Ballistic targets micro-motion and geometrical shape parameters estimation from sparse decomposition representation of infrared signatures

2017 ◽  
Vol 56 (4) ◽  
pp. 1276 ◽  
Author(s):  
Junliang Liu ◽  
Shangfeng Chen ◽  
Huanzhang Lu ◽  
Bendong Zhao
Keyword(s):  
Parameters Estimation ◽  
Geometrical Shape ◽  
Shape Parameters ◽  
Sparse Decomposition ◽  
Micro Motion

scholarly journals Micro-Motion Dynamics and Shape Parameters Estimation Based on an Infrared Signature Model of Spatial Targets

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 755 ◽  
Author(s):  
Dongya Wu ◽  
Huanzhang Lu ◽  
Bendong Zhao ◽  
Junliang Liu ◽  
Ming Zhao
Keyword(s):  
Radiation Intensity ◽  
Infrared Radiation ◽  
Estimation Method ◽  
Optimization Techniques ◽  
Parameters Estimation ◽  
Geometrical Shape ◽  
Infrared Signature ◽  
Micro Motion ◽  
Periodic Fluctuations ◽  
Motion Dynamics

Infrared imaging is widely applied in the discrimination of spatial targets. Extracting distinguishable features from the infrared signature of spatial targets is an important premise for this task. When a target in outer space experiences micro-motion, it causes periodic fluctuations in the observed infrared radiation intensity signature. Periodic fluctuations can reflect some potential factors of the received data, such as structure, dynamics, etc., and provide possible ways to analyze the signature. The purpose of this paper is to estimate the micro-motion dynamics and geometry parameters from the observed infrared radiation intensity signature. To this end, we have studied the signal model of the infrared radiation intensity signature, conducted the geometry and micro-motion models of the target, and we proposed a joint parameter estimation method based on optimization techniques. After analyzing the estimation results, we testified that the parameters of micro-motion and geometrical shape of the spatial target can be effectively estimated by our estimation method.

Micromotion dynamics and geometrical shape parameters estimation of exoatmospheric infrared targets

2016 ◽  
Vol 55 (11) ◽  
pp. 113103 ◽  
Author(s):  
Junliang Liu ◽  
Yabei Wu ◽  
Huanzhang Lu ◽  
Bendong Zhao
Keyword(s):  
Parameters Estimation ◽  
Geometrical Shape ◽  
Shape Parameters

Cramer-Rao bounds for micro-motion parameters estimation in coherent laser detection

2017 ◽  
Vol 46 (7) ◽  
pp. 706002
Author(s):  
郭力仁 Guo Liren ◽  
胡以华 Hu Yihua ◽  
王云鹏 Wang Yunpeng
Keyword(s):  
Parameters Estimation ◽  
Laser Detection ◽  
Micro Motion ◽  
Motion Parameters

Micro-motion Parameters Estimation Using Randomized Stepped Frequency Radar

2019 ◽  
Author(s):  
Chuncheng Zhao ◽  
Yimin Liu ◽  
Tianyao Huang ◽  
Lei Wang ◽  
Huaiying Tan
Keyword(s):  
Parameters Estimation ◽  
Micro Motion ◽  
Motion Parameters ◽  
Stepped Frequency

A New ISAR Imaging Method of Ballistic Midcourse Target

2011 ◽  
Vol 65 ◽  
pp. 485-490
Author(s):  
Teng Lei ◽  
Jin Mang Liu ◽  
Gang Wang ◽  
Song Li
Keyword(s):  
Azimuth Angle ◽  
Imaging Method ◽  
Cross Term ◽  
Imaging Algorithm ◽  
Sparse Decomposition ◽  
Range Resolution ◽  
Range Cell ◽  
Target Imaging ◽  
Isar Imaging ◽  
Micro Motion

A new micro-motion ISAR imaging algorithm based on the MP sparse decomposition is proposed in this paper. The algorithm use the changes of azimuth angle caused by micro-motion to achieve high cross range resolution, and decompose the echoes of the same range cell before Wigner-Ville transformation to eliminate the cross-term interference. Compared with the traditional Range-Doppler algorithm and the Wigner-Ville imaging algorithm, the new algorithm considered here exhibits better imaging precision and is without cross-term interference. The simulated results have demonstrated that it is an effective method for the micro-motion target imaging.

scholarly journals Parameters Estimation for Wear-out Failure Period of Three-Parameter Weibull Distribution

2021 ◽  
Vol 11 (1) ◽  
pp. 40
Author(s):  
Takatoshi Sugiyama ◽  
Toru Ogura
Keyword(s):  
Shape Parameter ◽  
Regression Line ◽  
Minimum Variance ◽  
Small Sample ◽  
Parameters Estimation ◽  
Small Samples ◽  
Linear Estimator ◽  
Shape Parameters ◽  
Sample Sizes ◽  
Long Time

The shape parameter estimation using the minimum-variance linear estimator with hyperparameter (MVLE-H) method is believed to be effective for a wear-out failure period in a small sample. In the process of the estimation, our method uses the hyperparameter and estimate shape parameters of the MVLE-H method. To obtain the optimal hyperparameter c, it takes a long time, even in the case of the small sample. The main purpose of this paper is to remove the restriction of small samples. We observed that if we set the shape parameters, for sample size n and c, we can use the regression equation to infer the optimal c from n. So we searched in five increments and complemented the hyperparameter for the remaining sample sizes with a linear regression line. We used Monte Carlo simulations (MCSs) to determine the optimal hyperparameter for various sample sizes and shape parameters of the MVLE-H method. Intrinsically, we showed that the MVLE-H method performs well by determining the hyperparameter. Further, we showed that the location and scale parameter estimations are improved using the shape parameter estimated by the MVLE-H method. We verified the validity of the MVLE-H method using MCSs and a numerical example.

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