Phase-only Nulling Research Articles

Radial Basis Function Neural Network Optimal Modeling for Phase-Only Array Pattern Nulling

Phase-only nulling with low sidelobe level is a problem of interest in array synthesis which is a tedious problem without an analytical solution. In this communication, a novel framework for the phase-only nulling based on radial basis function neural network (RBFNN) is proposed to predict the phase adjustment for the array pattern nulling with sidelobe control. In the process of network training, the parameters of the RBFNN are optimized simultaneously based on the self-adaptive differential evolution (SADE) algorithm, which aims to improve the approximation ability and reduce the complexity of the network. Simulation results of the optimized RBFNN models show compact network structure and form the array pattern under desired performance with good generalization capability.

Real-Time Phase-Only Nulling Based on Deep Neural Network With Robustness

Phase-only nulling under sidelobe and mainlobe constraints is a problem of interest in array synthesis which is a nonlinear problem without analytical solution. To reduce the computational cost of phase-only array nulling on-line, this paper proposes a real-time phase-only array synthesis method based on the deep neural network. The on-line real-time prediction of element excitation phase is achieved by the trained neural network which can be done off-line. The performance of the trained neural network is related with the number of data. Firstly, in order to obtain a large enough database for the deep neural network efficiently, a multi-task phase-only array synthesis model with nulling operation and sidelobe control is relaxed to a convex problem and solved by direct iterative rank refinement. Then, the deep neural network is devised to emulate the phase array nulling behavior. This is carried out by the design of the structure of the network, the dataset structure and the loss function of the network. To validate the performance of the deep neural network, the phase-only nulling of 10-element and 16-element linear array based on the deep neural network is realized and tested. Experimental results demonstrate that the proposed real-time array synthesis method not only satisfies the desired array pattern property but also shows robustness to the array imperfections. Robustness is validated with Monte Carlo test.

Adaptive Nulling with Time-Modulated Antenna Arrays Using a Hybrid Differential Evolution Strategy

A flexible approach based on a hybrid differential evolution strategy was developed for adaptive pattern nulling with time-modulated linear antenna arrays. The hybrid differential evolution strategy adjusts only the least significant bits of the digital phase shifters and the on—off time sequence to minimize the total output power; deep nulls can then be placed in the direction of the interferences without much compromise of the shape of the main beam. Thanks to the new design freedom introduced in time-modulated antenna arrays, this adaptive nulling approach exhibits better performance over the traditional phase-only, amplitude-only, and phase-amplitude adaptive nulling methods, in terms of the practical implementation, nulling depth, and pattern deterioration. Numerical examples are presented to demonstrate the effectiveness and robustness of the proposed approach.

Phase-only adaptive nulling with neural networks for antenna array synthesis

A new approach to adaptive phase-only nulling with phased arrays is described. An efficient method is presented to synthesise directive beam and multi-beam patterns and create adaptive nulls in interference direction. The proposed method is based on iterative minimisation of a function that incorporates constraints imposed in each direction with respect to excitation phases and neural network technique. Various results are presented to show the advantages and limitations of this approach. The back-propagation algorithm proves to be better than previous phase-only adaptive algorithms. To verify the performances of the proposed technique, an eight-element array has been realised and tested for various types of beam configurations.

Phase-only adaptive nulling with a genetic algorithm

This paper describes a new approach to adaptive phase-only nulling with phased arrays. A genetic algorithm adjusts some of the least significant bits of the beam steering phase shifters to minimize the total output power. Using small adaptive phase values results in minor deviations in the beam steering direction and small perturbations in the sidelobe level in addition to constraining the search space of the genetic algorithm. Various results are presented to show the advantages and limitations of this approach, in general, the genetic algorithm proves to be better than previous phase-only adaptive algorithms.

Partial adaptive nulling on a monopulse phased array antenna system

A partial phase-only nulling (PPON) algorithm has been developed and evaluated on a 496-element monopulse phased array antenna system which employs five-bit phase shifters. Using this PPON algorithm allows nulls in the far-field pattern to be steered to the desired directions for a phased array equipped with low-resolution phase shifters to perform simultaneous nulling in the sum and two difference patterns in the environment of multiple jammers. Simulated and experimental patterns are illustrated.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Adaptive nulling in monopulse antennas

Theoretical and experimental results are presented for phase-only nulling in low-sidelobe monopulse antennas. Both results are based on a gradient search algorithm that simultaneously searches for a minimum in the sum and difference channel output powers. The array's beam steering phase shifters double as the adaptive weights. Each element in the gradient is found by changing phase shifter setting by Delta Psi (the phase shifter stepsize) and measuring the change in output power. Then the phase shifter is restored to its original value, and the process repeated for all the remaining array phase shifters. The algorithm iterates as long as each new adaptive weight setting reduces the total output power. If the output does not go down, then Delta Psi is decremented by one setting and the iteration is started again. The algorithm stops when Delta Psi =0. The adaptive weights act as random perturbations to the phase taper of the array. Consequently, the sidelobe level is proportional to the size of the phase perturbations and inversely related to the number of elements. By keeping the adaptive phase shifts small, the average sidelobe level and the main beam gain do not drastically change.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>