337 research outputs found
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
SPOT-GPR: a freeware tool for target detection and localizationin GPR data developed within the COST action TU1208
SPOT-GPR (release 1.0) is a new freeware tool implementing an innovative Sub-Array Processing method, for the analysis of Ground-Penetrating Radar (GPR) data with the main purposes of detecting and localizing targets. The software is implemented in Matlab, it has a graphical user interface and a short manual. This work is the outcome of a series of three Short-Term Scientific Missions (STSMs) funded by European COoperation in Science and Technology (COST) and carried out in the framework of the COST Action TU1208 âCivil Engineering Applications of Ground Penetrating Radarâ (www.GPRadar.eu). The input of the software is a GPR radargram (B-scan). The radargram is partitioned in subradargrams, composed of a few traces (A-scans) each. The multi-frequency information enclosed in each trace is exploited and a set of dominant Directions of Arrival (DoA) of the electromagnetic field is calculated for each sub-radargram. The estimated angles are triangulated, obtaining a pattern of crossings that are condensed around target locations. Such pattern is filtered, in order to remove a noisy background of unwanted crossings, and is then processed by applying a statistical procedure. Finally, the targets are detected and their positions are predicted. For DoA estimation, the MUltiple SIgnal Classification (MUSIC) algorithm is employed, in combination with the matched filter technique. To the best of our knowledge, this is the first time the matched filter technique is used for the processing of GPR data. The software has been tested on GPR synthetic radargrams, calculated by using the finite-difference timedomain simulator gprMax, with very good results
Spatial Compressive Sensing for MIMO Radar
We study compressive sensing in the spatial domain to achieve target
localization, specifically direction of arrival (DOA), using multiple-input
multiple-output (MIMO) radar. A sparse localization framework is proposed for a
MIMO array in which transmit and receive elements are placed at random. This
allows for a dramatic reduction in the number of elements needed, while still
attaining performance comparable to that of a filled (Nyquist) array. By
leveraging properties of structured random matrices, we develop a bound on the
coherence of the resulting measurement matrix, and obtain conditions under
which the measurement matrix satisfies the so-called isotropy property. The
coherence and isotropy concepts are used to establish uniform and non-uniform
recovery guarantees within the proposed spatial compressive sensing framework.
In particular, we show that non-uniform recovery is guaranteed if the product
of the number of transmit and receive elements, MN (which is also the number of
degrees of freedom), scales with K(log(G))^2, where K is the number of targets
and G is proportional to the array aperture and determines the angle
resolution. In contrast with a filled virtual MIMO array where the product MN
scales linearly with G, the logarithmic dependence on G in the proposed
framework supports the high-resolution provided by the virtual array aperture
while using a small number of MIMO radar elements. In the numerical results we
show that, in the proposed framework, compressive sensing recovery algorithms
are capable of better performance than classical methods, such as beamforming
and MUSIC.Comment: To appear in IEEE Transactions on Signal Processin
Coherent, super resolved radar beamforming using self-supervised learning
High resolution automotive radar sensors are required in order to meet the
high bar of autonomous vehicles needs and regulations. However, current radar
systems are limited in their angular resolution causing a technological gap. An
industry and academic trend to improve angular resolution by increasing the
number of physical channels, also increases system complexity, requires
sensitive calibration processes, lowers robustness to hardware malfunctions and
drives higher costs. We offer an alternative approach, named Radar signal
Reconstruction using Self Supervision (R2-S2), which significantly improves the
angular resolution of a given radar array without increasing the number of
physical channels. R2-S2 is a family of algorithms which use a Deep Neural
Network (DNN) with complex range-Doppler radar data as input and trained in a
self-supervised method using a loss function which operates in multiple data
representation spaces. Improvement of 4x in angular resolution was demonstrated
using a real-world dataset collected in urban and highway environments during
clear and rainy weather conditions.Comment: 28 pages 10 figure
Polarimetric airborne scientific instrument, mark 2, an iceâsounding airborne synthetic aperture radar for subglacial 3DÂ imagery
Polarimetric Airborne Scientific INstrument, mark 2 (PASIN2) is a 150 MHz coherent
pulsed radar with the purpose of deep ice sounding for bedrock, subglacial channels and
iceâwater interface detection in Antarctica. It is designed and operated by the British
Antarctic Survey from 2014. With multiple antennas, oriented along and acrossâtrack, for
transmission and reception, it enables polarimetric 3D estimation of the ice base with a
single pass, reducing the gridding density of the survey paths. The offâline data processing
stream consists of channel calibration; 2D synthetic aperture radar (SAR) imaging based
on backâprojection, for alongâtrack and range dimensions; and finally, a direction of
arrival estimation (DoA) of the remaining acrossâtrack angle, by modifying the nonâlinear
MUSIC algorithm. Calibration flights, during the Antarctic Summer campaigns in 16/17
and 19/20 seasons, assessed and validated the instrument and processing performances.
