Improved single frequency estimation with wide acquisition range

Awoseyila, Adegbenga B.; Kasparis, C.; Evans, B.G.

doi:10.1049/el:20083015

Cited by 23 publications

(16 citation statements)

References 6 publications

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“…The estimation variance is adopted as the measure of estimation accuracy. The MCRLB [22] is also calculated as an absolute measure of the theoretical optimal valuation:…”

Section: Simulationmentioning

confidence: 99%

Phase Clustering Based Modulation Classification Algorithm for PSK Signal over Wireless Environment

et al. 2016

Mobile Information Systems

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Promptitude and accuracy of signals’ non-data-aided (NDA) identification is one of the key technology demands in noncooperative wireless communication network, especially in information monitoring and other electronic warfare. Based on this background, this paper proposes a new signal classifier for phase shift keying (PSK) signals. The periodicity of signal’s phase is utilized as the assorted character, with which a fractional function is constituted for phase clustering. Classification and the modulation order of intercepted signals can be achieved through its Fast Fourier Transform (FFT) of the phase clustering function. Frequency offset is also considered for practical conditions. The accuracy of frequency offset estimation has a direct impact on its correction. Thus, a feasible solution is supplied. In this paper, an advanced estimator is proposed for estimating the frequency offset and balancing estimation accuracy and range under low signal-to-noise ratio (SNR) conditions. The influence on estimation range brought by the maximum correlation interval is removed through the differential operation of the autocorrelation of the normalized baseband signal raised to the power ofQ. Then, a weighted summation is adopted for an effective frequency estimation. Details of equations and relevant simulations are subsequently presented. The estimator proposed can reach an estimation accuracy of10-4even when the SNR is as low as-15 dB. Analytical formulas are expressed, and the corresponding simulations illustrate that the classifier proposed is more efficient than its counterparts even at low SNRs.

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“…The estimation variance is adopted as the measure of estimation accuracy. The MCRLB [22] is also calculated as an absolute measure of the theoretical optimal valuation:…”

Section: Simulationmentioning

confidence: 99%

Phase Clustering Based Modulation Classification Algorithm for PSK Signal over Wireless Environment

et al. 2016

Mobile Information Systems

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“…In the latter, Tretter [19] was the first person to propose a phasebased approach by introducing an approximated and linear model for instantaneous signal phase. Subsequently, a great deal of improvements have erupted mainly in the following three parts: taking differences over one or more delays, which is well-known as Kay and generalized Kay estimators [20][21][22][23][24][25]; introducing autocorrelations and their different functions, such as Fitz, L&R, and M&M estimators [26][27][28][29][30]; and preprocessing by means of lowpass filter, blocking average, and filter banks to increase signal-to-noise ratio (SNR) [31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Experimental and Analytical Studies on Improved Feedforward ML Estimation Based on LS-SVR

Xue-qian

2013

Discrete Dynamics in Nature and Society

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Maximum likelihood (ML) algorithm is the most common and effective parameter estimation method. However, when dealing with small sample and low signal-to-noise ratio (SNR), threshold effects are resulted and estimation performance degrades greatly. It is proved that support vector machine (SVM) is suitable for small sample. Consequently, we employ the linear relationship between least squares support vector regression (LS-SVR)’s inputs and outputs and regard LS-SVR process as a time-varying linear filter to increase input SNR of received signals and decrease the threshold value of mean square error (MSE) curve. Furthermore, it is verified that by taking single-tone sinusoidal frequency estimation, for example, and integrating data analysis and experimental validation, if LS-SVR’s parameters are set appropriately, not only can the LS-SVR process ensure the single-tone sinusoid and additive white Gaussian noise (AWGN) channel characteristics of original signals well, but it can also improves the frequency estimation performance. During experimental simulations, LS-SVR process is applied to two common and representative single-tone sinusoidal ML frequency estimation algorithms, the DFT-based frequency-domain periodogram (FDP) and phase-based Kay ones. And the threshold values of their MSE curves are decreased by 0.3 dB and 1.2 dB, respectively, which obviously exhibit the advantage of the proposed algorithm.

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“…The problem of estimating the frequency of a complex exponential from a finite number of samples in additive white noise arises in many fields including radar, sonar, measurement, wireless communications, and speech processing [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. For instance, frequency estimation of singletone sinusoidal signals is an important technique for carrier recovery in wireless communication systems [6,10].…”

Section: Introductionmentioning

confidence: 99%

“…However, the ML estimator is not computationally simple [14]. Suboptimal algorithms with lower computation have been proposed, such as the linear prediction-based estimators [2,3], the autocorrelation-based estimators [4][5][6][7][8][9][10][11], and the periodogram-based estimators [12][13][14]. The linear prediction algorithms are to estimate the frequency from the coefficients of the predictor.…”

Section: Introductionmentioning

confidence: 99%

“…The estimator proposed in [7] has similar MSE performance and estimation range as Fitz and Luise and Reggiannini's estimators but has lower computational complexity. Estimators in [8][9][10][11] are proposed to achieve wider acquisition range. In this paper, we approximate the original signal with the spectral lines inside the main lobe of the zero-padded measurements' DFT spectrum and then calculate the autocorrelation function based on the approximated signal.…”

Section: Introductionmentioning

confidence: 99%

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An Estimation-Range Extended Autocorrelation-Based Frequency Estimator

Wang

Chen

2010

EURASIP J. Adv. Signal Process.

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We address the problem of autocorrelation-based single-tone frequency estimation. It has been shown that the frequency can be estimated from the phase of the available signal's autocorrelation with fixed lag. A large lag results in better performance but at the same time limits the estimation range. New methods have been proposed to extend the estimation range. In this paper, a new estimator which is a robust hybrid of periodogram-based and autocorrelation-based frequency estimators is presented. We propose to calculate the autocorrelation function with spectral lines inside the available signal's main lobe spectrum. We show that the new estimator obtains full estimation range of [−π, π). The theoretical performance bound is also deduced. Performance analysis and simulations demonstrate that the proposed estimator approaches the CRLB.

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Improved single frequency estimation with wide acquisition range

Cited by 23 publications

References 6 publications

Phase Clustering Based Modulation Classification Algorithm for PSK Signal over Wireless Environment

Phase Clustering Based Modulation Classification Algorithm for PSK Signal over Wireless Environment

Experimental and Analytical Studies on Improved Feedforward ML Estimation Based on LS-SVR

An Estimation-Range Extended Autocorrelation-Based Frequency Estimator

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