Multistage partially adaptive STAP CFAR detection algorithm

Goldstein, J.S.; Reed, I. S.; Zulch, Peter

doi:10.1109/7.766945

Cited by 123 publications

(64 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The PC-SI weight vector would be Where the weight vector is a function of both covariance matrix and cross correlation vector and cross correlation vector . Derived from the original weiner filter, the multi-stage weiner filter was introduced in [8].Its constrained form structure is shown in figure.4: Fig.4: The filter structure of the MWF In forward recursion ,the filter decomposes the sampled data snapshot x with a sequence of orthogonal projection like B 0 [5].Rank reduction can be accomplished by truncating these decomposition stages to a desired number r MWF .The result is a reduced rank transformation basis that spans the Krylov subspace instead of the eigenvector basis like PC. [12,17].…”

Section: Principal Component-signal Dependentmentioning

confidence: 99%

“…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].…”

mentioning

confidence: 99%

See 2 more Smart Citations

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

Fathima¹,

Venu²

2017

IJET

View full text Add to dashboard Cite

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...

show abstract

Section: Principal Component-signal Dependentmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

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

Fathima¹,

Venu²

2017

IJET

View full text Add to dashboard Cite

show abstract

“…A family of the Krylov subspace methods has been investigated thoroughly in the recent years. This class of reduced-rank algorithms, including the multistage Wiener filter (MSWF) [12], [18] and the auxiliary-vector filters (AVF) [19]- [21], projects the observation data onto a lower-dimensional Krylov subspace. These methods are very complex to implement in practice and suffer from numerical problems despite their improved convergence and tracking performance.…”

Section: Introductionmentioning

confidence: 99%

Reduced-Rank STAP Schemes for Airborne Radar Based on Switched Joint Interpolation, Decimation and Filtering Algorithm

Lamare

Wang

2010

IEEE Trans. Signal Process.

139

121

View full text Add to dashboard Cite

“…Minimal sample support methods which include reduced-rank STAP [2], chapters 5,6, and [9], multistage Wiener filtering techniques [5], joint-domain localized algorithms [6], least squares space-time filters exploiting the property of finite correlation length [2], chapter [7], principal components techniques [7], pre/post-Doppler STAP [1] based also on a partial a-priori knowledge of the disturbance scenarios, methods exploiting structural information 1 about the disturbance covariance matrix [8,9,10,11,12], and, finally, covariance tapers [7]. 2.…”

Section: Introductionmentioning

confidence: 99%

Knowledge-Based Generalized Likelihood Ratio Test (KB-GLRT): Exploiting Knowledge of the Clutter Ridge in Airborne Radar

Maio¹,

Farina²,

Wicks³

2004

View full text Add to dashboard Cite

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. In this paper we address knowledge-based radar detection for STAP applications. To this end we exploit, at the design stage, the characteristic structure of the clutter ridge and devise two decision rules according to the Generalized Likelihood Ratio Test (GLRT) and the two-step GLRT criteria. We first focus on the case of a clutter ridge with integer slope and then discuss the more general framework of a non-integer slope parameter. With reference to this last case we only provide approximate GLRT detectors due to the analytical difficulties connected with the exact solution of the problem.The performance analysis shows that the new knowledge-based systems achieve a performance level very close to the optimum detector which assumes the perfect knowledge of the clutter covariance matrix and can outperform some previously proposed adaptive detection schemes.

show abstract

Multistage partially adaptive STAP CFAR detection algorithm

Cited by 123 publications

References 37 publications

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

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

Reduced-Rank STAP Schemes for Airborne Radar Based on Switched Joint Interpolation, Decimation and Filtering Algorithm

Knowledge-Based Generalized Likelihood Ratio Test (KB-GLRT): Exploiting Knowledge of the Clutter Ridge in Airborne Radar

Contact Info

Product

Resources

About