Characterization of Received Signal Strength Perturbations Using Allan Variance

Luo, Chunbo; Casaseca‐de‐la‐Higuera, Pablo; McClean, Sally; Parr, Gerard; Ren, Peng

doi:10.1109/taes.2017.2768278

Cited by 20 publications

(21 citation statements)

References 42 publications

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“…21 The author derives a low complexity frequency domain channel estimation for faster than Nyquist (FTN) pilot transmission scenario with AWGN and whitening of colored noise. 25 Xiaogong proposed an improved Gaussian filter for nonlinear dynamic position ships to reduce the effect of cross-correlated colored noises. 23 The author proposed an improved spectrum sensing algorithm for cognitive radio to improve performance in the presence of colored noise.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The coefficient of various noise terms α is estimated practically for a different scenario, 25 and it is concluded that the contribution of h 0 and h −1 is dominant for a line of sight with reflection scenario, which is considered in this work. Further, the h α coefficient can be estimated by using parametric estimation techniques like the least squares (LS) algorithm.…”

Section: Allan Variancementioning

confidence: 99%

“…24 Luo confirms the presence of colored noise in wireless signals and suggests a Kalman filter-based noise removal technique for the real-time application like localization. 25 Xiaogong proposed an improved Gaussian filter for nonlinear dynamic position ships to reduce the effect of cross-correlated colored noises. This proposed filter has better performance than the Kalman filter technique.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The AVAR tool along with parametric estimation may analyze concerned time-dependent behavior and their proportionate contribution in the overall disturbance. The coefficient of various noise terms α is estimated practically for a different scenario, 25 and it is concluded that the contribution of h 0 and h −1 is dominant for a line of sight with reflection scenario, which is considered in this work.…”

Section: Allan Variancementioning

confidence: 99%

See 3 more Smart Citations

Performance improvement of wireless secret key generation with colored noise for IoT

Soni

Upadhyay

Kumar

2019

Int J Communication

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Achieving security in the Internet of things (IoT) networks by generating symmetric keys from the wireless channel parameters like received signal strength (RSS) is a promising approach. Despite the easy acquisition of the RSS signal, RSS-based security is less explored for IoT. In this work, we analyze the performance of RSS-based wireless physical layer key generation with correlated colored noise components and proposed a low complexity filtering approach to improve the performance for the IoT network. We started with providing a survey of various recent researches related to RSS-based key generation and also discussed correlated colored noise components with a few of the recent works considering them. Further, we analyze various colored noise components in the time domain by the Allan variance and Ljung-Box test. Furthermore, we develop a key generation model and proposed a moving window averaging-based filtering followed by Lloyd max quantization to improve the BDR performance, degraded due to the presence of correlated colored noise components. The simulation results show that the proposed preprocessing technique has a considerable improvement in the BDR performance, and the keys generated have sufficient randomness, which is verified by NIST test.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Allan Variancementioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Allan Variancementioning

confidence: 99%

See 2 more Smart Citations

Performance improvement of wireless secret key generation with colored noise for IoT

Soni

Upadhyay

Kumar

2019

Int J Communication

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“…Presently, the measure is effectively used for quantifying the noises in the measurement of other quantities [31,32], but it is particularly useful for evaluation of inertial motion capture sensors [33,34]. Allan variance [9] is defined as:σy2(τ)=12(y(t+τ)−y(t))2, where τ is the time intersample spacing, 〈·〉 denotes expected value.…”

Section: Introductionmentioning

confidence: 99%

On the Noise Complexity in an Optical Motion Capture Facility

Skurowski

Pawlyta

2019

Sensors

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Optical motion capture systems are state-of-the-art in motion acquisition; however, like any measurement system they are not error-free: noise is their intrinsic feature. The works so far mostly employ a simple noise model, expressing the uncertainty as a simple variance. In the work, we demonstrate that it might be not sufficient and we prove the existence of several types of noise and demonstrate how to quantify them using Allan variance. Such a knowledge is especially important for using optical motion capture to calibrate other techniques, and for applications requiring very fine quality of recording. For the automated readout of the noise coefficients, we solve the multidimensional regression problem using sophisticated metaheuristics in the exploration-exploitation scheme. We identified in the laboratory the notable contribution to the overall noise from white noise and random walk, and a minor contribution from blue noise and flicker, whereas the violet noise is absent. Besides classic types of noise we identified the presence of the correlated noises and periodic distortion. We analyzed also how the noise types scale with an increasing number of cameras. We had also the opportunity to observe the influence of camera failure on the overall performance.

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