Parameter Estimation of Radar Targets with Macro-Motion and Micro-Motion Based on Circular Correlation Coefficients

Motion compensation is a crucial step to inverse synthetic aperture radar imaging, and envelope correction is the foundation of motion compensation. Research on envelope correction based on the small-angle imaging model has matured after years of development. However, the small-angle imaging model is not applicable to parameter estimation and imaging of micro-motion targets. According to the characteristics of the micro-motion targets and the superiorities of terahertz imaging radar, an envelope correction method for micro-motion targets in the terahertz region was proposed in this paper, including the jump error correction based on periodic correction and drift error compensation based on nonlinear fitting. Then a 330 GHz imaging radar and two experiments on corner reflectors and a warhead model were introduced. The validity of the method was verified by the experimental results, and the performance of the method was proved by the inverse Radon transform of the range profile sequences.

“…It means that we can estimate the micro-motion period

T

Section: Envelope Correction Methodsmentioning

confidence: 99%

Envelope Correction of Micro-Motion Targets in the Terahertz ISAR Imaging

Yang

Deng

Wang

et al. 2018

“…For the parameter estimation, due to the fact that the coning period has been estimated in the radar community [8], we assume the coning angle speed ω is known. Now, the unknown parameters in the emissivity-area product model are the coefficient vector

x = ε \cdot s = ε \cdot {[a_{0}, a_{1}, \dots, a_{K}, b_{0}, b_{1}, \dots, b_{K}, c]}^{T}

(see Equations (2) and (9)) and the motion and initial attitude parameters

m = {[α_{N}, β_{N}, α_{0}, β_{0}]}^{T}

.…”

Section: Algorithmmentioning

confidence: 99%

“…The micro-motion features, which reflect the unique dynamic and structural characteristics of the target, serve as important features for target recognition and classification [3,4,5]. The time-frequency representation based methods have been used to extract the micro-motion parameter for maneuvering space objects among the radar community [6,7,8]. Other than using the radar data, the discriminating system based on the IR (infrared) camera is also an important research field [9,10].…”

Section: Introductionmentioning

confidence: 99%

Estimating Shape and Micro-Motion Parameter of Rotationally Symmetric Space Objects from the Infrared Signature

Lü

Zhao

et al. 2016

Shape serves as an important additional feature for space target classification, which is complementary to those made available. Since different shapes lead to different projection functions, the projection property can be regarded as one kind of shape feature. In this work, the problem of estimating the projection function from the infrared signature of the object is addressed. We show that the projection function of any rotationally symmetric object can be approximately represented as a linear combination of some base functions. Based on this fact, the signal model of the emissivity-area product sequence is constructed, which is a particular mathematical function of the linear coefficients and micro-motion parameters. Then, the least square estimator is proposed to estimate the projection function and micro-motion parameters jointly. Experiments validate the effectiveness of the proposed method.

“…The feature of micro-Doppler was firstly found in a coherent laser radar system. With a wavelength ranging from 1/100,000 to 1/10,000 of conventional radar’s wavelength, laser radar provides higher phase/frequency sensitivity, which is beneficial to the measurement and analysis of fine movement features of the targets [1,2,3,4,5,6,7,8,9]. In 2006, Chen et al introduced micro-Doppler into conventional radar and pointed out that Doppler signals of the target echoes are mostly multi-component time-varying signals.…”

Section: Introductionmentioning

confidence: 99%

“…Linear time-frequency distribution has no cross terms but its time-frequency resolution is low. By contrast, second-order Cohen class time-frequency distribution offers a higher time-frequency resolution but its downside is the cross terms being tainted by multi-component signals [4,10,11,12,13,14,15,16,17,18]. …”

Section: Introductionmentioning

confidence: 99%

Micro-Doppler Signal Time-Frequency Algorithm Based on STFRFT

Pang

Han

Hou

et al. 2016

This paper proposes a time-frequency algorithm based on short-time fractional order Fourier transformation (STFRFT) for identification of a complicated movement targets. This algorithm, consisting of a STFRFT order-changing and quick selection method, is effective in reducing the computation load. A multi-order STFRFT time-frequency algorithm is also developed that makes use of the time-frequency feature of each micro-Doppler component signal. This algorithm improves the estimation accuracy of time-frequency curve fitting through multi-order matching. Finally, experiment data were used to demonstrate STFRFT’s performance in micro-Doppler time-frequency analysis. The results validated the higher estimate accuracy of the proposed algorithm. It may be applied to an LFM (Linear frequency modulated) pulse radar, SAR (Synthetic aperture radar), or ISAR (Inverse synthetic aperture radar), for improving the probability of target recognition.