Ail Systems, Inc.

C David Wang

James P Thompson

William T Walter

Robert Howard Kagann

Apparatus for detecting molecular vapors in an atmospheric region includes an interferometer which monitors light parameter data signals received and provides an interferometer light parameter signal corresponding to the light parameter data signals at a plurality of frequencies. The apparatus further includes an interferogram detector/converter which records and digitizes the interferometer light parameter signal to generate a plurality of discrete data points wherein each discrete data point corresponds to the interferometer light parameter signal at a specific frequency. The apparatus also includes a Fourier transform circuit for receiving the discrete interferometer light parameter signal and providing a Fourier transformed molecular parameter data signal. The apparatus further includes a probabilistic neural network for receiving and sorting the Fourier transformed molecular parameter data signals without the use of a priori training data.

Apparatus and method for detection of molecular vapors in an atmospheric

An improved open-path fast Fourier infrared spectrometer includes a circuit or algorithm which performs real-time, spectral alignment on measured interferograms to reduce measuremental errors. The improved spectrometer measures selected water vapor lines in a measurement path and compares the centerline of these measured water vapor lines to a reference library. From two or more such comparisons within the spectrometer bandwidth, the spectrometer calculates correction factors for the entire bandwidth. The improved spectrometer performs an overlap and add algorithm which applies the correction factors to perform bandwidth segment-specific shifts on subsequently measured interferograms.

Apparatus and method for real-time spectral alignment for open-path

A device for target prediction and collision warning for tracking objects in a region proximate to a vehicle includes a signal transmitter which provides first and second detection signals for at least partial reflection by an object located in a spatial region. The device further includes a signal receiver for receiving the deflected first and second detection signals corresponding to first and second parameter signals. A Fourier transform circuit is provided for receiving the first and second object parameter signals and generating first and second Fourier transform object parameter signals corresponding to relative range and velocity data of a target being tracked. The device includes a probabilistic neural network which preferably sorts the first and second Fourier transform object parameter signals corresponding to the relative range and velocity of a target being tracked. Operatively coupled to the probabilistic neural network is a target tracker circuit which receives the sorted first and second Fourier transform object parameter signals after at least three samples of relative range and velocity data have been measured. The target tracker generates an output signal indicative of a prediction of regression parameters of a second order or higher order equation that characterizes the change in relative range and velocity of the target being tracked.

Target prediction and collision warning system

Apparatus for motion detection and tracking of objects in a region for collision avoidance includes a signal transmitter which provides first and second detection signals for at least partial reflection by an object located in a spatial region. The apparatus further includes a signal receiver for receiving the deflected first and second detection signals corresponding to first and second object parameter data signals. The apparatus further includes a Fourier transform circuit for receiving the first and second object parameter data signals and providing first and second Fourier transform object parameter data signals. The apparatus further includes a probabilistic neural network for receiving and sorting the first and second Fourier transform object parameter data signals without the use of a priori training data.

Apparatus and method for motion detection and tracking of objects in a

An adaptive probabilistic neural network (APNN) includes a cluster processor circuit which generates a signal which represents a probability density function estimation value which is used to sort input pulse parameter data signals based upon a probability of obtaining a correct match with a group of input pulse parameter data signals that have already been sorted. In the APNN system, a pulse buffer memory circuit is contained within the cluster processor circuit and temporarily stores the assigned input pulse parameter data signals. The pulse buffer memory circuit is initially empty. As the input pulse parameter data signals are presented to the APNN, the system sorts the incoming data signals based on the probability density function estimation value signal generated by each currently operating cluster processor circuit. The current input pulse parameter data signal is sorted and stored in the pulse buffer memory circuit of the cluster processor circuit. A small probability density function estimation value signal indicates the current unassigned input pulse parameter data signal is not recognized by the APNN system. A large probability density function estimation value signal indicates a match and the current input pulse parameter data signal will be included within a particular cluster processor circuit.

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Melville, NY, United States of America

C David Wang