SYMBOL RATE. ESTIMATION BOUNDS

2021 ◽  
Author(s):  
Е.А. БРУСИН
Keyword(s):  
Performance Evaluation ◽  
Lower Bound ◽  
Signal To Noise Ratio ◽  
Observation Interval ◽  
Spectral Characteristics ◽  
Interval Duration ◽  
Signal To Noise ◽  
Rate Estimation ◽  
Estimation Performance ◽  
Symbol Rate

В статье обсуждаются проблемы оценивания символьной частоты и определения эффективности получаемых оценок. Предложенный подход позволяет определить границы оценивания символьной частоты для сигналов различных видов модуляции и спектральныххарактеристик. Получено аналитическое выражение для нормированной модифицированной нижней границы Крамера-Рао оценивания символьной частоты для сигналов с косинусным скруглением. Представлены зависимости соответствующих границ от коэффициента скругления, отношения сигнал/шум на символ и длительности интервала наблюдения. The article discusses the problems of symbol rate estimation and the estimation performance evaluation. The proposed approach allows us to determine the symbol rate estimation bounds for signals of various types of modulation and various spectral characteristics. An analytical expression for the symbol rate modified Kramer-Rao lower bound is obtained for signals with root raised cosine spectrum. The dependences of the corresponding boundaries on the roll-off factor, the signal-to-noise ratio per symbol, and the observation interval duration are presented.

A Novel Symbol Rate Estimation Algorithm for Phase Modulating Signals in Wireless Communications

2013 ◽  
Vol 443 ◽  
pp. 392-396
Author(s):  
Peng Zhou ◽  
Chi Sheng Li
Keyword(s):  
Signal To Noise Ratio ◽  
Estimation Algorithm ◽  
Frequency Resolution ◽  
Phase Shift Keying ◽  
Phase Jump ◽  
Rate Estimation ◽  
Awgn Channel ◽  
Symbol Rate ◽  
Shift Keying ◽  
Simulation Results

In this paper, we proposed a new symbol rate estimation algorithm for phase shift keying (PSK) and qua drawtube amplitude modulation (QAM) signals in AWGN channel First we constructe a delay-multiplied signal, from which we obtaine the modulated information. Then we calculated the instantaneous autocorrelation of the delay-multiplied signal to pick out the phase jump. To eliminate the restriction of frequency resolution in fast Fourier transform, we performed a Chirp-Z transform to find out the exact spectral line which represente the symbol rate of the signal to be analyzed. Compared with the existing algorithms, it is a simple solution that has a better performance and accuracy in low signal-to-noise-ratio channel conditions. Simulation results show that the probability of relative estimating deviation below 0.1% reaches 100% and the average and standard variance of absolute estimation deviation are at the magnitude of 10-2 when SNR is over 2dB.

scholarly journals The Geometry of Noise in Color and Spectral Image Sensors

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4487
Author(s):  
Axel Clouet ◽  
Jérôme Vaillant ◽  
David Alleysson
Keyword(s):  
Signal To Noise Ratio ◽  
Spectral Estimation ◽  
Spectral Characteristics ◽  
Color Space ◽  
Color Correction ◽  
Signal To Noise ◽  
Noise Evaluation ◽  
Noise Ratio ◽  
Correction Step ◽  
The Impact

Digital images are always affected by noise and the reduction of its impact is an active field of research. Noise due to random photon fall onto the sensor is unavoidable but could be amplified by the camera image processing such as in the color correction step. Color correction is expressed as the combination of a spectral estimation and a computation of color coordinates in a display color space. Then we use geometry to depict raw, spectral and color signals and noise. Geometry is calibrated on the physics of image acquisition and spectral characteristics of the sensor to study the impact of the sensor space metric on noise amplification. Since spectral channels are non-orthogonal, we introduce the contravariant signal to noise ratio for noise evaluation at spectral reconstruction level. Having definitions of signal to noise ratio for each steps of spectral or color reconstruction, we compare performances of different types of sensors (RGB, RGBW, RGBWir, CMY, RYB, RGBC).

Optical Range and Range Rate Estimation for Teleoperator Systems

1974 ◽  
Vol 18 (5) ◽  
pp. 487-492
Author(s):  
Nicholas L. Shields ◽  
Mark Kirkpatrick ◽  
Thomas B. Malone ◽  
Carl T. Huggins
Keyword(s):  
Signal To Noise Ratio ◽  
Size Estimation ◽  
Image Size ◽  
Signal To Noise ◽  
Estimation Errors ◽  
Television System ◽  
Rate Estimation ◽  
Rate Controlled ◽  
The Mean ◽  
Range Rate

Range and range rate are crucial parameters which must be available to the operator during remote controlled orbital docking operations. A method was developed for the estimation of both these parameters using an aided television system. An experiment was performed to determine the human operator's capability to measure displayed image size using a fixed reticle or movable cursor as the television aid. The movable cursor was found to yield mean image size estimation errors on the order of 2.3 percent of the correct value. This error rate was significantly lower than that for the fixed reticle. Performance using the movable cursor was found to be less sensitive to signal-to-noise ratio variation than was that for the fixed reticle. The mean image size estimation errors for the movable cursor correspond to an error of approximately 2.25 percent in range suggesting that the system has some merit. Determining the accuracy of range rate estimation using a rate controlled cursor will require further experimentation.

scholarly journals Performance Evaluation of Frame based Adaptive Compressed Sensing and Non-Adaptive Compressed Sensing based on Average Frame Signal to Noise Ratio

2018 ◽  
Vol 7 (4.10) ◽  
pp. 202
Author(s):  
T. S. Arulananth ◽  
R. Satheesh ◽  
P. Bhaskara Reddy
Keyword(s):  
Performance Evaluation ◽  
Compressed Sensing ◽  
Signal To Noise Ratio ◽  
Projection Matrix ◽  
Noise Levels ◽  
Signal To Noise ◽  
Normal Frame ◽  
Noise Ratio

The primary inspiration of our work is to discovering upgrades in the current Compressed Sensing procedure that utilizations Non Adaptive Projection Matrix rule. Normal Frame Signal-to-Noise Ratio (AFSNR) is intended to evaluate the show of the Frame-Based Adaptive Compressed Sensing with the Non-Adaptive Compressed Sensing (CS). It is a developing sign securing strategy and straight gathers the signs in a compacted shape on the off chance that they are meager on some specific premise. Proposed approach utilizes Adaptive Projection Matrix in light of edge examination which gives fundamentally enhanced discourse recreation quality and decreases the noise levels.

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