Further investigation on time‐domain maximum likelihood estimation of chirp signal parameters

“…are obtained from the principal values, and no PU is performed. The choice of N 1 is very important, and it is shown that N 1 is SNR related [62]. Because in a real implementation we have no a priori knowledge of the input SNR, a fixed N 1 is a reasonable choice.…”

Section: Compound Time-frequency Domain Parameter Estimationmentioning

confidence: 99%

“…Defining the initialisation time as N 1 , the data phases

normal∠ D P_{M - m}^{normal′} false[r_{m}^{normal′} false(k false), 1 false]

for k = 1, …, N 1 are obtained from the principal values, and no PU is performed. The choice of N 1 is very important, and it is shown that N 1 is SNR related [62]. Because in a real implementation we have no a priori knowledge of the input SNR, a fixed N 1 is a reasonable choice.…”

Section: Compound Time‐frequency Domain Parameter Estimationmentioning

confidence: 99%

Compound time‐frequency domain method for estimating parameters of uniform‐sampling polynomial‐phase signals on the entire identifiable region

Deng

Zhang

et al. 2016

IET signal process.

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Parameter estimation of polynomial-phase signals (PPSs) observed in additive white Gaussian noise (AWGN) is addressed. Most of the existing estimators cannot work on a fully identifiable region. Using the algebraic number theory, McKilliam et al. proposed a least squares unwrapping (LSU) estimator, which can operate on the entire identifiable region. However, its computational load may be large, especially when the number of samples is large. In this study, the authors first extend the amplitude-weighted phase-based estimator (AWPE) for sinusoidal and chirp signals to PPSs and derive a time domain maximum likelihood estimator. The performance is analysed and compared with the Cramér-Rao lower bound (CRLB). Then, the authors propose an iterative compound time-frequency domain parameter estimation method, which includes a coarse estimation step and a fine estimation step conducted by the discrete polynomial phase transform and AWPE estimator, respectively. Monte-Carlo simulations show that the proposed method can work on the entire identifiable region and that it outperforms the existing state-of-the-art estimators. Its computational complexity is considerably lower than that of the LSU estimator, while its threshold signal-to-noise ratio is a few decibels higher than that of the LSU estimator.

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“…second‐order polynomial phase signals (PPSs), arise in many signal processing applications. The most notable applications are in radar, sonar, and wireless communications [1–5]. In recent years, a variety of algorithms have been proposed to estimate the chirp parameters.…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation for chirp signals using the spectrum phase

Jiang

2020

IET Radar, Sonar & Navigation

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Further investigation on time‐domain maximum likelihood estimation of chirp signal parameters

Cited by 13 publications

References 15 publications

Unambiguous velocity estimation method based on intra‐pulse cross‐correlation

Unambiguous velocity estimation method based on intra‐pulse cross‐correlation

Compound time‐frequency domain method for estimating parameters of uniform‐sampling polynomial‐phase signals on the entire identifiable region

Parameter estimation for chirp signals using the spectrum phase

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