC solution can be particularly beneficial in defense and instrumentation applications which demand the greatest performance.

Digital correction techniques for wideband receiver signal chain nonlinearities

A robot configured for multi-robot pose estimation execute iteratively the primary particle filter and the secondary particle filter independently from each other to update the pose estimations in the primary and the secondary particles. In response to each encounter of the robot with a neighboring robot, the robot receives neighboring particles of a particle filter of the neighboring robot identifying poses and likelihoods of the poses of the neighboring robot and replaces the primary particles of the primary particle filter with the secondary particles fused with the neighboring particles. The robot outputs its pose according to the primary particles of the primary particle filter.

System and method for probabilistic multi-robot positioning

A battery diagnostic system is disclosed for an RUL estimation of a battery. The battery diagnostic system comprises a memory configured to store a neural network trained to estimate a remaining useful life (RUL) of a test battery from a predetermined set of features indicative of a battery cycle of the test battery and a capacity of the test battery; a charging system configured to charge and discharge the test battery to provide measurements of the battery cycle and the capacity of the test battery; a processor configured to extract the predetermined set of features from the measurements to submit the extracted set of features to the neural network and an output interface configured to output the estimated RUL of the test battery.

Battery diagnostic system for estimating remaining useful life (RUL) of a battery

A robot performs simultaneous localization and mapping (SLAM) using a particle filter to process sensor measurements of movements of the robot in an environment and occupied locations in the environment to produce a set of particles. Each particle includes a map of the environment and a pose of the robot in the environment to represent a probability that the environment has the map oriented with respect to the pose of the robot. The robot receives a set of particles of a neighboring robot and, in response to receiving the set of particles of the neighboring robot, updates maps of probabilistically sampled particles of the robot with maps of probabilistically sampled particles of the neighboring robot. The update includes merging a map of the sampled particle of the robot with a map of the sampled particle of the neighboring robot roto-translated based on a relative pose between the robots.

System and method for probabilistic multi-robot SLAM

A system for estimating a pose of a robot includes a particle filter to track the pose of the robot using particles that defining a probability of pose of the robot and a particle tuner to update the particles of the robot based on particles of neighboring robot. Upon receiving data indicative of relative pose between a pose of the robot and a pose of a neighboring robot, and particles of the neighboring robot, the particle tuner pairs an arbitrarily sampled particle of the robot with an arbitrarily sampled particle of the neighboring robot, determines a weight of the paired particles in reverse proportion to an error between a relative pose defined by the paired particles and the relative pose between the robot and the neighboring robot, and updates the particles of the robot in accordance to the weights of corresponding paired particles.

A system for keyless entry applications using beamforming transmission in a millimeter wave spectrum in an environment. A memory with data including values indicative of link attributes associated with beam signal measurements with states of devices and states of environments. The states of the devices for each device including types of user behavior, locations and poses in each environment. Control circuitry performs beam training with a target device associated with at least one keyless entry application in the environment to measure beam signal values and environmental responses for different beams transmitted over the different beam angles. Selects, in response to the beam training, at least one dominant angle for a beamforming communication with the target device. Estimates, a state of the target device associated with the at least one keyless entry application or a state of the environment, corresponding to environmental responses for different beams estimated during the beam training.

Localization using millimeter wave beam attributes for keyless entry applications

A communication system using beamforming transmission in a millimeter wave spectrum in an environment. A memory with data including values indicative of link attributes associated with beam signal measurements with states of devices and states of environments. The states of the devices for each device including types of user behavior, locations and poses in each environment. The states of the environments for each environment including, locations of physical objects and types of behavior of ambient users. Control circuitry performs beam training with a target device in environment to measure beam signal values and environmental responses for different beams transmitted over the different beam angles. Selects, in response to the beam training, at least one dominant angle for a beamforming communication with the target device. Estimates, one of a state of the target device or a state of the environment, corresponding to environmental responses for different beams estimated during the beam training.

Localization using millimeter wave beam attributes

An apparatus for processing a perturbation quantity received in a wireless network. The perturbation quantity including a non-linear effect from at least one transmitted signal of the wireless network. The apparatus including a Base Station (BS) configured to establish a link between the BS and a user equipment (UE). A processing circuit is configured to implement receiver-side decoders. Wherein a receiver-side decoder is configurable to provide a recovery of unknown symbols perturbed by the perturbation quantity, the recovery is at a receiver-side and the perturbation is at the transmitter-side. Provide receiver-side decoding for symbol detection of symbol-dependent transmit-side perturbations by imposing a hierarchical signal model on the transmitted symbol. Wherein the decoding that is provided by the receiver-side decoders is used to recover the unknown symbols perturbed by the perturbation quantity and to identify symbol dependent perturbations, to reduce decoding processing costs by either the UE or the BS.

