The noise equivalent angle (NEA) is determined for a class of nose tracking algorithms that determine the leading edge by use of a partial body centroid algorithm. Analytical expressions for the track error and jitter variance are derived for silhouette and two types of intensity weighted centroid processing for a range of nose shapes determined by the parameter (alpha) . Graphical results are presented for triangular, parabolic and rectangle nose shapes. This paper only considers the effects of additive noise. Spatial quantization is not included.
In determining the processing requirements for a track processor, which is most easily specified in terms of processing delay, it is necessary to have a relationship between the system requirement of track loop bandwidth, the sensor sample frequency and all the delays in the track loop processing. These delays include the sensor integration time, the sensor integration time, the sensor readout time, the time for A/D conversion and reformatting, the track processor delay and any D/A conversion and transportation delays associated with getting the rate commands to the servo system. This paper documents a methodology used in determining the relationship between bandwidth, sample frequency and system delays. This is done algebraically in terms of an 'effective' sampling frequency that takes into account the phase loss due to system delays. While more sophisticated analysis can be done, the simplified methods presented here will often be helpful to the systems analyst without the need for more direct control system analysis.
A technique is developed for estimating the leading edge location of an imaged target based on the target's centroid and its slope as the raster is scanned across the target's image. Since the estimate involves more pixels than the standard edge algorithm is easily implemented in a pipe-line process so that the leading edge location can be determined as the video is being read off the focal plane array with minimum processing delay. This algorithm can be shown to be a generalization of the standard biased centroid track algorithm. However, while the biased centroid algorithm requires a priori knowledge of the imaged target shape in order to determine the propose bias, this algorithm estimates the shape dependent factors in real time without any a priori knowledge.
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