Data adaptive signal estimation by singular value decomposition of a data matrix

Tufts, D.W.; Kumaresan, R.; Kirsteins, Ivars

doi:10.1109/proc.1982.12367

Cited by 180 publications

(70 citation statements)

References 7 publications

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“…This estimator sought to minimize the mean square error of the reconstructed signal. In this paper, De Moor also showed that it was impossible to recover the original noise column space of the exact data signal using the method Tufts et al presented in [20]. He proved that the angle between the true and estimated subspaces would always be a function of the signal-to-noise ratio.…”

Section: Signal Subspace Techniques For Sinusoidal Signaismentioning

confidence: 84%

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Signal subspace speech enhancement with perceptual post-filtering

Klein

Kabal

2002

IEEE International Conference on Acoustics Speech and Signal Processing

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Section: Signal Subspace Techniques For Sinusoidal Signaismentioning

confidence: 84%

“…presented a method for retrieving the signal component from a noisy data set [20]. Their method entailed creating a Hankel data matrix, calculating the SVD and nulling the singular values corresponding to the noise signal alone.…”

Section: Signal Subspace Techniques For Sinusoidal Signaismentioning

confidence: 99%

Signal subspace speech enhancement with perceptual post-filtering

Klein

Kabal

2002

IEEE International Conference on Acoustics Speech and Signal Processing

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“…The idea to perform SS method was originally proposed in [4] where a modified SVD is used for reconstruction of noise free series. A general framework for recovering noise free series has been presented in [5].…”

Section: Introductionmentioning

confidence: 99%

Singular spectrum analysis based on the minimum variance estimator

Hassani¹

2010

Nonlinear Analysis: Real World Applications

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Abstract-In recent years Singular Spectrum Analysis (SSA), used as a powerful technique in time series analysis, has been developed and applied to many practical problems. In this paper, we introduce the SSA technique based on the minimum variance estimator. We also consider the SSA technique based on the minimum variance and structured total least squares estimators in reconstructing and forecasting time series. A well-known time series data set, namely, monthly accidental deaths in the USA time series, is used in examining the performance of the technique. The results are compared with several classical methods namely, Box-Jenkins SARIMA models, the ARAR algorithm and the Holt-Winter algorithm.

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“…Reduced dimension STAP Algorithms are required to ease both computation and training support. [3,4,5].This paper utilizes the framework of space time adaptive processing for radar. In STAP, the sensor is composed of K elements and each element is followed by J taps spaced at the pulse repetition interval.…”

mentioning

confidence: 99%

“…Reduced dimension STAP Algorithms are required to ease both computation and training support. [3,4,5].…”

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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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Data adaptive signal estimation by singular value decomposition of a data matrix

Cited by 180 publications

References 7 publications

Signal subspace speech enhancement with perceptual post-filtering

Signal subspace speech enhancement with perceptual post-filtering

Singular spectrum analysis based on the minimum variance estimator

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

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