scholarly journals An Optimization Method for BAQ(Block Adaptive Quantization) Threshold Table Using Real SAR Raw Data

2017 ◽  
Vol 20 (2) ◽  
pp. 187-196
Author(s):  
Sungjae Lim ◽  
Hyonik Lee ◽  
Seyoung Kim ◽  
Changho Nam
Keyword(s):  
Optimization Method ◽  
Raw Data ◽  
Adaptive Quantization

Study on an Algorithm for SAR Raw Data Compression

2013 ◽  
Vol 380-384 ◽  
pp. 1495-1498
Author(s):  
Shang Chun Zeng ◽  
Yun Xia Xie ◽  
Yi Xian Chen ◽  
Zhao Da Zhu
Keyword(s):  
Synthetic Aperture Radar ◽  
Data Compression ◽  
Linear Prediction ◽  
Synthetic Aperture ◽  
Bit Rate ◽  
Compression Method ◽  
Raw Data ◽  
Adaptive Quantization ◽  
Difference Series ◽  
Aperture Radar

t is difficult to directly compress the raw data of synthetic aperture radar for its low relativity. In this paper, a new algorithm is put forward. Firstly range focusing is imposed to SAR raw data, which makes it have comparative high relativity, secondly a linear prediction is performed along the azimuth, lastly block adaptive quantization is used to the prediction difference series. The experiments manifest that with same bit rate, SQNR and SDNR of the algorithm proposed in this paper surpass that of BAQ algorithm. The calculation in this paper is far less than that of compression method after range focusing advised in corresponding reference. The algorithm proposed in this paper has a certain practical value.

A Compression Algorithm for SAR Data after Range Focusing

2013 ◽  
Vol 694-697 ◽  
pp. 2877-2880
Author(s):  
Shang Chun Zeng ◽  
Xian Lin Deng ◽  
Yi Xian Chen ◽  
Yun Xia Xie ◽  
Zhao Da Zhu
Keyword(s):  
Synthetic Aperture Radar ◽  
Linear Prediction ◽  
Compression Algorithm ◽  
Synthetic Aperture ◽  
Bit Rate ◽  
Compression Method ◽  
Raw Data ◽  
Adaptive Quantization ◽  
Difference Series ◽  
Sar Data

It is difficult to directly compress the raw data of synthetic aperture radar for its low relativity. In this paper, a new algorithm is put forward. Firstly range focusing is imposed to SAR raw data, which makes it have comparative high relativity, secondly a linear prediction is performed along the azimuth, lastly block adaptive quantization is used to the prediction difference series. The experiments manifest that with same bit rate, SQNR and SDNR of the algorithm proposed in this paper surpass that of BAQ algorithm. The calculation in this paper is far less than that of compression method after range focusing advised in corresponding reference.

scholarly journals An Improved BAQ Encoding and Decoding Method for Improving the Quantized SNR of SAR Raw Data

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4221
Author(s):  
Wei Ji ◽  
Xiaolan Qiu ◽  
Xuejiao Wen ◽  
Lijia Huang
Keyword(s):  
Power Loss ◽  
Signal To Noise Ratio ◽  
Practical Application ◽  
Scattering Mechanism ◽  
Echo Signal ◽  
Signal To Noise ◽  
Raw Data ◽  
Look Up Table ◽  
Adaptive Quantization ◽  
Simulation Results

When the original echo data of SAR are saturated for quantization, the performance of the commonly used block adaptive quantization (BAQ) algorithm will be degraded, which will degrade the imaging quality. This article proposes an improved Llody-Max codec method, which only needs to change the codec look-up table to get better quantization performance when the original echo is saturated. The simulation results show that the proposed method can reduce the quantization power loss, improve the echo signal-to-noise ratio (SNR), and reduce the influence of quantization saturation on the scattering mechanism of polarized SAR data, which have good practical application value.

Responsibility for Raw Data.

1962 ◽  
Vol 17 (9) ◽  
pp. 657-658 ◽  
Author(s):  
Leroy Wolins
Keyword(s):  
Raw Data

Optimization Method of Superelevation Transition for Oval Racing Tracks

CICTP 2019 ◽  
2019 ◽  
Author(s):  
Yuchen Wang ◽  
Tao Lu ◽  
Hongxing Zhao ◽  
Zhiying Bao
Keyword(s):  
Optimization Method

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Sign in / Sign up

Export Citation Format

Share Document