Imaging flights over ice streams and ice shelves close to grounding lines demonstrate the
3D sensing capabilities. By resolving directional ambiguities and accounting for reflector
acrossâtrack location, the true ice thickness and bed elevation are obtained, thereby
removing the error of the usual assumption of vertical DoA, that greatly influence the
output of flow models of ice dynamics
Polarimetric airborne scientific instrument, mark 2, an iceâsounding airborne synthetic aperture radar for subglacial 3D imagery
Polarimetric Airborne Scientific INstrument, mark 2 (PASIN2) is a 150 MHz coherent pulsed radar with the purpose of deep ice sounding for bedrock, subglacial channels and ice-water interface detection in Antarctica. It is designed and operated by the British Antarctic Survey from 2014. With multiple antennas, oriented along and across-track, for transmission and reception, it enables polarimetric 3D estimation of the ice base with a single pass, reducing the gridding density of the survey paths. The off-line data processing stream consists of channel calibration; 2D synthetic aperture radar (SAR) imaging based on back-projection, for along-track and range dimensions; and finally, a direction of arrival estimation (DoA) of the remaining across-track angle, by modifying the non-linear MUSIC algorithm. Calibration flights, during the Antarctic Summer campaigns in 16/17 and 19/20 seasons, assessed and validated the instrument and processing performances. Imaging flights over ice streams and ice shelves close to grounding lines demonstrate the 3D sensing capabilities. By resolving directional ambiguities and accounting for reflector across-track location, the true ice thickness and bed elevation are obtained, thereby removing the error of the usual assumption of vertical DoA, that greatly influence the output of flow models of ice dynamics
Multi-source parameter estimation and tracking using antenna arrays
This thesis is concerned with multi-source parameter estimation and tracking using antenna arrays in wireless communications. Various multi-source parameter estimation and tracking algorithms are presented and evaluated.
Firstly, a novel multiple-input multiple-output (MIMO) communication system is proposed for multi-parameter channel estimation. A manifold extender is presented for increasing the degrees of freedom (DoF). The proposed approach utilises the extended manifold vectors together with superresolution subspace type algorithms, to achieve the estimation of delay, direction of departure (DOD) and direction of arrival (DOA) of all the paths of the desired user in the presence of multiple access interference (MAI).
Secondly, the MIMO system is extended to a virtual-spatiotemporal system by incorporating the temporal domain of the system towards the objective of further increasing the degrees of freedom. In this system, a multi-parameter es-
timation of delay, Doppler frequency, DOD and DOA of the desired user, and a beamformer that suppresses the MAI are presented, by utilising the proposed virtual-spatiotemporal manifold extender and the superresolution subspace type
algorithms.
Finally, for multi-source tracking, two tracking approaches are proposed based on an arrayed Extended Kalman Filter (arrayed-EKF) and an arrayed Unscented Kalman Filter (arrayed-UKF) using two type of antenna arrays: rigid array and
flexible array. If the array is rigid, the proposed approaches employ a spatiotemporal state-space model and a manifold extender to track the source parameters, while if it is flexible the array locations are also tracked simultaneously.
Throughout the thesis, computer simulation studies are presented to investigate and evaluate the performance of all the proposed algorithms.Open Acces
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