Analysis of the Folded Systolic Array Based MVDR Beamformer (original) (raw)
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Adaptive Diagonal Loading for Robust Minimum Power Distortionless Response Beamformer
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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...
IEE Proceedings - Radar, Sonar and Navigation, 1996
The authors present a novel systolic array for minimum variance distortionless response (MVDK) adaptivc beamforming, based on inversc QR (IQR) updates. The array is made up of O ( N 2 + tnN) processing cells, iv being the number of-degrees of freedom and 112 the number of loolc directions, and allows a fully parallel and pipelined weight extraction. In this respect the proposed array overcoimcs the limitations of existing systolic adaptive beamformers which require O(mnN') cells and/or do not allow parallel pipelined weight extraction. In addition, the paper considers CORDIC realisation of systolic adaptive bcarnformers. Finally, it providcs a comparative study, via sirnuiation experiments, on the numerical properties of various systolic adaptive beamformers when implemented with CORDIC cells and operating on typical radar data.
2017
The performance of Minimum Variance Distortionless Response (MVDR) beamformer is sensitive to errors such as the steering vector errors, the finite snapshots, and unsatisfactory null-forming level. In this paper, a combination of MVDR with linear antenna arrays (LAAs) for two scanning angles process in the azimuth and elevation are used to illustrate the MVDR performance against error which results in acquiring the desired signal and suppressing the interference and noise. The impact of various parameters, such as the number of elements in the array, space separation between array elements, the number of interference sources, noise power level, and the number of snapshots on the MVDR are investigated. The MVDR performance is evaluated with two important metrics: beampattern of two scanning angles and Signal to Interference plus Noise Ratio (SINR). The results found that the MVDR performance improves as the number of array elements increases. The beampattern relies on the number of e...
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.
A beam former is a processor used with an array of antenna elements to provide a versatile form of spatial filtering. The sensor array collects spatial samples of Electromagnetic waves that are processed by the beam former. A beam former performs spatial filtering to separate signals that have overlapping frequency content but originate from different spatial locations. Beam forming techniques can be broadly divided into two categories: (i) Conventional beam formers. (ii) Adaptive beam formers. Interfering and jamming systems are becoming an increasing concern to the military and security industries worldwide. To combat these problems Phase array antennas and adaptive beam forming systems, in which the antenna beam pattern can be modified to reject interference, offers a potential solution. STAP is a twodimensional filtering technique for antenna array with multiple spatial channels. The name "space-time" describes the coupling of these spatial channels with pulse-Doppler waveforms. Application for STAP includes GMTI for airborne radar systems. In this paper we have presented our work of designing a MVDR beam former receiver based on STAP using MATLAB/Simulink. The beam former accepts data matrix through its 2 input channels, calculates the MVDR values and generates some mean square error, which are nothing but some fixed d.c levels. These d.c values when fed to the phase array antenna system results in electronic steering of antenna beam.
MINIMUM VARIANCE ADAPTIVE BEAMFORMING APPLIED TO A CIRCULAR SONAR ARRAY
The minimum variance (MV) beamformer, also known as the Capon or minimum variance distortionless response (MVDR) beamformer, uses the recorded wavefield to compute a set of optimal weights to be applied to each sensor, before coherently adding the sensor outputs. The weights are chosen such that the variance of the output is minimized while maintaining unit gain in the view direction. The MV beamformer offers improved resolution and image quality compared to the conventional delay-and-sum (DAS) beamformer. The MV beamformer was originally introduced for passive systems. When adapting the MV beamformer to active sonar, sub-array averaging is necessary in order to avoid signal cancellation of coherent signals, and to reduce the sensitivity to errors in the wavefield parameters. Sub-array averaging is a technique developed for flat, uniformly sampled arrays, and it is not immediately evident that this will give satisfactory results on a non-flat array. In this work, we have successfully implemented the MV beamformer on an active 1D circular sonar array suitable for fishery sonar. We demonstrate, through simulations, that it is feasible to apply the MV beamformer with sub-array averaging to a circular array. Our results have been verified using experimental data from the Simrad SX90 fishery sonar.
A Novel Adaptive Beamforming for Radar Systems
Modern Radars use phased array antenna for higher directive gains. These array antennas can be used to form a beam in the desired direction also known as beam steering, if there is any jammer present then it cannot be suppressed by just beamforming or beam steering, in such case we need to use Adaptive antenna. Adaptive antennas use the smart signal processing algorithms also known as adaptive algorithms. Adaptive beamforming is used to place a deep null in the direction of the Jammer, identify the spatial signals