Joint Compensation of Jitter Noise and Time-Shift Errors in Multichannel Sampling System

Araghi, Hesam; Akhaee, Mohammad Ali; Amini, Arash

doi:10.1109/tim.2018.2882259

Cited by 2 publications

(7 citation statements)

References 44 publications

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“…As mentioned in [24], [25], let the measured acceleration at any sampling instant i be calculated by (1).…”

Section: B Modelmentioning

confidence: 99%

“…However, previous work in this field focused on assessment of uncertainty from a quantitative perspective rather than probabilistic modeling. On the other hand, sampling time uncertainty has been modeled as white Gaussian process in other domains such as analog to digital converters (ADCs) as illustrated in [24], [25]. Thus, for mathematical tractability we adopt white Gaussian model for sampling time uncertainty in smartphones.…”

Section: B Modelmentioning

confidence: 99%

“…As mentioned in [25], nth derivative is approximately evaluated by (20), where sinc (n) is the nth derivative of the cardinal sine function defined by (21), and K is the number of time samples, refer to appendix A for more information. Consequently, (2) can be written as (22) and the vector form is expressed by (23), where D is a matrix whose rows are defined by (24).…”

Section: Crlb For Acceleration Estimationmentioning

confidence: 99%

“…Hence, as described in [25], each element of the Fisher information matrix (FIM) is evaluated by (25), where the covariance matrix of a is given by (26) and its derivative with respect to x i is given by (27). Then, according to the CRLB theorem, the minimum mean squared error (MSE) obtained by an unbiased estimator is given by (28).…”

Section: Crlb For Acceleration Estimationmentioning

confidence: 99%

“…Figure 9 compares the CRLB to the linear estimation accuracy. It is worth noting that a linear estimator as mentioned in [25], corresponds tox = a, i.e. M SE| Linear = tr{Σ a Σ a Σ a } in case no over sampling is assumed.…”

Section: Crlb For Acceleration Estimationmentioning

confidence: 99%

See 4 more Smart Citations

Accuracy Limits of Embedded Smart Device Accelerometer Sensors

Ibrahim

El‐Tawil

2020

IEEE Trans. Instrum. Meas.

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Smartphones are an indispensable tool in modern day-today life. Their widespread use has spawned numerous applications targeting diverse domains such as bio-medical, environment sensing and infrastructure monitoring. In such applications, the accuracy of the sensors at the core of the system is still questionable, since these devices are not originally designed for high accuracy sensing purposes. In this work, we investigate the accuracy limits of one of the commonly used sensors, namely, smartphone accelerometer. We focus on the efficacy of a smartphone as an acceleration measuring device, rather than focusing only on the accuracy of its internal accelerometer chip. This holistic approach includes additional errors that arise from the device operating system such as sampling time uncertainty. Hence, we propose a novel smart device accelerometer error model that includes the traditional additive noise as well as sampling time uncertainty errors represented by a white Gaussian process. The model is validated experimentally using shake table experiments, and maximum likelyhood estimation (MLE) is used to estimate the sampling time uncertainty standard deviation. Moreover, we derive the Cramer-Rao lower bound (CRLB) of acceleration estimation based on the proposed model.

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“…As mentioned in [24], [25], let the measured acceleration at any sampling instant i be calculated by (1).…”

Section: B Modelmentioning

confidence: 99%

Section: B Modelmentioning

confidence: 99%