Random matrix theory inspired passive bistatic radar detection of low-rank signals

Gogineni, Sandeep; Setlur, Pawan; Rangaswamy, Muralidhar; Nadakuditi, Raj Rao

doi:10.1109/radar.2015.7131264

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…Recognizing the fact that a perfect reference signal for passive radar is not typically available in practice, no optimality claims can be made on the performance of the cross correlation detector. In [16] principles of random matrix theory were applied to the passive radar detection problem to improve upon the performance of the cross correlation receiver for this problem. A novel contribution in [17] develops for the first time for radar a commonly used technique from multiple antenna communications, which relies upon a probative MIMO mode for channel estimation.…”

Section: Fig 4: Sinr Performance Vs Number Of Eigenvectorsmentioning

confidence: 99%

Recent trends and findings in cognitive radar

Rangaswamy

Jones

Smith

2015

2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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We provide an overview of r have come about in the area of cognitive decade. The area of cognitive radar involv operation to overcome the challenges environments, difficult targets, and a spectrum. In particular this construct is dev all available resources on transmit and rece situational awareness of the operation maximize system performance. The multid the field is highlighted and salient advanc motivate the interested reader to delve deepe

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Section: Fig 4: Sinr Performance Vs Number Of Eigenvectorsmentioning

confidence: 99%

Recent trends and findings in cognitive radar

Rangaswamy

Jones

Smith

2015

2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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“…The work in [4] derives the GLRT in passive radar problem that models the received signal as a deterministic waveform scaled by an unknown single-input single-output (SISO) channel and under white noise of either known or unknown variance. A passive detector that exploits the low-rank structure of the received signal has been proposed in [5]. Instead of computing the cross-correlation between the surveillance and reference channel measurements, the ad-hoc detector proposed in [5] cross-correlates the dominant left singular vectors of the matrices containing the observations acquired at both channels.…”

Section: Introductionmentioning

confidence: 99%

“…A passive detector that exploits the low-rank structure of the received signal has been proposed in [5]. Instead of computing the cross-correlation between the surveillance and reference channel measurements, the ad-hoc detector proposed in [5] cross-correlates the dominant left singular vectors of the matrices containing the observations acquired at both channels.…”

Section: Introductionmentioning

confidence: 99%

Canonical correlations for target detection in a passive radar network

Wang

Scharf

Santamarı́a

et al. 2016

2016 50th Asilomar Conference on Signals, Systems and Computers

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In this work, we consider a two-channel multiple-input multiple-output (MIMO) passive detection problem, in which there is a surveillance array and a reference array. The reference array is known to carry a linear combination of broadband noise and a subspace signal of known dimension but unknown basis. The question is whether the surveillance channel carries a linear combination of broadband noise and a subspace signal of unknown basis, which is correlated with the subspace signal in the reference channel. We consider a second-order detection problem where these subspace signals are structured by an unknown, but common, p-dimensional random vector of symbols transmitted from sources of opportunity, and then received through unknown M × p matrices at each of the M-element arrays. The noises in each channel have arbitrary spatial correlation. We derive the generalized likelihood ratio test (GLRT) statistic and show it is a monotone function of canonical correlations between the reference and surveillance channels.

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Roy’s largest root under rank-one perturbations: The complex valued case and applications

Dharmawansa

Nadler

Shwartz

2019

Journal of Multivariate Analysis

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The largest eigenvalue of a Wishart matrix, known as Roy's largest root (RLR), plays an important role in a variety of applications. Most works to date derived approximations to its distribution under various asymptotic regimes, such as degrees of freedom, dimension, or both tending to infinity. However, several applications involve finite and relative small parameters, for which the above approximations may be inaccurate. Recently, via a small noise perturbation approach with fixed dimension and degrees of freedom, Johnstone and Nadler derived simple yet accurate stochastic approximations to the distribution of Roy's largest root in the real valued case, under a rank-one alternative. In this paper, we extend their results to the complex valued case. Furthermore, we analyze the behavior of the leading eigenvector by developing new stochastic approximations. Specifically, we derive simple stochastic approximations to the distribution of the largest eigenvalue under five common complex single-matrix and double-matrix scenarios. We then apply these results to investigate several problems in signal detection and communications. In particular, we analyze the performance of RLR detector in cognitive radio spectrum sensing and constant-modulus signal detection in the high signal-to-noise ratio (SNR) regime. Moreover, we address the problem of determining the optimal transmit-receive antenna configuration (here optimality is in the sense of outage minimization) for rank-one multiple-input and multiple-output Rician-Fading channels at high SNR.

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Random matrix theory inspired passive bistatic radar detection of low-rank signals

Cited by 8 publications

References 11 publications

Recent trends and findings in cognitive radar

Recent trends and findings in cognitive radar

Canonical correlations for target detection in a passive radar network

Roy’s largest root under rank-one perturbations: The complex valued case and applications

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