- D.Eng. 2017, M. Eng. 2013 in Information and Communication Engineering, School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu City, Sichuan, China Karary Current interests include Signal and Information processing, Radar, and communication systems.edit
In this paper, the completion of missing measurements in a squared distances matrix through Nystrom completion algorithm has been investigated. This missing occurred due to limitation of power when the sensors are deployed in a large... more
In this paper, the completion of missing measurements in a squared distances matrix through Nystrom completion algorithm has been investigated. This missing occurred due to limitation of power when the sensors are deployed in a large area. The Nystrom algorithm has overcome the classical multidimensional scaling in a low and moderate signal to noise ratio, in addition it performs well as the number of missing entries increase. The plotted figures show admissible consequences for the proposed algorithm.
Research Interests:
Time difference of arrival (TDOA) technology has been widely used in positioning and navigation system recently. The position estimation of a source through determining time difference of arrival (TDOA) of its signal among distributed... more
Time difference of arrival (TDOA) technology has been
widely used in positioning and navigation system recently.
The position estimation of a source through determining
time difference of arrival (TDOA) of its signal among
distributed sensors has many applications in civil as well
as in the military. According to civil aspect, in mobile
communication is widely used TDOA to perform location
of cell phones of their subscribers and mobile stations
using fixed base stations and also in costal stations is used
TDOA to estimate boats and ship's location using acoustic
waves. According the military aspect, it used to locate
enemy's emitting devices such as radars, communication
devices in the battle field. In this research paper we
analyse the performance of TDOA estimation for Binary
phase shift keying (BPSK) signals, the whole scenario for
estimating time-difference-of-arrival (TDOA)
measurements was considered. The cross-correlation
among arbitrary sensors is used to estimate TDOA also by
exploiting the spectral characteristic of the received signals
by considering the maximum likelihood generalized cross
correlation (ML-GCC) the source will as unknown
position emitting BPSK signal corrupted by the white
Gaussian noise, The problem studied is time-difference of
arrival estimation in a multipath channel. The TDOA
measurement can used for solving the localization problem
typically implies cross-correlating the noisy signals
received at pairs of sensors deployed within reception
range of the source. Correlation-based localization is
severely degraded by the presence of multipath. In
Simulation results show us that the proposed method for
TDOA can achieve the Cramer-Rao lower bound (CRLB)
accuracy compared with changing the signal-to-noise-ratio
(SNR) the observation time and bandwidth (BW) of the
signal and also show the good performance
widely used in positioning and navigation system recently.
The position estimation of a source through determining
time difference of arrival (TDOA) of its signal among
distributed sensors has many applications in civil as well
as in the military. According to civil aspect, in mobile
communication is widely used TDOA to perform location
of cell phones of their subscribers and mobile stations
using fixed base stations and also in costal stations is used
TDOA to estimate boats and ship's location using acoustic
waves. According the military aspect, it used to locate
enemy's emitting devices such as radars, communication
devices in the battle field. In this research paper we
analyse the performance of TDOA estimation for Binary
phase shift keying (BPSK) signals, the whole scenario for
estimating time-difference-of-arrival (TDOA)
measurements was considered. The cross-correlation
among arbitrary sensors is used to estimate TDOA also by
exploiting the spectral characteristic of the received signals
by considering the maximum likelihood generalized cross
correlation (ML-GCC) the source will as unknown
position emitting BPSK signal corrupted by the white
Gaussian noise, The problem studied is time-difference of
arrival estimation in a multipath channel. The TDOA
measurement can used for solving the localization problem
typically implies cross-correlating the noisy signals
received at pairs of sensors deployed within reception
range of the source. Correlation-based localization is
severely degraded by the presence of multipath. In
Simulation results show us that the proposed method for
TDOA can achieve the Cramer-Rao lower bound (CRLB)
accuracy compared with changing the signal-to-noise-ratio
(SNR) the observation time and bandwidth (BW) of the
signal and also show the good performance
Research Interests:
—In this paper, we suggested new algorithms to discriminate between eight analogue modulated signals (amplitude modulation (AM), frequency modulation (FM), double side band (DSB), lower side band (LSB), upper side band (USB),... more
—In this paper, we suggested new algorithms to
discriminate between eight analogue modulated signals
(amplitude modulation (AM), frequency modulation (FM),
double side band (DSB), lower side band (LSB), upper side
band (USB), vestigial side band (VSB), combined (AM-FM)
and carrier wave (CW)). The simulation results show that
the overall recognition of the new algorithms over 97%
when the signal to noise ratio (SNR)=0dB. These new
algorithms not only achieve a better recognition rate, but
also reduce the computational loads.
discriminate between eight analogue modulated signals
(amplitude modulation (AM), frequency modulation (FM),
double side band (DSB), lower side band (LSB), upper side
band (USB), vestigial side band (VSB), combined (AM-FM)
and carrier wave (CW)). The simulation results show that
the overall recognition of the new algorithms over 97%
when the signal to noise ratio (SNR)=0dB. These new
algorithms not only achieve a better recognition rate, but
also reduce the computational loads.