Abstract: A spectral feature extracting engine, chirp detecting engine, and target classifying engine with corresponding method, system, and computer product are provided. Given a “noisy” signal having multiplicative and additive noise, the spectral feature extracting engine extracts spectral features from the noisy signal in the form of a spectral detection density. The extraction includes identifying an initial detection by comparing the spectral content of a subject time-frequency slice with an initial detection threshold calculated from a set of time-frequency slices, and determining the spectral detection density as a function of a number of identified initial detections and number of compared time-frequency slices. Based on the spectral detection density of the noisy signal, the chirp detecting engine detects multiple channel-induced and target-induced chirps present in the noisy signal and the target classifying engine classifies a target from the noisy signal.

Abstract: Embodiments of a target classifier and method for target classification using measured target epsilons and target glint information are generally described herein. The target classifier is configured to compare a total epsilon measurement with target glint information to determine whether to the target being tracked corresponds to an intended target type. Based on the comparison, the target classifier may cause target tracking circuitry of a target-tracking radar to either continue tracking the target or break-off from tracking the target. Glint of different target types may be characterized at different ranges and the target's glint characteristics may be used to distinguish intended from non-intended targets. Accordingly, intended targets such as incoming artillery may be distinguished from non-intended targets such as aircraft to help prevent countermeasures from being launched against non-intended targets.

Abstract: Embodiments of a target-tracking radar and methods for responding to fluctuations in target SNR are generally described herein. In some embodiments, the target-tracking radar may be configured to determine whether a target can be considered a point target based on the SNR of received signals and off-boresight error estimates. Measurement-variance estimates generated from the SNR are provided to a target-state estimator when the target is determined to be a point target. When the target is determined not to be a point target, the measurement-variance estimates generated from SNR are not used by the target-state estimator. This may allow targets to be engaged at increased range.

Abstract: A spectral feature extracting engine, chirp detecting engine, and target classifying engine with corresponding method, system, and computer product are provided. Given a “noisy” signal having multiplicative and additive noise, the spectral feature extracting engine extracts spectral features from the noisy signal in the form of a spectral detection density. The extraction includes identifying an initial detection by comparing the spectral content of a subject time-frequency slice with an initial detection threshold calculated from a set of time-frequency slices, and determining the spectral detection density as a function of a number of identified initial detections and number of compared time-frequency slices. Based on the spectral detection density of the noisy signal, the chirp detecting engine detects multiple channel-induced and target-induced chirps present in the noisy signal and the target classifying engine classifies a target from the noisy signal.

Abstract: Embodiments of a target classifier and method for target classification using measured target epsilons and target glint information are generally described herein. The target classifier is configured to compare a total epsilon measurement with target glint information to determine whether to the target being tracked corresponds to an intended target type. Based on the comparison, the target classifier may cause target tracking circuitry of a target-tracking radar to either continue tracking the target or break-off from tracking the target. Glint of different target types may be characterized at different ranges and the target's glint characteristics may be used to distinguish intended from non-intended targets. Accordingly, intended targets such as incoming artillery may be distinguished from non-intended targets such as aircraft to help prevent countermeasures from being launched against non-intended targets.

Abstract: Embodiments of a target-tracking radar and methods for responding to fluctuations in target SNR are generally described herein. In some embodiments, the target-tracking radar may be configured to determine whether a target can be considered a point target based on the SNR of received signals and off-boresight error estimates. Measurement-variance estimates generated from the SNR are provided to a target-state estimator when the target is determined to be a point target. When the target is determined not to be a point target, the measurement-variance estimates generated from SNR are not used by the target-state estimator. This may allow targets to be engaged at increased range.