39,500 research outputs found
Performance Analysis for Direction of Arrival Estimating Algorithms
Smart antennas have emerged as one of the most promising directions in supporting maximum communication link throughput. In this paper, we have investigated the impact of smart antennas on a complex mobile network such as a railroad wireless communications system. The objective is to analyze the selection of a Direction-Of-Arrival (DOA) estimation algorithm which provides the maximum efficiency when deployed in our railroad testbeds for wireless vehicular communication. Our findings are discussed to provide an indepth understanding of how different algorithms should be selected to support efficient network operations
Secret Key Generation Based on AoA Estimation for Low SNR Conditions
In the context of physical layer security, a physical layer characteristic is
used as a common source of randomness to generate the secret key. Therefore an
accurate estimation of this characteristic is the core for reliable secret key
generation. Estimation of almost all the existing physical layer characteristic
suffer dramatically at low signal to noise (SNR) levels. In this paper, we
propose a novel secret key generation algorithm that is based on the estimated
angle of arrival (AoA) between the two legitimate nodes. Our algorithm has an
outstanding performance at very low SNR levels. Our algorithm can exploit
either the Azimuth AoA to generate the secret key or both the Azimuth and
Elevation angles to generate the secret key. Exploiting a second common source
of randomness adds an extra degree of freedom to the performance of our
algorithm. We compare the performance of our algorithm to the algorithm that
uses the most commonly used characteristics of the physical layer which are
channel amplitude and phase. We show that our algorithm has a very low bit
mismatch rate (BMR) at very low SNR when both channel amplitude and phase based
algorithm fail to achieve an acceptable BMR
Approximate maximum likelihood estimation of two closely spaced sources
The performance of the majority of high resolution algorithms designed for either spectral analysis or Direction-of-Arrival (DoA) estimation drastically degrade when the amplitude sources are highly correlated or when the number of available snapshots is very small and possibly less than the number of sources. Under such circumstances, only Maximum Likelihood (ML) or ML-based techniques can still be effective. The main drawback of such optimal solutions lies in their high computational load. In this paper we propose a computationally efficient approximate ML estimator, in the case of two closely spaced signals, that can be used even in the single snapshot case. Our approach relies on Taylor series expansion of the projection onto the signal subspace and can be implemented through 1-D Fourier transforms. Its effectiveness is illustrated in complicated scenarios with very low sample support and possibly correlated sources, where it is shown to outperform conventional estimators
Semi-Supervised Sound Source Localization Based on Manifold Regularization
Conventional speaker localization algorithms, based merely on the received
microphone signals, are often sensitive to adverse conditions, such as: high
reverberation or low signal to noise ratio (SNR). In some scenarios, e.g. in
meeting rooms or cars, it can be assumed that the source position is confined
to a predefined area, and the acoustic parameters of the environment are
approximately fixed. Such scenarios give rise to the assumption that the
acoustic samples from the region of interest have a distinct geometrical
structure. In this paper, we show that the high dimensional acoustic samples
indeed lie on a low dimensional manifold and can be embedded into a low
dimensional space. Motivated by this result, we propose a semi-supervised
source localization algorithm which recovers the inverse mapping between the
acoustic samples and their corresponding locations. The idea is to use an
optimization framework based on manifold regularization, that involves
smoothness constraints of possible solutions with respect to the manifold. The
proposed algorithm, termed Manifold Regularization for Localization (MRL), is
implemented in an adaptive manner. The initialization is conducted with only
few labelled samples attached with their respective source locations, and then
the system is gradually adapted as new unlabelled samples (with unknown source
locations) are received. Experimental results show superior localization
performance when compared with a recently presented algorithm based on a
manifold learning approach and with the generalized cross-correlation (GCC)
algorithm as a baseline
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