Adaptive Array Processing Research Articles

On the Davies tree for adaptive array processing

A recent paper [1] proposes using the Davies tree configuration as a processing structure for adaptive arrays. The advantage of doing this is that fewer computations are needed for adaptation. The purpose of this correspondence is to point out that the performance of the Davies tree is suboptimal. It does not, in general, produce maximum output SINR (signal-to-interference-plus-noise ratio) from the array.

VLSI Array processors

High speed signal processing depends critically on parallel processor technology. In most applications, general-purpose parallel computers cannot offer satisfactory real-time processing speed due to severe system overhead. Therefore, for real-time digital signal processing (DSP) systems, special-purpose array processors have become the only appealing alternative. In designing or using such array Processors, most signal processing algorithms share the critical attributes of regularity, recursiveness, and local communication. These properties are effectively exploited in innovative systolic and wavefront array processors. These arrays maximize the strength of very large scale integration (VLSI) in terms of intensive and pipelined computing, and yet circumvent its main limitation on communication. The application domain of such array processors covers a very broad range, including digital filtering, spectrum estimation, adaptive array processing, image/vision processing, and seismic and tomographic signal processing, This article provides a general overview of VLSI array processors and a unified treatment from algorithm, architecture, and application perspectives.

Empirical observations of a sonar adaptive array

The responses of adaptive and conventional acoustic array processors using data acquired from an underwater environment are compared. The data from a horizontal planar array with omnidirectional elements are processed using frequency-domain and element-space techniques to estimate the azimuthal noise anisotropy and to contrast the effectiveness of each processor. Three beamformers are represented: the uniform amplitude shaded beamformer, a nonuniformly shaded beamformer and a fully adaptive processor which seeks to optimise array response by minimising the beampower.

An adaptive array processor with robustness and broad-band capabilities

A new type of receiving array which adaptively minimizes ouput noise power while simultaneously satisfying certain robustness and/or bandwidth criteria is considered. The resulting array gains are shown to be robust against direction uncertainty in the assumed look direction, against wavefront distortions and against array geometry errors. The robustness property is incorporated directly into the adaption algorithm via constraints. Extensive simulation has established very satisfactory performance of this new algorithm, both as a limited broad-band processor and as a robust narrow-band processor.

Parallel processing of logic module placement

A parallel-processing algorithm for logic module placement is presented. A pairwise interchange algorithm, directed by the steepest-descent concept, is expanded to the parallel-processing case. By using an AAP (adaptive array processor), it is shown that an N-module placement problem can be processed in 0.06N of the time required by a sequential computer (1 MIPS).

LSI circuits for adaptive array processing in passive sonar

An important function in underwater passive listening is array processing. Its role is to distinguish, as far as possible, the acoustic targets present in the water. During the last two decades, much effort has been devoted to adaptive array processing to obtain better performance than with conventional beamforming, both in resolving power and protection against high-level sources. However, the amount of computation needed by this processing is great. To reduce the complexity of the processing, new algorithms have been developed. But for important applications, this is still not sufficient. To solve this problem, the processing scheme has been optimised in order to obtain a modular architecture by which systems can be built using the same elementary cascadable processor for many applications. The design of special LSI circuits for adaptive array processing is described and the performance advantages quantified.

Performance of an Adaptive Array Processor Subjected to Time-Varying Interference

Array processors for passive detection of directional wide-band Signals are commonly used in sonar, seismic work, and radio astronomy. The performance of such processors is degraded by directional noise sources referred to as interferences and adaptive processors to eliminate the effect of interferences have been used for many years. The issue addressed in this paper is movement of the interference and the effect that this has on adaptive loop design. The processor considered in the paper is an array of adjustable FIR filters using the Widrow LMS algorithm to adjust the filter weights. Changes in interference patterns affect both the covariance matrix of the observed signal and the optimum weight vector. For the purposes of this paper the optimum weight vector is modelled as an independent-increment process. This model permits the rigorous derivation of the optimum adaptive-loop gain parameter and of worst-case performance. In general it is found. that interference movement results in two error components: a gradient-noise component and a tracking component. Increases in adaptive loop gain decrease the tracking component but increase the gradient-noise component. Thus for a given set of interference-motion statistics an optimum gain can be found.

