Broadband Beamforming Using the Least Square Method Improved via Adaptive Diagonal Loading (original) (raw)
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IEEE Transactions on Antennas and Propagation, 1991
This paper is concerned with adaptive null steering in single receiver adaptive arrays. The single receiver structure allows only output power for a given set of weights to be measured. The problem, then, is to adaptively adjust the weights of the antenna array, based on output power measurements only, so as to reject interference signals while maintaining a fixed response in a given look direction. Current least mean square (LMW-based iterative techniques use perturbations of the beamformer weights to obtain an estimate of the gradient of a given cost (power) function in order to update an initial guess. The key idea of this paper is to determine the optimal beamformer weights in a single step, by estimating the covariance matrix of the array sensor outputs using a weight perturbation technique. Based on this covariance matrix estimate three different approaches for finding the beamformer weights are studied. The first one corresponds to a sample matrix inversion (SMI) scheme, with the sample covariance matrix replaced by the one obtained from the perturbation estimation method, while in the second approach the weights are determined using an eigenvalue decomposition of the covariance matrix estimate. In the third approach the direction-ofarrivals (DOA's) of the incoming wavefronts are first estimated, and then, in a second step, the beamformer weights are calculated from the DOA estimates. The advantage of the third approach is that this method is not affected by correlation between tbe different sources. In comparison with iterative techniques, the new approach offers much faster beamfonning, albeit at the expense of increased susceptibility to unmodeled characteristics. Subsequent modifications of the algorithm in light of experimental studies have enhanced robustness by providing scope for accommodating model errors.
Beamforming based on Constrained Linear Estimation: Analysis and Simulations
This paper presents a mathematical analysis of adaptive beamforming array based on linear estimation for nonrandom vectors of data. It involves the use of linear estimation of random vectors, but it is shown that the optimal values of the constants that minimize the mean-squared error for the estimation can be obtained by changing the nature of the input random vector by a deterministic one. Furthermore, this result is applied to the antenna beamforming in the presence of additive white Gaussian noise. Computer simulation results show the validity, reliability and the limitations that the model could have. As a second part, the optimal values of the constrained beamforming model are compared with those obtained by the LMS, -NLMS, RLS and exponentially-weighted RLS algorithms in order to verify the result and convergence of the weights.
A Study on Various Types of Beamforming Algorithms
This paper deals with the study of various types of Beamforming algorithms. A beam former is a processor used in conjunction with an array of sensors to provide a versatile form of spatial filtering. The sensor array collects spatial samples of propagating wave fields, which are processed by the beam former. The objective is to estimate the signal arriving from a desired direction in the presence of noise and interfering signals. A beam former performs spatial filtering to separate signals that have over lapping frequency content but originate from different spatial locations.
A Novel High Resolution Adaptive Beamforming Algorithm for High Convergencea
This paper introduces a new robust four way LMS and variable step size NLMS beam forming algorithm to reduce interference in a smart antenna system. This algorithm is able to resolve signals arriving from narrowband sources propagating plane waves close to the array end fire. The results of previously used adaptive algorithm have the fixed step size NLMS will result in a trade-off issue between convergence rate and steady-state MSE of NLMS algorithm. This issue is solved by using four way LMS and VSSNLMS which will improve the efficiency of the convergence point. The proposed algorithm implemented reduces the mean square error (MSE) and shows faster convergence rate when compared to the conventional NLMS.
A Simple Adaptive Beamforming Algorithm with Interference Supression
Modeling and simulation of uniform linear array usi ng Matrix Inversion-Normalized Least Mean Square (MI-NLMS) adaptive beam forming with minimum Bit Er ror Rate (BER) is developed for smart antenna applications. We have modeled a linear array of ant ennas for 20°Half Power Beam Width (HPBW) and obtained the beam formation with digital modulation of 16 point Quadrature Amplitude Modulation (QAM).This modulation technique is used for the sys tems like CDMA,Wi-Fi (IEEE802.11) and WiMAX (IEEE 802.16).The algorithm have the advantage of both block adaptation and sample by sample techniques which shows that the performance of blo ck adaptation and normalization of Least Mean Square (LMS) improves the system capacity and minim ize bit error rate (BER) upto 10 -4 for the signal to noise ratio of 13 dB's. The Quadrature amplitude mo dulation (QAM) allows us to send more bits per symbol to achieve higher throughput and to overcome fading and other interferences. The simulation is done in...
Performance Evaluation of the RLS Adaptive Beamforming Algorithm
The Recursive Least Square algorithm optimization method used in this paper for the synthesis of antenna array radiation pattern in adaptive beam forming. In this paper optimum value of weights of each antenna element is determined which produces radiation pattern with minimum side lobe level. Optimization is done using 8-elements array with an optimum inter-element spacing to avoid grating lobes. Simulation is done in MATLAB and can be taken into consideration, to obtain the desired beam in the lookup direction.
Adaptive Array Beamforming Using an Enhanced RLS Algorithm
International Journal on AdHoc Networking Systems
In recent times, the use of Smart antennas (SA) in wireless communications has greatly increased; this is because of their ability to increase the coverage and capacity of a network. There are two main functions which SAs perform: direction of arrival (DOA) and beamforming. SAs are able to produce the main beams towards a desired user and at the same time, form nulls in the direction of interfering signals. They are able to achieve this by the use of beamforming algorithms. The performance of an enhanced Recursive Least Squares (RLS) algorithm which takes into account an enhanced gain factor of the RLS algorithm is evaluated in this paper for adaptive array beamforming. Simulation results show that the enhanced RLS reduces mean square error (MSE), smoothens filter output improves SNR;thus, improving the gain of the RLS algorithm. The result of this work will lead to an increased range of beamforming and directivity of the smart antenna over a long range communication network.
Adaptive Diagonal Loading for Robust Minimum Power Distortionless Response Beamformer
2013
Most of the modern radar and sonar uses adaptive beam forming to eliminate the interference. The basic requirement of the adaptive algorithm is to eliminate the interference and increase the Signal-to-Interference-plusNoise Ratio (SINR). The efficiency of the adaptive algorithm depends on the accurate estimation of the spatial covariance matrix. Generally the data used to estimate the covariance matrix should not contain any signal of interest, if so adaptive algorithm eliminates the signal also along with the interference. Diagonal loading is one of the most widely used and effective method to improve robustness of adaptive beamformer, but selecting the diagonal loading value has become a key aspect. In the absence of complete knowledge of signal characteristics and continuously changing environment, fixed diagonal loading method may not give desired performance, hence the adaptive method is essential. In this paper we propose adaptive diagonal loading method for Minimum Power Dist...