Reduced-rank adaptive filtering

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

doi:10.1109/78.554317

Cited by 279 publications

(170 citation statements)

References 9 publications

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“…Instead of selecting dominant eigenvectors, selecting the eigenvectors that contain a large correlation with the desired user array response given by results in a better approximation. This approach is similar to one technique in [25] where the authors sort the basis vectors based on the cross spectral norm, defined as More precisely, let with . Then we can write and obtain the energy of the output signal where and is the -th eigenvalue in .…”

Section: Appendix Apn Design Using Cross Spectral Projectionsmentioning

confidence: 99%

Analog Beamforming in MIMO Communications With Phase Shift Networks and Online Channel Estimation

Venkateswaran

Veen

2010

IEEE Trans. Signal Process.

358

272

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Abstract-In multiple-input multiple-output (MIMO) systems, the use of many radio frequency (RF) and analog-to-digital converter (ADC) chains at the receiver is costly. Analog beamformers operating in the RF domain can reduce the number of antenna signals to a feasible number of baseband channels. Subsequently, digital beamforming is used to capture the desired user signal. In this paper, we consider the design of the analog and digital beamforming coefficients, for the case of narrowband signals. We aim to cancel interfering signals in the analog domain, thus minimizing the required ADC resolution. For a given resolution, we will propose the optimal analog beamformer to minimize the mean squared error between the desired user and its receiver estimate. Practical analog beamformers employ only a quantized number of phase shifts. For this case, we propose a design technique to successively approximate the desired overall beamformer by a linear combination of implementable analog beamformers. Finally, an online channel estimation technique is introduced to estimate the required statistics of the wireless channel on which the optimal beamformers are based.Index Terms-ADC power consumption, analog beamforming, matching pursuit, passive RF phase shifters.

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Section: Appendix Apn Design Using Cross Spectral Projectionsmentioning

confidence: 99%

Analog Beamforming in MIMO Communications With Phase Shift Networks and Online Channel Estimation

Venkateswaran

Veen

2010

IEEE Trans. Signal Process.

358

272

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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 this section we show that instead of selecting the eigenvectors corresponding to the dominant eigenvalues, selecting eigenvectors corresponding to the N d largest elements of the cross spectral norm cxs = Λ −1 U H rxs (cf. (4)) results in a reduced rank minimum MSE (MMSE) solution [7,8]. The vector rxs can be estimated using the LRB outputs and training signals similar to the technique in Section 4.1.…”

Section: Design 2: Cross Spectral Projectionsmentioning

confidence: 99%

Partial beamforming to reduce ADC power consumption in antenna array systems

Venkateswaran

Veen

2008

2008 IEEE 9th Workshop on Signal Processing Advances in Wireless Communications

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In this paper, we consider a receiver setup with fewer analog to digital convertors (ADC's) than antennas. An analog preprocessing network (APN) is placed before the ADC's to reduce power consumption in the receiver. A set of low resolution beamformers is used to design the APN and cancel contributions of interfering users. Simulation results show that introduction of such APN design algorithms for a narrowband channel with 3 to 4 interfering users, 6 antennas and 3 ADCs results in a reduction of the total consumed receiver power by 15%.Index Terms-analog beamforming, reduced rank processing, cross-spectral projections, passive weighing structures.

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Reduced-rank adaptive filtering

Cited by 279 publications

References 9 publications

Analog Beamforming in MIMO Communications With Phase Shift Networks and Online Channel Estimation

Analog Beamforming in MIMO Communications With Phase Shift Networks and Online Channel Estimation

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

Partial beamforming to reduce ADC power consumption in antenna array systems

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