Search results for "Multilateration"
showing 5 items of 5 documents
Do Randomized Algorithms Improve the Efficiency of Minimal Learning Machine?
2020
Minimal Learning Machine (MLM) is a recently popularized supervised learning method, which is composed of distance-regression and multilateration steps. The computational complexity of MLM is dominated by the solution of an ordinary least-squares problem. Several different solvers can be applied to the resulting linear problem. In this paper, a thorough comparison of possible and recently proposed, especially randomized, algorithms is carried out for this problem with a representative set of regression datasets. In addition, we compare MLM with shallow and deep feedforward neural network models and study the effects of the number of observations and the number of features with a special dat…
Indoor localization using time difference of arrival with UWB signals and unsynchronized devices
2020
Abstract Indoor location using radio frequency (RF) signals has been traditionally focused mainly on RSSI and fingerprint techniques, because methods based on time of arrival (ToA) or time difference of arrival (TDoA) were not suitable for measuring short distances. However, the release of the IEEE 802.15.4a standard, the emergence of commercial ultra wide band (UWB) transceivers that are compliant to this norm and the integration of high frequency oscillators have permitted the design of more accurate indoor localization systems using the RF signal transmission time. In this paper, we propose an indoor localization system based on TDoA for UWB. The method implements an only one way transmi…
ETP/GDOP Behavior Study for N-Sensors Arrays ina Multilateration Radar System
2009
In this paper, we evaluated the ETP (Expected Theoretical Precision) and GDOP (Geometric Dilution Of Precision) enhancement related to the number of sensors in a Multilateration radar system. An introduction about the principles of the Multilateration radar system basis operation is described, then, the formulation for evaluation the ETP/GDOP of the 3D positioning is shown. We observed that the ETP and GDOP enhance with the increase of the number of sensors. A substantial improvement was obtained until nine sensors but, for more sensors that improvement is reduced. Results for a 75km×75km area are shown, including LAM (Local Area Multilateration) and WAM (Wide Area Multilateration) settings…
Time Difference of Arrival Estimation from Frequency-Sliding Generalized Cross-Correlations Using Convolutional Neural Networks
2020
The interest in deep learning methods for solving traditional signal processing tasks has been steadily growing in the last years. Time delay estimation (TDE) in adverse scenarios is a challenging problem, where classical approaches based on generalized cross-correlations (GCCs) have been widely used for decades. Recently, the frequency-sliding GCC (FS-GCC) was proposed as a novel technique for TDE based on a sub-band analysis of the cross-power spectrum phase, providing a structured two-dimensional representation of the time delay information contained across different frequency bands. Inspired by deep-learning-based image denoising solutions, we propose in this paper the use of convolutio…
Frequency-Sliding Generalized Cross-Correlation: A Sub-Band Time Delay Estimation Approach
2020
The generalized cross correlation (GCC) is regarded as the most popular approach for estimating the time difference of arrival (TDOA) between the signals received at two sensors. Time delay estimates are obtained by maximizing the GCC output, where the direct-path delay is usually observed as a prominent peak. Moreover, GCCs play also an important role in steered response power (SRP) localization algorithms, where the SRP functional can be written as an accumulation of the GCCs computed from multiple sensor pairs. Unfortunately, the accuracy of TDOA estimates is affected by multiple factors, including noise, reverberation and signal bandwidth. In this paper, a sub-band approach for time del…