Robust Minimum Variance Beamforming

Lorenz, Robert; Boyd, Stephen

doi:10.1002/0471733482.ch1

Cited by 65 publications

(79 citation statements)

References 11 publications

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“…The exact interference-plus-noise covariance matrix R i + n is unavailable in application. What we can get in application is only the array snapshot which is described by formula (2). There the data actually contains desired signal, interference and random noise respectively.…”

Section: Traditional Algorithms For Robust Adaptive Beamformingmentioning

confidence: 99%

“…When adaptive array methods are applied to practice, the performance degradation of adaptive beamforming may become even more serious than that in the ideal case. Although adaptive beamformings have pretty good theoretical solutions, and some of them even have similar solutions, such as MVDR Beamformer [2], most of the adaptive methods have the drawbacks of sensitivity to slight mismatches of arr ay parameters that can easily occur in practical applications. Under this circumstance, robust adaptive beamformings [3] are capable of improving the performances of arr ay output.…”

Section: Introductionmentioning

confidence: 99%

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A new algorithm for robust adaptive beamforming

Lin

Bei-li

2010

2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering

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In practical array signal processing systems, traditional adaptive beamforming algorithms will degrade if some exploited assumptions become wrong or imprecise. In order to increase the robustness against the mismatch, robust beamforming brings about great improvement on array performances when compared with traditional methods. After introducing the traditional robust beamforming, it emphasizes the new robust beamforming which uses the Support Vector Machines (SVMs) to improve the generalization performance. By incorporating additional inequality constraints, this paper presents the modified SVM-based cost function and illustrates how it can be used to linear beamforming. In comparison with other robust beamforming techniques, simulation results show that the proposed SVM-based beam former has the desired robust performance both in no-mismatch and mismatch scenarios.

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Section: Traditional Algorithms For Robust Adaptive Beamformingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new algorithm for robust adaptive beamforming

Lin

Bei-li

2010

2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering

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“…More critically, such an approximately linear constrained estimate may not satisfy the original nonlinear constraint specified in (6). It is therefore desired to reduce this approximation-introduced error by including higher order terms while keeping the problem computationally tractable.…”

Section: Linearly Constrained State Estimationmentioning

confidence: 99%

“…765-766], which was previously applied for the joint estimation and calibration [14]. Similar techniques have been used for the design of filters for radar applications [1] and in robust minimum variance beamforming [6]. When M = 0, the constraint in (9) degenerates to a linear one.…”

Section: Nonlinearly Constrained State Estimationmentioning

confidence: 99%

Kalman Filtering with Nonlinear State Constraints

Yang¹,

Blasch

2009

IEEE Trans. Aerosp. Electron. Syst.

152

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An analytic method was developed by D. Simon and T. L. Chia to incorporate linear state equality constraints into the Kalman filter. When the state constraint was nonlinear, linearization was employed to obtain an approximately linear constraint around the current state estimate. This linearized constrained Kalman filter is subject to approximation errors and may suffer from a lack of convergence. We present a method that allows exact use of second-order nonlinear state constraints. It is based on a computational algorithm that iteratively finds the Lagrangian multiplier for the nonlinear constraints. Computer simulation results are presented to illustrate the algorithm.

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“…Similar techniques have been used for the design of filters for radar applications [1] and in robust minimum variance beamforming [7]. When M = 0, the constraint in (26) degenerates to a linear one.…”

Section: E(λ) = [… E I (λ) …] T = V T (G T ) -1 (H T Z -λM) (29c) T mentioning

confidence: 99%

Track Fusion with Road Constraints

Yang¹,

Blasch

2007

2007 10th International Conference on Information Fusion

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This paper is concerned with tracking of ground targets on roads and investigates possible ways to improve target state estimation via fusing a target's track with information about a road along which the target is believed to be traveling. A target track is estimated by a surveillance radar whereas a digital map provides the road network of a region under surveillance. When the information about roads is as accurate as (or even better than) radar measurements, it is desired naturally to incorporate such information (fusion) into target state estimation. In this paper, roads are modeled with analytic functions and its fusion with a target track is cast as linear or nonlinear state constraints in an optimization procedure. The constrained optimization is then solved with the Lagrangian multiplier, leading to a closed-form solution for linear constraints and an iterative solution for nonlinear constraints. Geometric interpretations of the solutions are provided for simple cases. Computer simulation results are presented to illustrate the algorithms.

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Robust Minimum Variance Beamforming

Cited by 65 publications

References 11 publications

A new algorithm for robust adaptive beamforming

A new algorithm for robust adaptive beamforming

Kalman Filtering with Nonlinear State Constraints

Track Fusion with Road Constraints

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