Symbol detection of massive MIMO systems with unknown symbol-dependent transmit-side impairments

A battery diagnostic system stores capacity degradation models for batteries of a specific type mapped to sets of battery cycle models formed by voltages and/or currents measured at different capacities. Each capacity degradation model is mapped to a set of battery cycle models associated with different battery capacities. The system, upon accepting measurements indicative of a battery cycle and a current capacity of the test battery, compares the battery cycle of the test battery with the battery cycle models of different capacity degradation models associated with a value of the battery capacity closest to the current capacity of the test battery to select a battery cycle model closest to the battery cycle of the test battery and the degradation capacity model mapped to the selected battery cycle model. The system estimates future degradation of the capacity of the test battery based on the retrieved capacity degradation model.

Battery diagnostic system for estimating capacity degradation of batteries

A communication system receives a jammed signal including a sequence of complex numbers encoding one or combination of symbols transmitted by multiple transmitters. The system transforms the sequence of complex numbers into an image of real numbers having at least two columns and multiple rows, with real values of real and imaginary parts of each complex number forming different columns but the same row of the first image, processes the image with a neural network trained to suppress the wideband signal in the received signal, and detects the symbols transmitted by the transmitters from the de-jammed signal.

Symbol detection is shared wireless channel subject to jamming interference

Methods and systems for predicting an unknown voltage of a battery corresponding to a future current demand for at least one time instant. The method including determining parameters of a first joint Gaussian distribution of a set of historical values of the voltage prediction of the battery from a set of historical measured physical quantities of the state of the battery stored in the memory. Determining a second joint Gaussian distribution of the unknown voltage and the set of historical values of the voltage prediction of the battery, based on a present measured physical quantities of the battery, the set of historical measured physical quantities and the determined parameters of the first joint Gaussian distribution. Determining a mean and a variance of an unknown voltage of the battery from the second joint Gaussian distribution, to obtain the predicted unknown voltage of the battery.

Methods and systems for battery state of power prediction

A method decodes an optical signal transmitted over an optical channel from a transmitter to a receiver. The method produces, from the transmitted optical signal, a digital signal including received data symbols and received pilot symbols and determines phase noise on the pilot symbols using a statistical probability distribution of phase noise on the optical channel and errors between phases of the received pilot symbols and the transmitted pilot signals. The method estimates phase noise on the data symbols using an interpolation of the phase noise on the pilot symbols and compensates the digital signal with the phase noise on the data symbols to produce a filtered signal with an equalized phase. The method demodulates and decodes the filtered signal to produce an estimate of the transmitted optical signal.

Pilot-aided carrier phase estimation for optical communications

A receiver for use in a wireless communication system to receive signals transmitted over a wireless channel. The receiver includes a plurality of radio-frequency (RF) chains coupled a plurality of antennas. Each RF chain has at least one-bit analog-to-digital converter (ADC) to convert each measurement of an analog signal received by the antenna into at least one bit of information representing a result of a comparison of the measurement with a randomly selected threshold to produce a sequence of bits and a corresponding sequence of thresholds. Each bit in the sequence of bits represents a relative value of the measurement of the analog signal with respect to a threshold from the corresponding sequence of thresholds. A processor coupled to the plurality of RF chains estimates parameters of the wireless channel using the sequences of bits and the corresponding sequences of thresholds received from the plurality of RF chains.

System and method for angular-domain channel estimation of massive MIMO system with low-resolution ADC with time-varying thresholds

A method for tracking a device determines correlations among locations of the device including a set of previous locations of the device and an initial estimate of a current location of the device, and determines, for each access point (AP), a current path loss exponent for the current location of the device using previous path loss exponents determined for the previous locations of the device and the correlations among the locations of the device. The method determines the current location of the device according to a path loss model using received signal strengths (RSS) of signals received from each AP at the current location and the current path loss exponent determined for each AP. The current path loss exponent for each AP are updated using the current location of the device and the RSS of signals received from the corresponding AP.

Device localization using RSS based path loss exponent estimation

A communication system includes a receiver to receive a signal with symbols encoded with a spreading code selected from a set of spreading codes, a filter to produce a filtered signal using a number of correlators less than a number of the spreading codes in the set of spreading codes, and a detector to detect the symbols transmitted by the transmitters from the filtered signal using sparse recovery with the dictionary matrix. The communication system also includes a processor to determine a minimum mean squared error (MMSE) matrix based on the set of spreading codes and a variance of noise in the channels, project the MMSE matrix to a low-dimensional space to produce a low-dimensional MMSE matrix, update the set of coefficients of set of correlators with the elements of the low-dimensional MMSE matrix, and update elements of a dictionary matrix based on the elements of the low-dimensional MMSE matrix.

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Cambridge, MA, United States of America

Milutin Pajovic