Rockwell Collins, Inc.

Shubha L Kadambe

Gina M Reyes

Jason A Fuemmeler

Carlos J Chavez

Robert J Frank

Michael N Newhouse

W Sam Spencer

Steven E Koenck

David W Jensen

Jaclyn A Hoke

Marco Mattos Carvalho

Benjamin J Haan

Lawrence L Carin

Carlos Arturo Perez Monsalve

William S Spencer

A method for behavior based Friend/Foe/Neutral determination is disclosed. The method receives RF communications from a plurality of perceived entities and records a plurality of first level characteristics of the RF communications. The method determines, from an analysis of the first level characteristics a plurality of second level characteristics of the RF communications. From these second level characteristics, the method determines transmission patterns of the entities over time which lead to an ultimate classification of the perceived entity as a Friend/Foe/Neutral based on the patterns.

Behavior based friend foe neutral determination method

Disclosed is an apparatus for estimating the features of a radio frequency signal including a reference signal generator configured for generating a reference signal from a complex signal and one or more feature estimation units configured to generate one or more feature estimates from the reference signal and the complex signal. Techniques for creating reference signals from both signals spread by a scrambling code and FM signals are discussed.

Referenced-based radio frequency feature estimation

Systems, methods, and computer-readable media for detecting signals are provided. A method comprises determining a time-frequency representation for a received signal and partitioning the time-frequency representation of the received signal into a plurality of time-frequency partitions. The method further comprises determining an energy (e.g., a spectral energy) for each of the plurality of time-frequency partitions and detecting one or more signals within the received signal based on the energy (e.g., the mean spectral energy distribution) of the time-frequency partitions. In some embodiments, multiple signals received simultaneously within an input signal may be detected in unknown noise conditions.

Multiple signals detection system

A method for low-light imaging may include, but is not limited to: detecting one or more photons via the least one photon-counting sensor; determining an orientation of at least one photon-counting sensor; and correlating data associated with the at least one photon-counting sensor with the orientation of the at least one photon-counting sensor. A system for low-light imaging may include, but is not limited to: one or more photon-counting sensors configured for detecting one or more photons via the least one photon-counting sensor; one or more inertial sensors; and at least one processing unit configured for: determining an orientation of at least one photon-counting sensor and correlating data associated with the at least one photon-counting sensor with the orientation of the at least one photon-counting sensor.

Photon-starved imaging system

An apparatus and method for signal separation and SINR improvement is disclosed. In particular, disclosed is an iterative technique whereby a noisy signal, that may include signals from one or more distinct emitters, is first improved through an SINR improvement technique. Next, one signal from the noisy signal is identified and a reference signal substantially free of noise and interference is generated for this signal, and then this reference signal is subtracted from the noisy signal. This process is repeated until no other signals may be identified. In this way, the signal with the highest SINR may be identified first and then removed from the original signal. As such, the identified signal is no longer present as interference for the remaining signals, thus improving each of the remaining signals' respective SINRs.

Signal separation and SINR enhancement

An apparatus for identifying a specific emitter in the presence of noise and/or interference is disclosed. The apparatus includes a sensor configured to sense radio frequency signal data, the signal data containing noise and signal from at least one emitter, a reference estimation unit configured to estimate a reference signal relating to the signal transmitted by one emitter, a feature estimation unit configured to generate one or more estimates of one or more feature from the reference signal and the signal transmitted by that particular emitter, and an emitter identifier configured to identify the signal transmitted by that particular emitter as belonging to a specific device using one or more feature estimates. The emitter identifier identifies the signal transmitted by that particular emitter as belonging to a specific device using Gaussian Mixture Models and the Bayesian decision engine.

System for specific emitter identification

An apparatus for identifying a scrambling code used in the received CDMA-based signal. The apparatus includes a frame timing estimation unit configured to estimate a frame timing of the received CDMA-based signal, a searching unit configured to search for and select a scrambling code seed based on at least the estimated frame timing, and a seed evaluation unit configured to determine if the selected scrambling code seed is the correct seed used in the received CDMA-based signal. The searching unit is further configured to provide an identified scrambling code seed based on an indication from the seed evaluation unit that the selected scrambling code seed is the correct seed. The apparatus may also include a scrambling code generator configured to generate the scrambling code from the identified seed.

Identifying a CDMA scrambling code

A method and system are shown and described for separating, on a communication channel, multiple complex communication signals originating from corresponding multiple communication sources. The method includes estimating separate complex communication signals using an unmixing matrix. The unmixing matrix is recursively updated by transforming the estimated complex communication signals into real numbers for use with a gradient-based training module. The gradient-based training module is configured to use the real numbers to calculate learning rule updates to apply to the unmixing matrix. The gradient-based training module is configured to minimize error in the signal estimation obtained by application of the unmixing matrix. The transformation of the estimated complex communication signals into real numbers may include applying the complex communication signals to a hyperbolic tangent function.

Blind source separation of co-channel communication signals

A computerized method for transforming a signal representative of image information, the signal being a sparse signal, includes obtaining the signal. The method further includes using the signal to learn a dictionary of basis functions that represent at least part of the signal. The method further includes using the learned dictionary of basis functions to transform the signal.

Basis learning for sparse image representation and classification and low data rate compression

An apparatus for identifying a specific emitter in the presence of noise and/or interference is disclosed. The apparatus includes a sensor configured to sense radio frequency signal data, the signal data containing noise and signal from at least one emitter, a reference estimation unit configured to estimate a reference signal relating to the signal transmitted by one emitter, a feature estimation unit configured to generate one or more estimates of one or more feature from the reference signal and the signal transmitted by that particular emitter, and an emitter identifier configured to identify the signal transmitted by that particular emitter as belonging to a specific device using one or more feature estimates. The emitter identifier identifies the signal transmitted by that particular emitter as belonging to a specific device using Gaussian Mixture Models and the Bayesian decision engine. The apparatus may also include an SINR enhancement unit configured to enhance the SINR of the data before the reference estimation unit estimates the reference signal.

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Cedar Rapids, IA, United States of America

Shubha L Kadambe