Abstract: Technology for determining a position of a platform is described. A location of a horizon line can be determined using a sensor onboard the platform. One or more celestial objects in the sky can be detected using the sensor onboard the platform. Differential angular measurements between the horizon line and at least one of the celestial objects in the sky can be determined over a duration of time. The position of the platform can be determined based on the differential angular measurements between the horizon line and the celestial objects.

Abstract: Technology for determining a position of a platform is described. A location of a horizon line can be determined using a sensor onboard the platform. One or more celestial objects in the sky can be detected using the sensor onboard the platform. Differential angular measurements between the horizon line and at least one of the celestial objects in the sky can be determined over a duration of time. The position of the platform can be determined based on the differential angular measurements between the horizon line and the celestial objects.

Abstract: Methods and systems are provided for estimating background spectral content in a hyperspectral imaging (HSI) scene. A HSI processor computes a scene covariance matrix for each of a plurality of sparsely sampled pixel sets, identifies and removes the spectral content of contaminating pixels from the covariance matrices, and checks the consistency among the plurality of decontaminated covariance matrices, iteratively re-sampling and re-computing said matrices until an acceptable consistency is achieved, and then computes a final decontaminated covariance matrix representative of the background spectral content of the scene. Alternate approaches to pixel sampling, and/or using fewer spectral dimensions than are available for the pixels are presented.

Abstract: A rapid target detection approach with corresponding method and system to detect targets in scene pixels, efficiently, is presented. The approach includes tailoring an approximation of a target score for each scene pixel, individually, based on an “intermediate target score.” The intermediate target score includes a portion of the terms used to compute the target score. The portion is selected by computing a signal-to-clutter ratio (SCR) for a spectral reference associated with a target and ranking the terms by their contribution to the SCR. Scene pixels with low intermediate target scores are removed from further processing. The remaining scene pixels are further processed, including computing target scores to detect targets in these scene pixel. Advantageously, examples of the approach process a few terms of all scene pixels, eliminate most scene pixels, and calculate more terms on high target scoring scene pixels as needed.

Abstract: The disclosure provides a filtering engine for selecting a subset of hyperspectral imaging wavebands having information useful for detecting a target in a scene. Selecting these wavebands, called “sparse bands,” is an iterative process. One or more search techniques of varying computational complexity are used in the process. The techniques rely on various selection criteria, including a signal to clutter ratio that measures the “goodness” of band selection. A convenient example of the filtering engine uses several of the techniques together in a layered approach. In this novel approach, simpler computational techniques are applied, initially, to reduce a number of bands. More computationally intensive techniques then search the reduced band space. Accordingly, the filtering engine efficiently selects a set of sparse bands tailored for each target and each scene, and maintains some of the detection capability provided with a full set of wavebands.

Abstract: Provided are examples of a detecting engine for determining in which pixels in a hyperspectral scene are materials of interest or targets present. A collection of spectral references, typically five to a few hundred, is used in look a through a million or more pixels per scene to identify detections. An example of the detecting engine identifies detections by calculating a kernel vector for each spectral reference in the collection. This calculation is quicker than the conventional Matched Filter kernel calculation which computes a kernel for each scene pixel. Another example of the detecting engine selects pixels with high detection filter scores and calculates coherence scores for these pixels. This calculation is more efficient than the conventional Adaptive Cosine/Coherence Estimator calculation that calculates a score for each scene pixel, most of which do not provide a detection.

Abstract: Provided are examples of a detecting engine for identifying detections in compressed scene pixels. For a given compressed scene pixel having a set of M basis vector coefficients, set of N basis vectors, and code linking the M basis vector coefficients to the N basis vectors, the detecting engine reduces a spectral reference (S) to an N-dimensional spectral reference (SN) based on the set of N basis vectors. The detecting engine computes an N-dimensional spectral reference detection filter (SN*) from SN and the inverse of an N-dimensional scene covariance (CN). The detecting engine forms an M-dimensional spectral reference detection filter (SM*) from SN* based on the compression code and computes a detection filter score based on SM*. The detecting engine compares the score to a threshold and determines, based on the comparison, whether the material of interest is present in the given compressed scene pixel and is a detection.

Abstract: Provided are examples of a detecting engine for identifying detections in compressed scene pixels. For a given compressed scene pixel having a set of M basis vector coefficients, set of N basis vectors, and code linking the M basis vector coefficients to the N basis vectors, the detecting engine reduces a spectral reference (S) to an N-dimensional spectral reference (SN) based on the set of N basis vectors. The detecting engine computes an N-dimensional spectral reference detection filter (SN*) from SN and the inverse of an N-dimensional scene covariance (CN). The detecting engine forms an M-dimensional spectral reference detection filter (SM*) from SN* based on the compression code and computes a detection filter score based on SM*. The detecting engine compares the score to a threshold and determines, based on the comparison, whether the material of interest is present in the given compressed scene pixel and is a detection.

Abstract: Provided are examples of a detecting engine for determining in which pixels in a hyperspectral scene are materials of interest or targets present. A collection of spectral references, typically five to a few hundred, is used in look a through a million or more pixels per scene to identify detections. An example of the detecting engine identifies detections by calculating a kernel vector for each spectral reference in the collection. This calculation is quicker than the conventional Matched Filter kernel calculation which computes a kernel for each scene pixel. Another example of the detecting engine selects pixels with high detection filter scores and calculates coherence scores for these pixels. This calculation is more efficient than the conventional Adaptive Cosine/Coherence Estimator calculation that calculates a score for each scene pixel, most of which do not provide a detection.