Signal detection with noisy reference for passive sensing

Cui, Guolong; Liu, Jun; Li, Hongbin; Himed, Braham

doi:10.1016/j.sigpro.2014.09.034

Cited by 81 publications

(46 citation statements)

References 23 publications

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“…The previous detection probability results have shown the efficiency of the optimum signal reconstruction in dealing with noisy reference signal scenarios. In [14] , a set of four detection algorithms have been proposed for the detection with a noisy reference signal scenario. These algorithms are based on the generalized likelihood ratio test (GLRT) principle.…”

Section: Comparison With the Glrt Detectormentioning

confidence: 99%

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Performance analysis of the reference signal reconstruction for DVB-T passive radars

2019

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The present work investigates the reference signal reconstruction method for passive coherent location (PCL) radars exploiting Digital Video Broadcasting-Terrestrial (DVB-T) signals. The reference signal reconstruction improves the signal-to-noise ratio (SNR) of the reference signal, which enhances the detection probability for DVB-T based PCL systems. The existing approach for the reference signal reconstruction is performed by demodulating the received reference signal to retrieve the transmitted quadrature amplitude modulation (QAM) symbols and modulate them to obtain the reconstructed signal. For low SNR values, the QAM symbol detection is accompanied with a significant error, which leads to a mismatch between the reconstrued signal and the exact one. Consequently, the expected improvement due to the signal reconstruction is limited. In this work, we propose an optimum reconstruction method which filters the detected QAM symbol prior to the remodulation step. To validate the proposed method, we retrieved analytic expressions for the detection and false-alarm probabilities and verified them through Monte-Carlo simulations. In addition, real-data sets were used to evaluate the proposed method performances. The results show that the proposed method outperforms the conventional one by extending the feasibility of the reference signal reconstruction for low SNR values.

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Section: Comparison With the Glrt Detectormentioning

confidence: 99%

“…Therefore, a performance degradation of the CC detector compared to the MF is expected. The impact of the reference signal noise on the detection performance has been addressed in many studies [13][14][15][16][17] . It increases the detection noise floor, which buries low-power target echoes and degrades the detection probability.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of the reference signal reconstruction for DVB-T passive radars

2019

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“…Passive target detection with a noisy reference channel has recently been considered in [1,6] for an unknown deterministic signal…”

Section: Introductionmentioning

confidence: 99%

Passive detection of rank-one signals with a multiantenna reference channel

Santamarı́a

Scharf

Cochran

et al. 2016

2016 24th European Signal Processing Conference (EUSIPCO)

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In this work we consider a two-channel passive detection problem, in which there is a surveillance array where the presence/absence of a target signal is to be detected, and a reference array that provides a noise-contaminated version of the target signal. We assume that the transmitted signal is an unknown rank-one signal, and that the noises are uncorrelated between the two channels, but each one having an unknown and arbitrary spatial covariance matrix. We show that the generalized likelihood ratio test (GLRT) for this problem rejects the null hypothesis when the largest canonical correlation of the sample coherence matrix between the surveillance and the reference channels exceeds a threshold. Further, based on recent results from random matrix theory, we provide an approximation for the null distribution of the test statistic.

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“…For example, in passive radar systems, target detection exploits readily available, non-cooperative illuminators of opportunity such as broadcast radio or television transmissions [21]- [23]. In such systems, the transmitted signal is out of control and generally unknown to a passive receiver, and a noisy reference may or may not be available [24], [25]. However, the normalized PSD of the transmitted signal may be known, and it would make sense to exploit this feature to improve detection.…”

Section: Introductionmentioning

confidence: 99%

Multiantenna GLR Detection of Rank-One Signals With Known Power Spectral Shape Under Spatially Uncorrelated Noise

Sala-Alvarez

Vazquez-Vilar

López-Valcarce

et al. 2016

IEEE Trans. Signal Process.

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Abstract-We establish the generalized likelihood ratio (GLR) test for a Gaussian signal of known power spectral shape and unknown rank-one spatial signature in additive white Gaussian noise with an unknown diagonal spatial correlation matrix. This is motivated by spectrum sensing problems in dynamic spectrum access (DSA), in which the temporal correlation of the primary signal can be assumed known up to a scaling, and where the noise is due to an uncalibrated receive array. For spatially independent identically distributed (i.i.d.) noise, the corresponding GLR test reduces to a scalar optimization problem, whereas the GLR detector in the general non-i.i.d. case yields a more involved expression, which can be computed via alternating optimization methods. Low signal-to-noise ratio (SNR) approximations to the detectors are given, together with an asymptotic analysis showing the influence on detection performance of the signal power spectrum and SNR distribution across antennas. Under spatial rank-P conditions, we show that the rank-one GLR detectors are consistent with a statistical criterion that maximizes the output energy of a beamformer operating on filtered data. Simulation results support our theoretical findings in that exploiting prior knowledge on the signal power spectrum can result in significant performance improvement.

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Signal detection with noisy reference for passive sensing

Cited by 81 publications

References 23 publications

Performance analysis of the reference signal reconstruction for DVB-T passive radars

Performance analysis of the reference signal reconstruction for DVB-T passive radars

Passive detection of rank-one signals with a multiantenna reference channel

Multiantenna GLR Detection of Rank-One Signals With Known Power Spectral Shape Under Spatially Uncorrelated Noise

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