Numerous Free Space Optical Communications (FSOC) applications use fine tracking to achieve precise jitter stabilization necessary for high data rate communications. In addition, precision pointing mechanism are often required for point-ahead of the transmit laser. Prior systems have used either fast steering mirrors (FSMs), piezoelectric fiber optic positioners, or inertially stabilized platforms each of which has its own advantages and disadvantages for different applications.
We developed a small form-factor, high performance FSM capable of meeting both high bandwidth stabilization requirements as well as high precision pointing necessary for the point-ahead function. The current design achieves a 2.5 kHz closed-loop optical track bandwidth, <5 μrad/mrad accuracy, and better than 15 nm rms surface figure error. Because there is no single approach to FSOC architecture, we designed the FSM to be easily scaled and customized for various applications ranging from FSOC, image stabilization, and scanning. Simple choices and customization of the FSM components including the mirror substrate, flexure, feedback sensors, and actuator design can provide custom designs for various applications. Analysis tools were developed to quickly trade the multitude of design parameters that influence performance. This paper reviews the FSM design, performance, and qualification test results, and trade space available to customize the FSM. We present analysis and test data from a couple of design variations to show how our design and analysis approach allows the FSM to be quickly adapted to various performance and environmental requirements.
Multi-pass encoding is a technique employed in the field of video compression that maximizes the quality of an encoded video sequence within the constraints of a specified bit rate. This paper presents research where multi-pass encoding is extended to the field of hyperspectral image compression. Unlike video, which is primarily intended to be viewed by a human observer, hyperspectral imagery is processed by computational algorithms that generally attempt to classify the pixel spectra within the imagery. As such, these algorithms are more sensitive to distortion in the spectral dimension of the image than they are to perceptual distortion in the spatial dimension. The compression algorithm developed for this research, which uses the Karhunen-Loeve transform for spectral decorrelation followed by a modified H.264/Advanced Video Coding (AVC) encoder, maintains a user-specified spectral quality level while maximizing the compression ratio throughout the encoding process. The compression performance may be considered near-lossless in certain scenarios. For qualitative purposes, this paper presents the performance of the compression algorithm for several Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Hyperion datasets using spectral angle as the spectral quality assessment function. Specifically, the compression performance is illustrated in the form of rate-distortion curves that plot spectral angle versus bits per pixel per band (bpppb).