An LSI adaptive array processor

Describes an LSI adaptive array processor (AAP) for two-dimensional data processings. The AAP contains a large number of one-bit processing elements (PEs) arranged in a square array. The large degree of parallelism and control registers in each PE allow for high speed and flexible operations. High transfer capability is also obtained by a simple inter-PE connection network with hierarchical bypasses. The high applicability to various data processings is indicated by a matrix multiplication example, utilizing an algorithm similar to a systolic one. An AAP LSI composed of 8/spl times/8 PEs with powerful functions has been implemented in a 96.0 mm/SUP 2/ chip by using 2 /spl mu/m Si-gate p-well CMOS technology. A high-speed cycle time of 55 ns, low power dissipation of 1.1 W, and high packing density of 1170 transistors/mm/SUP 2/ has been achieved by a skilful manual design. Though the LSI contains as many as 111900 transistors, the design effort has only required one man-year due to cellular array regularity. This LSI is expected to realize a high-performance AAP compactly.

Analysis of the performance of adaptive beam forming using perturbation sequences

Conventional adaptive antenna array processing employing gradient descent requires access to signals on all the antenna array elements. The processing can be carried out at the radio frequency stage, an intermediate frequency stage, and in some cases at baseband. In all cases coherence of the array element signals must be maintained. For situations where access to all the array channels is not possible or not economical, a new technique for adaptively controlling the array weights has been developed. The technique requires access to the controllable weights and the beam-former output. Detailed analysis of the performance of the technique has been carried out. A number of possible structures which can be used are briefly reviewed, and the results of analysis of the performance achievable with some of these/structures are presented. A detailed comparison of the performance of the new technique with that of a system in which all signals are available is given. Ways of optimizing the performance of the new technique are considered. Results of computer studies of the performance of the new technique are presented.

Eigenfilter approaches to adaptive array processing

High-resolution methods of spectral estimation have recently found application in the processing of data received by spatially distributed arrays of sensors. These techniques have been used for estimation of the directional power illuminating the array and have proved useful as a first step in the analysis and classification of possible targets. Several desirable qualities are offered by these spatial spectral analysis techniques: high resolution for short arrays, low sidelobe levels, ability to work with arbitrary array geometries, and tolerance to correlated (multipath) targets being but a few. Spectral estimators based on certain eigenvectors of the data covariance matrix have these properties. The paper discusses the use of filters based on these eigenvectors (called eigenfilters) in adaptive array processing, and two different estimation algorithms are compared. The first algorithm involves a simple gradient descent approach, while the second utilises a decomposition of the array data by a spatial filter which leads to a triangular structured lattice filter. Comparisions are made between these algorithms and with other conventional high-resolution spectral estimators.

Eigenfilter approaches to adaptive array processing

High-resolution methods of spectral estimation have recently found application in the processing of data received by spatially distributed arrays of sensors. These techniques have been used for estimation of the directional power illuminating the array and have proved useful as a first step in the analysis and classification of possible targets. Several desirable qualities are offered by these spatial spectral analysis techniques: high resolution for short arrays, low sidelobe levels, ability to work with arbitrary array geometries, and tolerance to correlated (multipath) targets being but a few. Spectral estimators based on certain eigenvectors of the data covariance matrix have these properties. The paper discusses the use of filters based on these eigenvectors (called eigenfilters) in adaptive array processing, and two different estimation algorithms are compared. The first algorithm involves a simple gradient descent approach, while the second utilises a decomposition of the array data by a spatial filter which leads to a triangular structured lattice filter. Comparisons are made between these algorithms and with other conventional high-resolution spectral estimators.

Adaptive array processing. A tutorial

An array is defined as a set of sensors, for example antennas or acoustic transducers. The outputs from these sensors can be processed in an adaptive manner to respond to an unknown interference environment in an attempt to optimise the performance of the system in some defined manner. The paper is intended as an introduction to, and to some extent a survey of, some of the methods used in adaptive array processing. It has, however, no pretence to be comprehensive. Some of the topics covered include the use of constraints, gradient techniques, including the stochastic steepest-descent algorithm, properties of the covariance matrix, the power inversion algorithm and the use of weight purturbation techniques.

Adaptive array processing. A tutorial

An array is defined as a set of sensors, for example antennas or acoustic transducers. The outputs from these sensors can be processed in an adaptive manner to respond to an unknown interference environment in an attempt to optimise the performance of the system in some defined manner. The paper is intended as an introduction to, and to some extent a survey of, some of the methods used in adaptive array processing. It has, however, no pretence to be comprehensive. Some of the topics covered include the use of constraints, gradient techniques, including the stochastic steepest-descent algorithm, properties of the covariance matrix, the power inversion algorithm and the use of weight purturbation techniques.

ARMA Time Series Modeling: an Effective Method

The ability to generate rational models of time series plays an important role in such applications as adaptive filtering, spectral estimation, digital control, array processing, and forecasting. A method for effecting an autoregressive moving average (ARMA) model estimate is presented which possesses a number of admirable properties: 1) it has an elegant algebraic structure, 2) its modeling performance in spectral estimation applications has been empirically found to typically exceed that of such contemporary techniques as the periodogram, the Burg method, and the Box-Jenkins method on a variety of problems, 3) it is implementable by computationally efficient algorithms, and 4) it is based on pseudomaximum likelihood concepts. Taken in combination, these properties mark this method as being an effective tool in challenging applications requiring high modeling performance in a real time setting.

