Photorefractive processing for large adaptive phased arrays

Abstract

An adaptive null-steering phased-array optical processor that utilizes a photorefractive crystal to time integrate the adaptive weights and null out correlated jammers is described. This is a beam-steering processor in which the temporal waveform of the desired signal is known but the look direction is not. The processor computes the angle(s) of arrival of the desired signal and steers the array to look in that direction while rotating the nulls of the antenna pattern toward any narrow-band jammers that may be present. We have experimentally demonstrated a simplified version of this adaptive phased-array-radar processor that nulls out the narrow-band jammers by using feedback-correlation detection. In this processor it is assumed that we know a priori only that the signal is broadband and the jammers are narrow band. These are examples of a class of optical processors that use the angular selectivity of volume holograms to form the nulls and look directions in an adaptive phased-array-radar pattern and thereby to harness the computational abilities of three-dimensional parallelism in the volume of photorefractive crystals. The development of this processing in volume holographic system has led to a new algorithm for phased-array-radar processing that uses fewer tapped-delay lines than does the classic time-domain beam former. The optical implementation of the new algorithm has the further advantage of utilization of a single photorefractive crystal to implement as many as a million adaptive weights, allowing the radar system to scale to large size with no increase in processing hardware.

Efficient Adaptive Optical Processing for Sonar Arrays

“…These can be implemented optically as a scrolling input modulator and a time-delay-and-integrate output detector. The adaptive weights can then be be optically implemented very compactly (for example by use of a photorefractive crystal, which requires less than a cubic centimeter of space), reducing the costs associated with space, hardware, and power consumption [1].…”

“…This is the reason that most DSP approaches utilize techniques that reduce the problem size, such as subarray adaptation. In this way the number of adaptive parameters are reduced at the expense of sacrificing optimal processing, thus diminishing their ability to process data from complex signal environments with large numbers of targets.As an alternative, in this proiject we investigated a class of optical processors that we have previously employed[1,2,3,4] for adaptive phased-array radar jammer-nulling and beamsteering operations. This architecture is fully adaptive, with the full ml degress of freedom, but requires only 1 feedback delay line and one additional delay line per adaptively formed beam.…”

Experimental demonstration of a broadband adaptive processor for phased-array antennas

“…Applications include the bias-free, timeintegrating correlator, 1 adaptive signal processing, and jammer excision. 2,3 Photorefractive processing of signals from RF antenna arrays is especially appropriate because of the massive parallelism that is readily achievable in a photorefractive crystal (in which many resolvable beams can be incident on a single crystal simultaneously-each coming from a separate RF antenna element), and because a number of approaches for adaptive array processing using photorefractive crystals have been successfully investigated (in which the adaptive weight coefficients are represented by the amplitude and phase of the holographic gratings 4,5 ). In these types of applications, RF modulated optical signals from each array element are diffracted from the adaptively recorded photorefractive gratings (which can be multiplexed either angularly or spatially), and are then coherently combined with the appropriate amplitude weights and phase shifts to effectively steer the angular receptivity pattern of the antenna array toward the desired arriving signal.…”

<title>Optical implementation of a single-layer finite impulse response neural network</title>

“…13 The first of them uses a time-integrating (TI) optical architecture for the implementation of multi-layer FIRNNs at high frequencies, making use of acousto-optic devices (AOD) for the instantaneous representation of the input nodes and charge-coupled devices (CCD) for the time-integration of the output. However, such an architecture is not capable of on-line learning.1 Other architectures use both time-integration and space-integration (SI) in the implementation of multi-layer FIRNNs.2'3 In these proposed schemes, the TI and SI architectures are folded together to efficiently implement on-line learning, using Wan's temporal back-propagation algorithm.…”