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United States of America As Represented by the Secretary of the Air Force

James H Michels

Dennis W Davis

Jaime R Roman

Jaime R Rom&aacute;n

Muralidhar Rangaswamy

Disclosed is an apparatus and method for improving the detection of signals obscured by either correlated Gaussian or non-Gaussian noise plus additive white noise. Estimates from multichannel data of model parameters that describe the noise disturbance correlation are obtained from data that contain signal-free data vectors, referred to as &ldquo;secondary&rdquo; or &ldquo;reference&rdquo; cell data. These parameters form the coefficients of a multichannel whitening filter. A data vector to be tested for the presence of a signal passes through the multichannel whitening filter. The filter output is then processed to form a test statistic. The test statistic is compared to a threshold value to decide whether a signal is &ldquo;present&rdquo; or &ldquo;absent.&rdquo; The method is effective in detecting targets without knowledge of the non-Gaussian noise statistics.

Normalized parametric adaptive matched filter receiver

Apparatus and method for improving the detection of signals obscured by either correlated Gaussian or non-Gaussian noise plus additive white Gaussian noise using Estimates from multi-channel data of model parameters that describe the noise disturbance correlation are obtained from data that contain signal-free data vectors, referred to as &ldquo;secondary&rdquo; or &ldquo;reference&rdquo; cell data. These parameters form the coefficients of a multi-channel whitening filter. A data vector to be tested for the presence of a signal passes through the multi-channel whitening filter. The filter's output is then processed to form a test statistic. The test statistic is compared to a threshold value to decide whether a signal is &ldquo;present&rdquo; or &ldquo;absent&rdquo;. Embodiments of the apparatus and method include estimating the signal amplitude both implicitly and explicitly and calculating test statistics for signal detection in both Gaussian and non-Gaussian noise.

Multichannel parametric adaptive matched filter receiver

Processing methods and associated hardware architectures for the spatio-temporal processing of phased array sensor signals are disclosed. The processing method employs a joint spatio-temporal domain processing approach. The disclosed method is an optimal estimation scheme which exhibits significantly reduced computational burden. Suppression of interference sources at angles-of-arrival other than the desired signal is achieved implicitly. The associated system architecture provides not only the ability to achieve good joint angle-Doppler estimates, but offers the ability to easily trade the relative performance in each domain. The disclosed approach emphasizes the use of multiple hypothesis testing with spatio-temporal whitening filters.

Method and apparatus for joint space-time array signal processing

The invention is directed to a method and associated system for the detection of various physiological features in multichannel sensor data. Truth data organized in the form of a vector sequence is used to generate a multichannel state variable model for data that is known to lack physiological features of interest. Such state variable models are obtained using one or more of a host of system identification algorithms. Upon identification of the model parameters, a multichannel inverse filter is derived from the model parameters. The inverse filter can be used to whiten or decorrelate components of sensor data sequences that are not associated with the physiological features of interest. The unwhitened output of the inverse filter then represents the physiological features of interest that can be extracted from the concomitant white noise. On this basis, a number of such filters can be produced for a variety of undesired physiological feature components of the data that occur separately or in combination. The invention is useful for the extraction of known and unknown features of interest from multichannel physiological sensor data.

Method and system for extraction and detection of physiological features

The invention described herein is directed to a method and associated system for the detection of various physiological conditions using multichannel sensor data. Truth data organized in the form of a vector sequence is used to generate a multichannel state variable model for a given known physiological condition. Such state variable models are obtained using one or more of a host of system identification algorithms. Upon identification of the model parameters, a multichannel inverse filter is derived from the model parameters. The inverse filter can be used to whiten or decorrelate sensor data sequences that are associated with the given physiological condition. The degree of whitening is a measure of the match of an unknown physiological condition with that physiological condition represented by the data used to generate the filter. On this basis, a number of such filters can be produced for a variety of physiological conditions. In a preferred embodiment, these filters operate in parallel on a given multichannel data sequence that represents an unknown physiological condition. The outputs of these filters are used in a hypothesis testing formulation to determine the identity of the physiological condition represented by the data under test.

Method and system for detection of physiological conditions

A process and system for improving the detection of signals in additive correlated non-Gaussian noise using multichannel data. This improves detection performance of receivers through the signal processing architecture when the observation data X(n) is first split into an upper and lower path. In the upper path, the known signal is first subtracted from the data and then passed directly to the prediction error filter. The output of the prediction error filters are then input to the zero-mean non-linear processor and their associated estimated variances are used to determine H.sub.0 and H.sub.1 where H.sub.0 denotes the condition where no signal is present in the data and H.sub.1 denotes the signal present case.

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Clinton, NY, United States of America

James H Michels