Performance assessments of a time-domain adaptive processor in a broadband environment

The paper is a study of a time-domain adaptive array processor operating in a broadband environment. The processor is a variant of that due to Widrow; in particular processing is carried out in octave bands and sensor data are not time shifted. The constraint system consists of four to seven linear point and gradient constraints, together with a soft norm constraint. Computations assume full adaptation, stationary data and sensor amplitude and phase errors. Calculations are made of array gain, array response and of the detectability of wanted-signal spectral lines in the output beam. Comparison is made between the performance of this processor and that of a conventional beamformer and of a frequency-domain adaptive processor. A wide variety of noise environments are selected, some fairly complex. Frequency bands are taken to cover a range of six octaves. The general conclusion is that the time-domain processor is both economical on processing time and has a reasonable level of robustness against sensor errors and time-varying environments. The performance loss due to suboptimal processing is normally not great, but for certain types of noise environments it can be significant.

Performance assessments of a time-domain adaptive processor in a broadband environment

The paper is a study of a time-domain adaptive array processor operating in a broadband environment. The processor is a variant of that due to Widrow; in particular processing is carried out in octave bands and sensor data are not time shifted. The constraint system consists of four to seven linear point and gradient constraints, together with a soft norm constraint. Computations assume full adaptation, stationary data and sensor amplitude and phase errors. Calculations are made of array gain, array response and of the detectability of wanted-signal spectral lines in the output beam. Comparison is made between the performance of this processor and that of a conventional beamformer and of a frequency-domain adaptive processor. A wide variety of noise environments are selected, some fairly complex. Frequency bands are taken to cover a range of six octaves. The general conclusion is that the time-domain processor is both economical on processing time and has a reasonable level of robustness against sensor errors and time-varying environments. The performance loss due to suboptimal processing in normally not great, but for certain types of noise environments it can be significant.

Perturbation adaptive array processor for airborne application

The adaptive processor here described uses a perturbation algorithm embodied in a microprocessor, and has been used with a four-element array. The resulting system is of low cost and great flexibility, with particular advantages for installation in aircraft. The transient response is shown to be of the same form as that of an LMS algorithm for small perturbations, and a simulation shows the deviations from this form for larger perturbations. The simulation also provided some interesting examples of processor behaviour, as well as being used to determine several design parameters. The use of a microprocessor allows more complicated features to be used than is possible with dedicated hardware. An example is the weight rescaling process which takes the place of the clamped weight or reference signal. The hardware description includes the highly successful weight linearisation technique used. Good linearity keeps to a minimum the word length used for weight control, and is essential to the weight rescaling process.

Perturbation adaptive array processor for airborne application

The adaptive processor here described uses a perturbation algorithm embodied in a microprocessor, and has been used with a four-element array. The resulting system is of low cost and great flexibility, with particular advantages for installation in aircraft. The transient response is shown to be of the same form as that of an LMS algorithm for small perturbations, and a simulation shows the deviations from this form for larger perturbations. The simulation also provided some interesting examples of processor behaviour, as well as being used to determine several design parameters. The use of a microprocessor allows more complicated features to be used than is possible with dedicated hardware. An example is the weight rescaling process which takes the place of the clamped weight or reference signal. The hardware description includes the highly successful weight linearisation technique used. Good linearity keeps to a minimum the word length used for weight control, and is essential to the weight rescaling process.

An alternative approach to linearly constrained adaptive beamforming

A beamforming structure is presented which can be used to implement a wide variety of linearly constrained adaptive array processors. The structure is designed for use with arrays which have been time-delay steered such that the desired signal of interest appears approximately in phase at the steered outputs. One major advantage of the new structure is the constraints can be implemented using simple hardware differencing amplifiers. The structure is shown to incorporate algorithms which have been suggested previously for use in adaptive beamforming as well as to include new approaches. It is also particularly useful for studying the effects of steering errors on array performance. Numerical examples illustrating the performance of the structure are presented.

Eliminating Reference Loop Phase Shift in Adaptive Arrays

Adaptive array processors utilizing the Widrow LMS algroithm with an internally generated reference signal have been shown to be subject to weight cycling caused by phase shift in the reference extracting loop. Adaptive compensation schemes that eliminate the reference loop phase shift are suggested here. Two such schemes are proposed. They differ in the amount of hardware complexity needed as well as in the rate at which each one eliminates the phase shift. Computer calculations are used to compare the rates of convergence of the two schemes.