Abstract: A scene is reconstructed by receiving pulses transmitted by a virtual array of transducers. The pulses are reflected by the scene. The virtual array has a set of configurations subject to positioning errors. Each received pulse is sampled and decomposed to produce frequency coefficients stacked in a set of linear systems modeling a reflectivity of the scene. There is one linear system for each configuration. A reconstruction method is applied to the set of linear systems. The reconstruction method solves each linear system separately to obtain a corresponding solution. The corresponding solutions share information during the solving. Then, the solutions are combined to reconstruct the scene.

Abstract: A method reconstructs a scene behind a wall by transmitting a signal through the wall into the scene. Parameters of the wall are estimated from a reflected signal. A model of a permittivity of the wall is generated using the parameters, and then the scene is reconstructed as an image from the reflected signal using the model and sparse recovery.

Abstract: A spotlight synthetic aperture radar (SAR) image is generated by directing randomly a beam of transmitted pulses at a set of two or more areas using a steerable array of antennas. Each area is illuminated by an approximately equal number of the transmitted pulses. Then, a reconstruction procedure is applied independently to received signals from each area due to reflecting the transmitted pulses to generate the image corresponding to the set of areas.

Abstract: A synthetic aperture radar (SAR) system includes a non-uniform pulse generator, and an echo receiver. A SAR image is reconstructed from samples of received echoes, wherein transmitted pulses and reflected echoes overlap in time.

Abstract: An inner product if formed from an input signal and a hashing vector, to which a dither scalar is added. A binary digit is produced according to a quantization function, wherein the binary digit is zero or one, and wherein the quantization function is non-monotonic and subject to a sensitivity parameter. The steps are iterated while decreasing the sensitivity parameter until a termination condition is reached.

Abstract: An inner product if formed from an input signal and a hashing vector, to which a dither scalar is added. A binary digit is produced according to a quantization function, wherein the binary digit is zero or one, and wherein the quantization function is non-monotonic and subject to a sensitivity parameter. The steps are iterated while decreasing the sensitivity parameter until a termination condition is reached.

Abstract: A synthetic aperture radar (SAR) system includes a non-uniform pulse generator, and an echo receiver. A SAR image is reconstructed from samples of received echoes, wherein transmitted pulses and reflected echoes overlap in time.

Abstract: A method constructs a location model for locating and tracking sources of acoustic signals. Acoustic training signals are acquired from an acoustic training source at an unknown location in an environment with an array of microphones placed at unknown positions in the environment. From each acoustic training signal, relative acoustic features are extracted to construct a location model that is trained with the relative acoustic features.

Abstract: A method models trajectories of a signal source. Training signals generated by a signal source moving along known trajectories are acquired by each sensor in an array of sensors. Phase differences between all unique pairs of the training signals are determined. A wrapped-phase hidden Markov model is constructed from the phase differences. The wrapped-phase hidden Markov model includes multiple Gaussian distributions to model the known trajectories of the signal source.

Abstract: A method models trajectories of a signal source. Training signals generated by a signal source moving along known trajectories are acquired by each sensor in an array of sensors. Phase differences between all unique pairs of the training signals are determined. A wrapped-phase hidden Markov model is constructed from the phase differences. The wrapped-phase hidden Markov model includes multiple Gaussian distributions to model the known trajectories of the signal source.

Abstract: A method constructs a location model for locating and tracking sources of acoustic signals. Acoustic training signals are acquired from an acoustic training source at an unknown location in an environment with an array of microphones placed at unknown positions in the environment. From each acoustic training signal, relative acoustic features are extracted to construct a location model that is trained with the relative acoustic features.