Training-based MIMO channel Rice factor estimation algorithms

2010 ◽  
Author(s):  
Hamid Nooralizadeh ◽  
Shahriar Shirvani Moghaddam
Keyword(s):  
Mimo Channel ◽  
Estimation Algorithms ◽  
Factor Estimation

scholarly journals A Comprehensive Review on Various Estimation Techniques for Multi Input Multi Output Channel

2019 ◽  
Vol 27 (1) ◽  
pp. 262-274 ◽  
Author(s):  
Awwab Qasim Jumaah Althahab ◽  
Sameer Abdul Kadhim Alrufaiaat
Keyword(s):  
Channel Estimation ◽  
Communication Systems ◽  
Multiple Input Multiple Output ◽  
Wireless Channel ◽  
Mimo Channel ◽  
Estimation Accuracy ◽  
Blind Estimation ◽  
Quality Of Data ◽  
Single Input Single Output ◽  
Estimation Algorithms

The problem of wireless channel estimation has been evolving due to some undesirable effects of channel physical properties on transmitted signals. At the receiver end, distortions, delays, attenuations, interferences, and phase shifts are the most issues encounter together with the received signals. In order to overcome channel effects and provide almost a perfect quality of data transmission, channel parameter estimation is needed. In Multiple Input-Multiple Output systems (MIMO), channel estimation is a more complicated step as compared with the Single Input-Single Output systems, SISO, because of the fact that the number of sub-channels that needs estimate is much greater than SISO systems. The fundamental objective of this research paper is to go over the famous and efficient algorithms that have been innovated to solve the problem of MIMO channel estimation in wireless communication systems. In this paper, these techniques have been classified into three groups: non-blind, semi-blind and blind estimation. For each group, a brief illustration is presented for familiar estimation algorithms. Finally, we compare between these techniques based on computational complexity, latency and estimation accuracy.

An Absolute Phase Estimation Method for Interferometry Imagery

2019 ◽  
Vol 1 (2) ◽  
pp. 14-19
Author(s):  
Sui Ping Lee ◽  
Yee Kit Chan ◽  
Tien Sze Lim
Keyword(s):  
Noise Suppression ◽  
Estimation Method ◽  
Phase Image ◽  
Phase Estimation ◽  
Phase Reconstruction ◽  
Estimation Algorithms ◽  
Absolute Phase ◽  
Filtering Rate ◽  
Interferometric Phase ◽  
Image Performance

Accurate interpretation of interferometric image requires an extremely challenging task based on actual phase reconstruction for incomplete noise observation. In spite of the establishment of comprehensive solutions, until now, a guaranteed means of solution method is yet to exist. The initially observed interferometric image is formed by 2π-periodic phase image that wrapped within (-π, π]. Such inverse problem is further corrupted by noise distortion and leads to the degradation of interferometric image. In order to overcome this, an effective algorithm that enables noise suppression and absolute phase reconstruction of interferometric phase image is proposed. The proposed method incorporates an improved order statistical filter that is able to adjust or vary on its filtering rate by adapting to phase noise level of relevant interferometric image. Performance of proposed method is evaluated and compared with other existing phase estimation algorithms. The comparison is based on a series of computer simulated and real interferometric data images. The experiment results illustrate the effectiveness and competency of the proposed method.

Automotive Radars

2017 ◽  
Author(s):  
Sujeet Patole ◽  
Murat Torlak ◽  
Dan Wang ◽  
Murtaza Ali
Keyword(s):  
Signal Processing ◽  
Pedestrian Detection ◽  
Radar Signal ◽  
Automotive Radar ◽  
Estimation Algorithms ◽  
Radar Systems ◽  
Starting Point ◽  
Processing Techniques ◽  
Automotive Radars ◽  
Signal Processing Techniques

Automotive radars, along with other sensors such as lidar, (which stands for “light detection and ranging”), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter- wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade-off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird’s-eye view to the existing research community.

Sign in / Sign up

Export Citation Format

Share Document