Raytheon Company

Eric J Gudim

William Hale Wellman

Lee M Savage

William H Wellman

A receiver operable to determine a geolocation of a radio emitter is disclosed. The receiver can identify a set of observations derived from signals emitted by the radio emitter. The signals can be detected via an antenna associated with the receiver. The receiver can identify an estimated location of the radio emitter. The receiver can calculate a cone angle complement for each observation in the set of observations. The cone angle complement can correspond to an ambiguity level of each observation. The receiver can sort the observations based on corresponding ambiguity levels to produce a set of sorted observations. The receiver can process, using a Kalman filter in the receiver, the set of sorted observations to iteratively refine the estimated location for determination of the geolocation of the radio emitter.

Emitter geolocation using sorted observations

Methods and apparatus for determining an angle of arrival in a radar warning system that uses tracking to provide a more accurate angle of arrival than conventional systems. In exemplary embodiments, angle of arrival and range are mapped from measured body angles to a 3D coordinate system where modern tracking techniques are applied to improve accuracy and stabilization of measurements, then mapped back into body angles for display.

Methods and apparatus for determining angle of arrival (AOA) in a radar warning receiver

Several types of noise limit the performance of remote sensing systems, e.g., systems that determine the location, color, or shape of remote objects. When noise detected by sensors of the remote sensing systems is known and well estimated, a Kalman filter can converge on an accurate value without noise. However, non-Gaussian noise bursts can cause the Kalman filter to diverge from an accurate value. Current approaches arbitrarily boost noise with fixed additive or multiplicative factors, which slows filter response and often fails to give timely results. Such noise boosts prevent divergence due to badly corrupted measurements. Disclosed embodiments eliminate a subset of noise measurements having the largest errors from a data set of noise measurements and process the remaining data through the Kalman filter. Advantageously, disclosed embodiments enable a Kalman filter to converge on an accurate value without the introduction of noise boost estimates.

Iterative Kalman filtering

Accurate remote tracking of fixed objects from a moving platform requires overcoming platform noise. Such tracking becomes difficult when the only inertial reference (such as a central aircraft inertial navigation system) is remote from the sensor, which experiences non-measured angular movements due to airframe vibrations and flexing. In such a scenario, Kalman filtering cannot converge on a true value because all noise sources are not known. Current na&#xef;ve approaches arbitrarily boost noise with fixed additive or multiplicative factors. However, such approaches slow filter response and; thus, often fail to give timely results. Embodiments of the present disclosure derive inertial reference parameters to quantify noise of the sensor that is remote from the inertial reference. Advantageously, disclosed embodiments enable use of remote sensors with an existing inertial reference, rather than consolidating sensors and the inertial reference at a single location or providing inertial references at each sensor.

Growing community of inventors

Goleta, CA, United States of America

Eric J Gudim