Analysis of modified SMI method for adaptive array weight control

by Ronald Louis Dilsavor

Publisher: National Aeronautics and Space Administration, Publisher: National Technical Information Service, distributor in Washington, DC, Springfield, VA

Written in English
Published: Downloads: 982
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  • Adaptive control.,
  • Algorithms.,
  • Antenna arrays.,
  • Covariance.,
  • Noise reduction.,
  • Radio frequency interference.,
  • Signal processing.,
  • Signal reception.,
  • Weighting functions.

Edition Notes

StatementBy Ronald Louis Dilsavor.
SeriesNASA contractor report -- NASA CR-185493.
ContributionsUnited States. National Aeronautics and Space Administration.
The Physical Object
Pagination1 v.
ID Numbers
Open LibraryOL15408676M

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Analysis of modified SMI method for adaptive array weight control by Ronald Louis Dilsavor Download PDF EPUB FB2

Analysis of modified SMI method for adaptive array weight control The authors characterize the performance of the diagonally loaded sample matrix inverse (SMI) algorithm versus the number K of snapshots used in the covariance matrix estimate by providing O(1/K) statistics (bias and variance) of the array weights, output powers, and output Cited by: Get this from a library.

Analysis of modified SMI method for adaptive array weight control. [Ronald Louis Dilsavor; Randolph Lyle Moses; Ohio State University.; Lewis Research Center.].

Get this from a library. Analysis of modified SMI method for adaptive array weight control. [Ronald Louis Dilsavor; United States.

National Aeronautics and Space Administration.]. Analysis of Modified SMI Method for Adaptive Array Weight Control. An adaptive array is applied to the problem of receiving a desired signal in the presence of weak interference signals which need to be suppressed.

A modification, suggested by Gupta, of the sample matrix inversion (SMI) algorithm controls the array weights. In the modified SMI algorithm, interference suppression is increased by subtracting a fraction F of the noise power from the diagonal Cited by: 4.

Analysis of Modified SMI Method for Adaptive Array Weight Control. By Ronald Louis Dilsavor. Abstract. An adaptive array is used to receive a desired signal in the presence of weak interference signals which need to be suppressed.

A modified sample matrix inversion (SMI) algorithm controls the array weights. Analysis of Modified SMI Method for Adaptive Array Weight Control R.L. Dilsavor and R.L. Moses The Ohio State University ElectroScience Laboratory Kinnear Road Columbus, OH 7.

Author(s) 9. Performing Organisation Name and Address Sponsoring Organisation Name and Address NASA-Lewis Research Center Brookpark Road Cleveland. Analysis of Modified SMI Method for Adaptive Array Weight Control A Thesis Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of the Ohio State University bY Ronald Louis Dilsavor, B.S.E.E.

***** The Ohio State University (NASA-CB) ANALYSIS OF MODIFIED SMZ N8 9 9 3. The analysis method is based on the first order perturbation expansion of the projection operator. Analysis of Modified SMI Method for Adaptive Array Weight Control Digital control of.

Dilsavor* and R. Moses,""Analysis of Modified SMI Method for Adaptive Array Weight Control"." IEEE Transactions on Signal Process no. 2, - Beamforming with the sample matrix inversion method (SMI), using an antenna array, is considered for a GSM-signal.

The problem of generating the reference signal from the training sequence is. R.L. Dilsavor, R.L. MosesAnalysis of modified SMI method for adaptive array weight control IEEE Trans. Signal Process., 41 (2) (), pp. Google Scholar. Minimum variance beamformers are usually complemented with diagonal loading techniques in order to provide robust ness against finite sample size effects.

In spite of its usefulness and popularity, some of the insights of this interesting technique have remained inconspicuous for decades. This work presents a new asymptotic performance study that will shed some light on the finite sample size. The PCI method [4, 11, 5] of adaptive detection is based on reduced-rank nulling of interference.

A signif- icant advantage of the PCI method is that it achieves much more rapid adaptation [4, 5] LP. Kirsteins and D. Tufts than conventional methods such as adaptive loops [1] and the Sample Matrix Inverse (SMI) method [7]. Space-domain suppression techniques apply beamforming methods to deal with the high power interferences.

The adaptive optimum beamformer for passive radar application has been investigated in [2, 3]. Methods which utilize the eigen-structure of the spatial correlation matrix have been proposed in [4, 5]. Array processing for radar is well established in the literature, but only few of these algorithms have been implemented in real systems.

The reason may be that the impact of these algorithms on the overall system must be well understood. For a successful implementation of array processing methods exploiting the full potential, the desired radar task has to be considered and all processing.

Space-time adaptive processing (STAP) is a signal processing technique most commonly used in radar systems. It involves adaptive array processing algorithms to aid in target detection. Radar signal processing benefits from STAP in areas where interference is a problem (i.e. ground clutter, jamming, etc.).Through careful application of STAP, it is possible to achieve order-of-magnitude.

31 ∑ = = + M m j m AF I e m m 1 (θ,φ) (ζ δ) () where Im is the magnitude and δm is the phase of the weighting of the mth element. The normalized array factor is given by f {} AF AF (,) (,) max (,) θφ θφ θφ = () This would be the same as the array pattern if the array consisted of ideal isotropic.

Adaptive beamforming has been one of the most significant research areas in array signal processing, which has been widely used in radar, sonar, wireless communications, and many other fields [].The aim of adaptive beamforming algorithm is to extract the desired signal and suppress the interference as well as noise at the array output simultaneously.

The SNR and INR are set to 10 dB, 30dB respectively and all samples are used to compute weights for the sample matrix inversion (SMI) solution while the [3] LS algorithm including the forward method of [3]LS with sparse constraint (SC-[3]LS-F) and original forward method of [3]LS ([3]LS-F) only require one.

• Stochastic approximation of the steepest decent is used as an optimization method. SMI algorithm [5] • based on the inversion of sample correlation of array processor.

• Optimal weights are the estimates of the Weiner weight solution. • Used as an alternative to LMS algorithm for rapid convergence.

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