Ricean parameter estimation using phase information in low SNR environments

Morabito, A.N.; Percival, Donald B.; Sahr, John D.; Berkowitz, Z.M.P.; Vertatschitsch, Laura

doi:10.1109/lcomm.2008.072147

Cited by 9 publications

(5 citation statements)

References 5 publications

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“…22 For discrete digital devices, SNR is defined as the ratio of net signal value to root mean square of the noise in the signal. In TOF camera, the photon shot noise is the dominant noise in the signal, associated with the Poisson-distributed nature of the arrival process of photons.…”

Section: Signal To Noise Performance Of Tof Cameramentioning

confidence: 99%

Statistical analysis of measurement processes for time-of-flight cameras

Mufti

Mahony

2009

SPIE Proceedings

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In recent years, the demand for 3D vision systems has increased in fields such as detection and recognition, motion modelling, 3D environment reconstruction and tracking. This has motivated the development of range image technology, especially Time-of-Flight (TOF) cameras, that provide direct measurement of distance between the camera and the targeted surface. These devices have an advantage over traditional range data sensors due to their capability to provide frame rate range data over a full image array. The quality of the measurement of these sensors depends heavily on signal-to-noise (SNR) of the incoming signal and the subsequent processing algorithms. In phase shift TOF cameras, phase shift sampling is used to measure amplitude, phase and the offset (intensity) of the received signal. Each of these measurements has an associated statistical distribution that affects the SNR of the TOF signal, limiting the reliability of 3D range data. It is crucial to understand the statistical distributions of these three parameters for accurate distance measurement analysis especially in low SNR scenarios. In this paper, we provide explicit noise models for the three parameters of amplitude, phase and intensity. We use this analysis to provide an improved estimation of error in range measurement.

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Section: Signal To Noise Performance Of Tof Cameramentioning

confidence: 99%

Statistical analysis of measurement processes for time-of-flight cameras

Mufti

Mahony

2009

SPIE Proceedings

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“…The k estimator of Azemi et al (2004) is based on the statistics of the instantaneous frequency of the received signal at the mobile station. In order to estimate the Rice factor in low SNR environments, the phase information of received signal has been used by Morabito et al (2008). The Rice factor along with some other parameters is also estimated using weighted LS (WLS) and ML criteria (Sieskul and Jitapunkul, 2005;Sieskul and Kaiser, 2005).…”

Section: Introductionmentioning

confidence: 99%

Robust channel estimation in the Rician distributed flat fading multiple-input–multiple-output channel

Nooralizadeh

2015

Transactions of the Institute of Measurement and Control

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In this paper, the mean square error (MSE) of the new technique, the shifted scaled least squares (SSLS), and the minimum MSE (MMSE) estimator is analysed in the Rician distributed flat fading multiple-input–multiple-output (MIMO) channels. First, the closed form expressions are obtained for MSE of the estimators using the estimated and the actual mathematical expectation matrix of the channel and the matrix of channel covariances. It is analytically and numerically shown that the performance of the estimators is less sensitive to the erroneous estimation of the Rice factor at the receiver. On the other hand, it is shown that the performance of the MMSE estimator is quite sensitive to the erroneous estimation of the channel correlation coefficient. In order to estimate the channel Rice factor, two algorithms are also proposed in this paper. These algorithms work based on the optimal training signal and least squares (LS) technique. Finally, the estimated Rice factor is used in the SSLS and MMSE estimators. Simulation results confirm the efficiency of the algorithms and the robustness of the above-mentioned estimators to the erroneous estimation of Rice factor.

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“…Recent versions of both approaches can be found in the literature, e.g. [1] describes a time-segment approach and [2] describes a frequencysegment approach. However the frequency-segment method in [2] assumes the sinusoidal frequencies are known, and a considerable extension is required when this is not true.…”

Section: Introductionmentioning

confidence: 99%

“…[1] describes a time-segment approach and [2] describes a frequencysegment approach. However the frequency-segment method in [2] assumes the sinusoidal frequencies are known, and a considerable extension is required when this is not true. Therefore, we first develop this extension, and then provide a comparison of the two approaches based on their accuracy, sample and segment size dependence, frequency resolution, and computational complexity.…”

Section: Introductionmentioning

confidence: 99%

A comparison of methods for estimating broadband noise in the frequency domain

Hush

Pawley

Myers

et al. 2011

2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)

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Estimating the noise component of a signal that consists of sinusoids plus broadband noise is a ubiquitous problem. Most methods work in the time-domain, but frequencydomain methods can be computationally more efficient and invariant to the time-domain noise distribution. We compare two prominent frequency-domain approaches, one that computes statistics over periodograms of multiple time segments, and another that computes statistics over frequency segments from a single periodogram. We explore the accuracy-resolution tradeoff for both approaches and provide comparisons of accuracy, sample and segment size dependence, frequency resolution, and computational complexity for each method.

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Ricean parameter estimation using phase information in low SNR environments

Cited by 9 publications

References 5 publications

Statistical analysis of measurement processes for time-of-flight cameras

Statistical analysis of measurement processes for time-of-flight cameras

Robust channel estimation in the Rician distributed flat fading multiple-input–multiple-output channel

A comparison of methods for estimating broadband noise in the frequency domain

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