Optical BEAMTAP beam-forming and jammer-nulling system for broadband phased-array antennas.

We present an approach to receive-mode broadband beam forming and jammer nulling for large adaptive antenna arrays as well as its efficient and compact optical implementation. This broadband efficient adaptive method for true-time-delay array processing (BEAMTAP) algorithm decreases the number of tapped delay lines required for processing an N-element phased-array antenna from N to only 2, producing an enormous savings in delay-line hardware (especially for large broadband arrays) while still providing the full NM degrees of freedom of a conventional N-element time-delay-and-sum beam former that requires N tapped delay lines with M taps each. This allows the system to adapt fully and optimally to an arbitrarily complex spatiotemporal signal environment that can contain broadband signals of interest, as well as interference sources and narrow-band and broadband jammers--all of which can arrive from arbitrary angles onto an arbitrarily shaped array--thus enabling a variety of applications in radar, sonar, and communication. This algorithm is an excellent match with the capabilities of radio frequency (rf) photonic systems, as it uses a coherent optically modulated fiber-optic feed network, gratings in a photorefractive crystal as adaptive weights, a traveling-wave detector for generating time delay, and an acousto-optic device to control weight adaptation. Because the number of available adaptive coefficients in a photorefractive crystal is as large as 10(9), these photonic systems can adaptively control arbitrarily large one- or two-dimensional antenna arrays that are well beyond the capabilities of conventional rf and real-time digital signal processing techniques or alternative photonic techniques.

Predictive head movement tracking using a Kalman filter.

The use of head movements in control applications leaves the hands free for other tasks and utilizes the mobility of the head to acquire and track targets over a wide field of view. We present the results of applying a Kalman filter to generate prediction estimates for tracking head positions. A simple kinematics approach based on the assumption of a piecewise constant acceleration process is suggested and is shown to track head positions with an rms error under 2 degrees for head movements with accelerations smaller than 3000 degrees /s. To account for the wide range of head dynamic characteristics, an adaptive approach with input estimation is developed. The performance of the Kalman filter is compared to that based on a simple polynomial predictor.