MVDR Beamformer Research Articles

Finite-time performance of a quadratically constrained MVDR beamformer

The finite-time behavior of a quadratically constrained version of the minimum-variance, distortionless-response line-array beamformer is examined. An entity known as the weight ratio is defined and it is demonstrated that there are simple expressions that relate the weight ratio to the field scenario which explicitly take into account the finite-time misadjustment phenomenon. It is further demonstrated that holding the weight ratio to a value of about 0.5 produces an adaptive beamformer with a beneficial combination of robustness to nonideal situations (e.g., sensor misalignment, pointing errors, correlated interferers, misadjustment effects, etc.) while retaining the ability to adapt quickly in highly dynamic fields.

Programmable switched-capacitor neural network for MVDR beamforming

In this paper, a real-time adaptive antenna array based on a neural network approach is presented. Since an array operating in a nonstationary environment requires a programmable synaptic weight matrix for the neural network, the switched-capacitor (SC) circuits with the capability of programmability and reconfigurability is conducted to implement the neural-based adaptive array. Moreover, the SC techniques can directly implement the neural network with less chip area and provide the ratio of SC-equivalent resistors with accuracy of 0.1 percent. Programming of the switched-capacitor values could be made by allocating each synaptic weight to a set of parallel capacitors with values in a digitally programmable capacitor array (PCA). A relatively wide range of values (5 to 10 binary bits resolution) can be realized for each synaptic weight. A simulation tool called SWITCAP is used to verify the validity and performance of the proposed implementation. Experimental results show that the computation time of solving a linear array of 5 elements is about 0.1 ns for 1 ns time constant and is independent of signal power levels.

Optimum two-dimensional beamforming employing sum-and-difference technique

A 2-D adaptive beamforming scheme using a quadruplet array is presented. The scheme is developed based on the sum-and-difference technique employed in conventional monopulse trackers. The construction of the proposed beamformer involves a joint linearly constrained minimum variance procedure on the sum-and-difference processors. With the difference beam used as an auxiliary to suppress the desired signal, the resulting sum beam exhibits robustness to pointing errors in acquiring the desired signal. To maintain an accurate look direction, a simple method for desired source localization is developed using beamspace MUSIC. The proposed beamformer working in conjunction with the angle estimator provides a much better solution to 2-D beamforming than the MVDR beamformer, as confirmed by analysis results and numerical examples.

Parallel implementations of direction finding algorithms on networks of general purpose DSP processors

In this paper we address the problem of implementing two direction of arrival (DOA) estimation techniques on a multiprocessor network comprising of general purpose DSP processors. The techniques chosen are the MVDR beamforming technique and ESPRIT. Although optimal array architectures have been proposed for these problems, an implementation on a generalized network with the aim of a more flexible, modular and reconfigurable solution, is more suitable for highly dynamic environments with changing applications and problem sizes. The parallelization issues have been explored and a scheme for an efficient implementation of both techniques has been described.

Comments on a "A neural network approach to MVDR beamforming problem"

Several corrections to the above-titled paper by P.R. Chang et al. (ibid, vol. 40, pp 313-322, 1992) are given by the commenter, who also discusses a number of Chang et al.'s results. The commenter also discusses the practical issues that arise when an array operates in a nonstationary environment. >

A neural network approach to MVDR beamforming problem

A Hopfield-type neural network approach which leads to an analog circuit for implementing the real-time adaptive antenna array is presented. An optimal array pattern can be steered by updating the weights across the array in order to maximize the output signal-to-noise ratio (SNR). The problem of adjusting the array weights can be characterized as a constrained quadratic nonlinear programming. The adjustment of settings is required to respond to a rapid time-varying environment. A Hopfield-type neural net with a number of graded-response neurons designed to perform the constrained quadratic nonlinear programming would lead to a solution in a time determined by RC time constants, not by algorithmic time complexity. The constrained quadratic programming neural net has associated it with an energy function which the net always seeks to minimize. A fourth-order Runge-Kutta simulation shows that the circuit operates at a much higher speed than conventional techniques and the computation time of solving a linear array of 10 elements is about 0.1 ns for RC=5*10/sup -9/. >