A Fast Beamforming Algorithm for Large Arrays

Hung, Eric K.; Turner, R.M.

doi:10.1109/taes.1983.309349

Cited by 83 publications

(52 citation statements)

References 6 publications

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“…From a historical perspective, the fundamental idea of excising interference from a desired signal by vector subspace partitioning has appeared in many forms for a variety of disciplines and applications, including adaptive beamforming, subspace detectors, and interference cancellers used in SONAR and RADAR. For examples, see [17]- [23].…”

Section: Introductionmentioning

confidence: 99%

Auxiliary antenna-assisted interference mitigation for radio astronomy arrays

Jeffs

Warnick

2005

IEEE Trans. Signal Process.

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Abstract-Interferometric image synthesis in radio astronomy is plagued by signal corruption from man-made sources. The very weak signals of interest can be overwhelmed by such interference. Recent work has proposed using array signal processing techniques of adaptive beamforming, adaptive filtering, and subspace projection to remove interference prior to image synthesis. In some practical scenarios, we have found poor cancellation performance when using such methods. Because signal-of-interest levels in radio astronomy (RA) are usually well below the noise, even interference to noise ratios (INR) less than unity can affect signal estimation. At these low INRs, it is difficult to estimate interference parameters or statistics with sufficient accuracy for high-performance adaptive cancellation or subspace projection. By adding a few (one to three) low-gain (relative to the primary telescope dishes) "auxiliary" antennas to an array, it is possible to overcome this problem. This paper will show that using such antennas (e.g. commercial-grade 3-m dishes) with an existing array can significantly improve interference rejection. New extensions to subspace projection spatial filtering methods are presented, along with analytical and simulated results for performance comparison.

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Section: Introductionmentioning

confidence: 99%

Auxiliary antenna-assisted interference mitigation for radio astronomy arrays

Jeffs

Warnick

2005

IEEE Trans. Signal Process.

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“…[4,9,10,11,15,16,21]. This lower rank estimate would still be a good approximation to the original but it would dramatically reduce the required computer processing power.…”

Section: Principal Component-signal Dependentmentioning

confidence: 99%

Comparative study on PC-SD and MWF Algorithms for STAP RADAR

Fathima¹,

Venu²

2017

IJET

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Abstract-The paramount challenge in the radar system is to alleviate the consequences of cold (homogeneous) clutter, severe dynamic (heterogeneous) hot clutter and jamming interferences while estimating the states of targets under track. To surmount this challenge, Space-Time Adaptive Processing (STAP) intensify the competence of radar systems. Space-time Adaptive Processing is a two-dimensional filtering technique for antenna array with multiple spatially distributed channels. The name 'Space-Time' elucidate the coupling of multifarious spatial channels with pulse-Doppler waveforms. The term "Adaptive processor" signifies that it can employ using a variety of algorithms on many platforms ranging from space satellites to a small low flying unmanned aerial. In order to develop STAP algorithms to operate in adverse environments, where intense environmental interference can reduce STAP proficiency to detect and track ground targets. STAP can effectively suppress these interferences and maximize the signal to interference plus noise ratio (SINR). Methods such as principal component analysis, Multi-stage Weiner Filter (MWF) are applied to STAP system. Rank and Minimum square error are parameters considered for estimating the performance of two stated techniques. Keywords-STAP (space Time Adaptive Processing), homogeneous clutter, heterogeneous clutter, PC-SD, MWF, Rank, MSE.I. INTRODUCTION An adaptive processing uses spatial and temporal domains for signal detection ,which offers significant advantages in a variety of applications including radar, sonar, and satellite communication [1].The signal processing for radar systems uses multiple antenna elements that coherently process multiple pulses. An adaptive array of spatially distributed sensors, which processes multiple temporal snapshots, surmount the directivity and resolution limitations. Specifically, STAP using STAP creates an aptness to suppress interfering signals while simultaneously conserving gain of the desired signal. Additional gain afforded by an array of sensors leads to enhancement in the signal-to-noise ratio, resulting in an ability to place deep nulls in the direction of interfering signals.Advanced airborne radar systems are equipped to detect targets in presence of both clutter and jamming. The ground clutter scrutinize by an airborne platform is extended in both angle and range and is spread in Doppler frequency because of the platform motion. STAP can significantly improve airborne radar performance. Computational complexity and the need to estimate non-stationary interference with limited data forces considerations of partially adaptive architectures. The STAP computational complexity is driven not just be the size of a single adaptive problem, but also by the number of adaptive problems that must be solved per coherent processing interval (CPI) [2].Fully adaptive STAP, though optimum given perfect knowledge, is impractical for two reasons. First is the computational burden of solving large system of equations in real-time. Second is the inte...

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“…In fact, the performances of the existing adaptive array algorithms are known to degrade substantially in the presence of even slight mismatches between the actual and presumed array responses to the desired signal [10]- [12]. Similar types of degradation can take place when the array response is known precisely but the training sample size is small, namely the mismatch between the actual and the estimated covariance matrix [13]- [15]. Therefore, robust approaches to adaptive beamforming appear to be of primary importance in these cases [16] [17].…”

Section: Robust Beamforming Overviewmentioning

confidence: 99%

Robust Beamforming and DOA Estimation

Liu¹

2012

Fourier Transform Applications

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A Fast Beamforming Algorithm for Large Arrays

Cited by 83 publications

References 6 publications

Auxiliary antenna-assisted interference mitigation for radio astronomy arrays

Auxiliary antenna-assisted interference mitigation for radio astronomy arrays

Comparative study on PC-SD and MWF Algorithms for STAP RADAR

Robust Beamforming and DOA Estimation

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