Signal-to-Noise power ratio available from photomultipliers used as star detectors in star tracking systems. A method of assessment

1962 ◽  
Vol 23 (3) ◽  
pp. 209-216
Author(s):  
D.S.J. Chapman
Keyword(s):  
Noise Power ◽  
Power Ratio ◽  
Tracking Systems ◽  
Signal To Noise ◽  
Star Tracking ◽  
Method Of Assessment

scholarly journals Path Integral Approach to Nondispersive Optical Fiber Communication Channel

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 607
Author(s):  
Aleksei V. Reznichenko ◽  
Ivan S. Terekhov
Keyword(s):  
Optical Fiber ◽  
Communication Channel ◽  
Optical Fiber Communication ◽  
Integral Approach ◽  
Nonlinearity Parameter ◽  
Noise Power ◽  
Power Ratio ◽  
Large Signal ◽  
Signal To Noise ◽  
Fiber Communication

In the present paper we summarize the methods and results of calculations for the theoretical informational quantities obtained in our works for the nondispersive optical fiber channel. We considered two models: the per-sample model and the model where the input signal depends on time. For these models we found the approach for the calculation of the mutual information exactly in the nonlinearity parameter but for the large signal-to-noise power ratio. Using this approach for the per-sample model we found the lower bound of the channel capacity in the intermediate power range.

scholarly journals On Asymptotic Efficiency of the M2M4 Signal-to-Noise Estimator for Deterministic Complex Sinusoids

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 4950
Author(s):  
Gianmarco Romano
Keyword(s):  
Covariance Matrix ◽  
Gaussian Noise ◽  
Asymptotic Efficiency ◽  
Noise Power ◽  
Finite Sample ◽  
Signal To Noise ◽  
Numerical Examples ◽  
Unknown Phase ◽  
The Moment ◽  
Asymptotically Efficient

The moment-based M2M4 signal-to-noise (SNR) estimator was proposed for a complex sinusoidal signal with a deterministic but unknown phase corrupted by additive Gaussian noise by Sekhar and Sreenivas. The authors studied its performances only through numerical examples and concluded that the proposed estimator is asymptotically efficient and exhibits finite sample super-efficiency for some combinations of signal and noise power. In this paper, we derive the analytical asymptotic performances of the proposed M2M4 SNR estimator, and we show that, contrary to what it has been concluded by Sekhar and Sreenivas, the proposed estimator is neither (asymptotically) efficient nor super-efficient. We also show that when dealing with deterministic signals, the covariance matrix needed to derive asymptotic performances must be explicitly derived as its known general form for random signals cannot be extended to deterministic signals. Numerical examples are provided whose results confirm the analytical findings.

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