2,265 research outputs found
Dir-MUSIC Algorithm for DOA Estimation of Partial Discharge Based on Signal Strength represented by Antenna Gain Array Manifold
Inspection robots are widely used in the field of smart grid monitoring in
substations, and partial discharge (PD) is an important sign of the insulation
state of equipments. PD direction of arrival (DOA) algorithms using
conventional beamforming and time difference of arrival (TDOA) require
large-scale antenna arrays and high computational complexity, which make them
difficult to implement on inspection robots. To address this problem, a novel
directional multiple signal classification (Dir-MUSIC) algorithm for PD
direction finding based on signal strength is proposed, and a miniaturized
directional spiral antenna circular array is designed in this paper. First, the
Dir-MUSIC algorithm is derived based on the array manifold characteristics.
This method uses strength intensity information rather than the TDOA
information, which could reduce the computational difficulty and the
requirement of array size. Second, the effects of signal-to-noise ratio (SNR)
and array manifold error on the performance of the algorithm are discussed
through simulations in detail. Then according to the positioning requirements,
the antenna array and its arrangement are developed, optimized, and simulation
results suggested that the algorithm has reliable direction-finding performance
in the form of 6 elements. Finally, the effectiveness of the algorithm is
tested by using the designed spiral circular array in real scenarios. The
experimental results show that the PD direction-finding error is 3.39{\deg},
which can meet the need for Partial discharge DOA estimation using inspection
robots in substations.Comment: 8 pages,13 figures,24 reference
Model Order Selection in DoA Scenarios via Cross-Entropy based Machine Learning Techniques
In this paper, we present a machine learning approach for estimating the
number of incident wavefronts in a direction of arrival scenario. In contrast
to previous works, a multilayer neural network with a cross-entropy objective
is trained. Furthermore, we investigate an online training procedure that
allows an adaption of the neural network to imperfections of an antenna array
without explicitly calibrating the array manifold. We show via simulations that
the proposed method outperforms classical model order selection schemes based
on information criteria in terms of accuracy, especially for a small number of
snapshots and at low signal-to-noise-ratios. Also, the online training
procedure enables the neural network to adapt with only a few online training
samples, if initialized by offline training on artificial data
Power-Based Direction-of-Arrival Estimation Using a Single Multi-Mode Antenna
Phased antenna arrays are widely used for direction-of-arrival (DoA)
estimation. For low-cost applications, signal power or received signal strength
indicator (RSSI) based approaches can be an alternative. However, they usually
require multiple antennas, a single antenna that can be rotated, or switchable
antenna beams. In this paper we show how a multi-mode antenna (MMA) can be used
for power-based DoA estimation. Only a single MMA is needed and neither
rotation nor switching of antenna beams is required. We derive an estimation
scheme as well as theoretical bounds and validate them through simulations. It
is found that power-based DoA estimation with an MMA is feasible and accurate
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