Complex Signal Amplitude Estimation and Adaptive Detection in Unknown Low-rank Interference

2006 ◽  
Author(s):  
Aleksandar Dogandzic ◽  
Benhong Zhang
Keyword(s):  
Signal Amplitude ◽  
Low Rank ◽  
Complex Signal ◽  
Adaptive Detection ◽  
Amplitude Estimation

Differential synchronous demodulation for small signal amplitude estimation

1993 ◽  
Vol 42 (5) ◽  
pp. 926-931 ◽  
Author(s):  
C.S. Koukourlis ◽  
V.K. Trigonidis ◽  
J.N. Sahalos
Keyword(s):  
Signal Amplitude ◽  
Small Signal ◽  
Amplitude Estimation

scholarly journals A Method of m-Point Sinusoidal Signal Amplitude Estimation

2016 ◽  
Vol 16 (5) ◽  
pp. 244-253
Author(s):  
Sergiusz Sienkowski
Keyword(s):  
Signal Amplitude ◽  
Original Method ◽  
Point Estimation ◽  
Sinusoidal Signal ◽  
Initial Signal ◽  
Amplitude Estimation ◽  
Starting Point ◽  
Special Cases ◽  
Point Estimator ◽  
Signal Period

Abstract The paper presents a new and original method of m-point estimation of sinusoidal signal amplitude. In this method, an m-point estimator is calculated on the basis of m initial signal samples. The way the estimator is constructed is explained. It is shown that the starting point for constructing the estimator is two initial signal samples. Next, in order to determine the estimator general form, three and m subsequent initial signal samples appearing in a signal period are used. Some special cases of an estimator are considered. Such an estimator is compared with a four-point estimator proposed by Vizireanu and Halunga. It is shown that the m-point estimator makes it possible to estimate the signal amplitude more accurately.

Bias in Amplitude Estimation of a Sine Signal with Known Frequency due to the Rounding Quantization Noise

2014 ◽  
Vol 667 ◽  
pp. 318-323
Author(s):  
Yuan Tian ◽  
Jie Luo
Keyword(s):  
Signal Amplitude ◽  
Quantization Noise ◽  
Amplitude Estimation ◽  
The Difference ◽  
Sine Signal ◽  
Period Of Oscillation

Based on properties of quantization, a method is proposed to derive the bias in amplitude estimation of a sine signal with known frequency due to quantization noise by the rounding quantizer. For a selected quantization unit, the bias oscillates and decays with the signal amplitude, and the period of oscillation is just the quantization unit. Different quantizers may contribute to different biases. A comparison with the bias due to the rounding-down quantizer shows that the difference between them depends on the signal amplitude, and it tends to be small as the signal amplitude increases, not monotonically. Therefore, by choosing appropriate quantizer and quantization unit, the bias in estimated amplitude due to quantization noise will be decreased.

scholarly journals Spectral Analysis of MST Radar Signal using Maximum Likelihood Estimation Algorithm

2019 ◽  
Vol 9 (2) ◽  
pp. 3372-3379
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Radar Data ◽  
Low Rank ◽  
Radar Signal ◽  
Complex Signal ◽  
Complex Data ◽  
Mst Radar ◽  
Frequency Components

In this work, we propose Maximum likelihood estimation of low- rank Toeplitz covariance matrix (MELT) with reduced complexity algorithm for computing the power spectral density of mesosphere-stratosphere-troposphere (MST) radar data. MELT is designed based on the method of majorization-minimization and it is an iterative algorithm to update the powers in each successive step. We tested MELT algorithm for complex signal, which contain multiple frequency components in existence of different noise conditions. For simulated complex data, it can be seen that MELT works much better for low Signal to Noise Ratio (SNR) conditions and also effectively detects the frequency components with a fine resolution in the existence with high noise impact. At last, MELT algorithm is applied to the radar data received from MST radar established at National Atmospheric Research laboratory (NARL), Gadhanki. MELT algorithm estimates the accurate Doppler spectra and thus in turn, estimate the wind parameters using Doppler profiles. For the purpose of validation, the obtained radar results through MELT are compared with the Global Positioning System (GPS) radiosonde.

scholarly journals Adaptive Detection of Structured Signals in Low-Rank Interference

2019 ◽  
Vol 67 (13) ◽  
pp. 3439-3454 ◽  
Author(s):  
Philip Schniter ◽  
Evan Byrne
Keyword(s):  
Low Rank ◽  
Adaptive Detection
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