Abstract: A system and method for assessing the probability of detection of a target of a hyperspectral sensing system. The system is adapted to calculate the probability of detection of targets based on various sensor parameters, atmospheric conditions, and a specified combination of targets and backgrounds for a given false alarm rate. The system may be executed, for example, on an IBM compatible PC to allow the user to optimize the hyperspectral sensor and subsequent signal processing to a particular set of backgrounds and targets. The sensor models, atmospheric models and target and background profiles are initially applied to the system in the form of the databases. As such, the system enables the user to select among the various parameters to optimize a hyperspectral sensor and the subsequent signal processing for a particular set of parameters.

Abstract: A system and method for assessing the probability of detection of a target of a hyperspectral sensing system. The system is adapted to calculate the probability of detection of targets based on various sensor parameters, atmospheric conditions, and a specified combination of targets and backgrounds for a given false alarm rate. The system may be executed, for example, on an IBM compatible PC to allow the user to optimize the hyperspectral sensor and subsequent signal processing to a particular set of backgrounds and targets. The sensor models, atmospheric models and target and background profiles are initially applied to the system in the form of the databases. As such, the system enables the user to select among the various parameters to optimize a hyperspectral sensor and the subsequent signal processing for a particular set of parameters.

Abstract: A minimum-spanning gradient filter used to suppress clutter in a target detection and tracking system. The minimum-spanning gradient filter uses subspace projection clutter suppression techniques, but does not require eigenanalysis. Model frames of data from a sensor array are stored in a memory in which the model frames do not include targets that are being detected. At start-up, a Gram-Schmidt system generates a series of unit vectors that define a clutter subspace where most of the clutter in the model frames will reside. Current frames of data from the sensor array and unit vectors from the Gram-Schmidt system are applied to a subspace projection system that removes the clutter subspace from the current frames of data. Once the original clutter subspace has been generated, a plane smoothing system updates the clutter subspace as new frames of data are added to the model frames. Current frames of data sent to the subspace projection system are centered.

Abstract: A band analysis system for determining the optimal signal-to-noise ratio between the spectral emissions from an object of interest and the spectral emissions from background noise for a target detecting and tracking system. Known target and background spectra are applied to a band select system in order to select an initial choice of bands. These bands are then applied to a sensor model system which determines a template for each spectrum. The templates are applied to a noise generation system in order to generate a noise function indicative of the vibrational noise of the sensor and the electronic noise of the system. The noise function also includes a function of the integration time over each band. The noise function and the templates are then used to determine a signal-to-noise ratio based on a weighting function. The signal-to-noise ratio is applied to the band select system and the integration time select system in order to adjust the bands and their integrations time to the optimum value.