Bae Systems Information and Electronic Systems Integration Inc.

Masoud Farshchian

William H Alexander

A feature detection system, the system comprising: at least one processor in operative communication with a signal source, said processor further comprising at least one non-transitory storage medium, wherein at least one non-transitory storage medium contains instructions configured to cause the processor to: apply a joint group sparse denoising and delay estimation approach to a signal received from said signal source; and output statistics regarding the signal, wherein the joint group sparse denoising and delay estimation approach comprises; using the following equation: where: ϕare regularization functions; ∥y−x∥is a data-fidelity term and, in embodiments, is chosen as the least-square term; lare real numbers; Dare operators, which may be linear filters that can be written in matrix form; λare regularization parameters; and x*,τ* represent estimates of at least one transmitted pulse and associated delay, and solving the equation for multiple values of ϵ; choosing a vector, x, such that a cost function comprising the data fidelity term and regularization function is minimized; determining the ϵ that corresponds to the x that minimizes the cost function; and calculating the pulse width of the received signal, which corresponds to the desired estimate of the vector, x.

Joint denoising and delay estimation for the extraction of pulse-width of signals in RF interference

Techniques, systems, architectures, and methods for estimating the rise time of a pulse for multi-channel and signal channel cases involving an optimization system and method that can be solved iteratively based on sparse priors for a wide span of signal-to-noise ratios.

Method to estimate the rise-time of a pulse for single and multi-channel data

Techniques, systems, architectures, and methods for providing improved feature detection of signals, especially those in relatively high interference regions, thereby allowing for earlier and longer range detection of communications and radar signals are herein provided. The techniques utilize a general framework of total variation denoising, where signals are assumed to be sparse in a combination of their first or higher order derivatives, to increase signal-to-interference ratio, which is followed by cyclostationarity detection, which is used to estimate signal features, including the period of the signals of interest and their modulation type.

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Englewood Cliffs, NJ, United States of America

Masoud Farshchian