Relative Location Estimation Algorithm Based on Coordinates Inner Product Matrix-Based Maximum Likelihood

2013 ◽  
Author(s):  
Huan Dai ◽  
Keqing Li ◽  
Wei Xiao
Keyword(s):  
Maximum Likelihood ◽  
Estimation Algorithm ◽  
Location Estimation ◽  
Inner Product ◽  
Relative Location ◽  
Product Matrix

WALE: a weighted adaptive location estimation algorithm

2018 ◽  
Vol 10 (7) ◽  
pp. 2621-2632 ◽  
Author(s):  
Darshak Sundar ◽  
Siddharth Sendil ◽  
Vasanth Subramanian ◽  
Vidhya Balasubramanian
Keyword(s):  
Estimation Algorithm ◽  
Location Estimation

scholarly journals Control system design of duct cleaning robot capable of overcoming L and T-shaped ducts

2020 ◽  
Vol 9 (2) ◽  
pp. 123
Author(s):  
Myeong In Seo ◽  
Woo Jin Jang ◽  
Junhwan Ha ◽  
Kyongtae Park ◽  
Dong Hwan Kim
Keyword(s):  
Control Method ◽  
Estimation Algorithm ◽  
Location Estimation ◽  
Positional Error ◽  
Cleaning Robot ◽  
Communication Module ◽  
Duct Cleaning Robot ◽  
Robot Position ◽  
Uwb Communication ◽  
Duct Cleaning

This study introduces the control method of duct cleaning robot that enables real-time position tracking and self-driving over L-shaped and T-shaped duct sections. The developed robot has three legs and is designed to flexibly respond to duct sizes. The position of the robot inside the duct is identified using the UWB communication module and the location estimation algorithm. Although UWB communication has relatively large distance error within the metal, the positional error was reduced by introducing appropriate filters to estimate the robot position accurately. TCP/IP communication allows commands to be sent between the PC and the robot and to receive live images of the camera attached to the robot. Using Haar-like and classifiers, the robot can recognize the type of duct that is difficult to overcome, such as L-shaped and T-shaped duct, and it moves successfully inside the duct according to the corresponding moving algorithms.

scholarly journals BER analysis of concatenated levels of encoding in GFDM system using labview

2019 ◽  
Vol 14 (1) ◽  
pp. 77
Author(s):  
Nagarjuna Telagam ◽  
S Lakshmi ◽  
K Nehru
Keyword(s):  
Maximum Likelihood ◽  
Channel Estimation ◽  
Estimation Algorithm ◽  
Front Panel ◽  
Transmission Scheme ◽  
Multicarrier Transmission ◽  
Labview Software ◽  
Ofdm Signal ◽  
Before And After ◽  
Input Side

<p>All the devices are interconnected each other in digital form, for different applications the input data is encoded for error correcting and detecting purpose. The paper describes the transmission of QAM signals with two level encoded stages, i.e. convolutional and hamming coded GFDM system with 256-point IFFT at transmitter and FFT at the receiver using LABVIEW software. GFDM is a non-orthogonal, digital multicarrier transmission scheme which digitally implements the classical filter bank approach. GFDM transmits a block of frame composed by M time slots with K subcarriers. The higher order QAM is used because of transmitting more data but is less reliable when compared to lower order QAM. Based on GFDM specifications for the IEEE 802.11, latest 5G physical layer standards, the coding is provided by ½ rate encoder at the input side, and Maximum Likelihood decoder at the receiver side is used. The standard convolution code (7, [171, 133]), is used as encoder for the GFDM system. The GFDM complex values are displayed in the front panel, along with FFT and power spectrum is plotted for GFDM signal. The array of input bits and output bits are shown with green colour LED’s. The van de Beek algorithm is used at the receiver for maximum likelihood detection acts as convolutional decoder of GFDM signal. Next the signal is subjected to remove cyclic prefix and zero padding and applied to channel estimation algorithm. The un-equalized data and equalized data graph is shown in the front panel, before and after channel estimation VI. With BER VI available in the LABVIEW the data is normalized and its response is plotted with respect to SNR. BER values for different levels of encoders have shown in table for SNR values. This paper concludes the 32.91% improvement in BER for two levels of concatenated codes.Thus the GFDM signal outperforms the OFDM signal interms of BER for series levels of coding using labVIEW software.</p>

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