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Abstract
For targets with complex motion, such as ships fluctuating with oceanic waves and high maneuvering airplanes, azimuth echo signals can be modeled as multicomponent quadratic frequency modulation (QFM) signals after migration compensation and phase adjustment. For the QFM signal model, the chirp rate (CR) and the quadratic chirp rate (QCR) are two important physical quantities, which need to be estimated. For multicomponent QFM signals, the cross terms create a challenge for detection, which needs to be addressed. In this paper, by employing a novel multi-scale parametric symmetric self-correlation function (PSSF) and modified scaled Fourier transform (mSFT), an effective parameter estimation algorithm is proposed—referred to as the Two-Dimensional product modified Lv’s distribution (2D-PMLVD)—for QFM signals. The 2D-PMLVD is simple and can be easily implemented by using fast Fourier transform (FFT) and complex multiplication. These measures are analyzed in the paper, including the principle, the cross term, anti-noise performance, and computational complexity. Compared to the other three representative methods, the 2D-PMLVD can achieve better anti-noise performance. The 2D-PMLVD, which is free of searching and has no identifiability problems, is more suitable for multicomponent situations. Through several simulations and analyses, the effectiveness of the proposed estimation algorithm is verified.
Highlights
In the past decades, radar signal processing has been a very important research field [1,2,3], especially estimating radar parameters [4,5,6]
We only focus on research into the frequency of the quadratic frequency modulation (QFM) signal [2,4]
In the proposed algorithm, which is similar to the frequency resolution of Fourier transform, the resolution for representing the parameters of chirp rate (CR) and quadratic chirp rate (QCR) relate to the signal length, which makes sense, since the 1-D QFM signals are converted into the 2-D single frequency signals in the CR-QCR domain
Summary
Radar signal processing has been a very important research field [1,2,3], especially estimating radar parameters [4,5,6]. The proposed estimation algorithm is based on a novel multi-scale parametric symmetric self-correlation function (PSSF) and the idea of keystone transform By means of this processing, the QFM signal is transformed into different 2-D frequency domains, but at the same coordinate position. Due to the values of scale factors have been selected by the selection criteria, which will be presented in Section 2.3.2, the relationships in Equation (10) can be obtained According to these relationships, an improved energy accumulation method is presented, which is called mSFTt,i − FFTτ,i. In order to describe the shortcoming of the EA method and explain the principle of the energy accumulation method in the proposed algorithm, we firstly consider one scale factor in Equation (17)
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