Interference Mitigation using a Dual-Polarized Antenna: a deep analysis in Space domain and Polarimetric domain

In this work we aim at providing a comprehensive understanding of the behaviour of a Dual-polarized (DP) array in both space and polarimetric domains. Reference paper: M. Sgammini et al., "Interference Mitigation Using a Dual-Polarized Antenna in a Real Environment, " in Proc. of the 29th Int. Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2016), Portland, OR, September 2016

State Space-Based Method for the DOA Estimation by the Forward-Backward Data Matrix Using Small Snapshots

In this presentation, a new low computational burden method for the direction of arrival (DOA) estimation from noisy signal using small snapshots is presented. The approach introduces State Space-based Method (SSM) to represent the received array signal, and uses small snapshots directly to form the Hankel data matrix. Those Hankel data matrices are then utilized to construct forward-backward data matrix that is used to estimate the state space model parameters from which the DOA of the incident signals can be extracted. In contrast to existing methods, such as MUSIC, Root-MUSIC that use the covariance data matrix to estimate the DOA and the sparse representation (SR) based DOA which is obtained by solving the sparsest representation of the snapshots, the SSM algorithm employs forward-backward data matrix formed only using small snapshots and doesn't need additional spatial smoothing method to process coherent signals. Three numerical experiments are employed to compare the performance among the SSM, Root-MUSIC and SR-based method as well as Cramér–Rao bound (CRB). The simulation results demonstrate that when a small number of snapshots, even a single one, are used, the SSM always performs better than the other two method no matter under the circumstance of uncorrelated or correlated signal. The simulation results also show that the computational burden is reduced significantly and the number of antenna elements is saved greatly

Performance Comparison Between Music And Esprit Algorithms For Direction Estimation Of Arrival Signals

This thesis examines and compares the performance of Multiple Signal Classification (MUSIC) and Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) for the estimation of Direction of Arrival (DOA) of incoming signals to the smart antenna. The comparison of these two algorithms was done on the basis of parameters like number of array elements, number of incoming signals, angle difference between the incoming signals, number of the samples taken of signal, processing time and SNR ratio. These two algorithms were implemented with MATLAB and SIMULINK for the experimental purpose. After all the experiments performed, it was analyzed that results obtained from both of the software were almost same. Comparing MUSIC\u27s results with ESPRIT, it was found that MUSIC is less prone to error than ESPRIT for almost all parametric tests. This superiority of MUSIC made it desirable to recommend it for DOA estimation in smart antenna system

A Fast DOA Estimation Algorithm Based on Polarization MUSIC

A fast DOA estimation algorithm developed from MUSIC, which also benefits from the processing of the signals' polarization information, is presented. Besides performance enhancement in precision and resolution, the proposed algorithm can be exerted on various forms of polarization sensitive arrays, without specific requirement on the array's pattern. Depending on the continuity property of the space spectrum, a huge amount of computation incurred in the calculation of 4-D space spectrum is averted. Performance and computation complexity analysis of the proposed algorithm is discussed and the simulation results are presented. Compared with conventional MUSIC, it is indicated that the proposed algorithm has considerable advantage in aspects of precision and resolution, with a low computation complexity proportional to a conventional 2-D MUSIC

Measurement and modelling of spectrum occupancy

Based on the conception of spectrum sharing, cognitive Radio as a promising technology for optimizing utilization of the radio spectrum has emerged to revolutionize the next generation wireless communications industry. In order to adopt this technology, the current spectrum allocation strategy has to be reformed and the real spectrum occupancy information has to be systemically investigated. To assess the feasibility of cognitive radio technology, the statistical information of the present spectral occupancy needs to be examined thoroughly, which forms the basis of the spectrum occupancy project. We studied the 100-2500 MHz spectrum with the traditional radio monitoring systems whose technical details have been fully recorded in this thesis. In order to detect the frequency agile signals, a channel sounder, which is capable of scanning 300 MHz spectrum within 4 ms with multiple channel inputs, was used as a dedicated radio receiver in our measurements. The conclusion of the statistical information from the spectrum monitoring experiments shows that the spectrum occupancy range from 100-2500 MHz are low indeed in the measuring locations and period. The average occupancies for most bands are less than 20%. Especially, the average occupancies in the 1 GHz to 2.5GHz spectrum are less than 5%. Time series analysis was initially introduced in spectrum occupancy analysis as a tool to model spectrum occupancy variations with time. For instance, the time series Airline model fits well the GSM band occupancy data. In this thesis, generalized linear models were used as complementarily solutions to model occupancy data into other parameters such as signal amplitude. The validation of the direction of arrival algorithms (EM and SAGE) was verified with the anechoic chamber, by which we can determine the spectrum occupancy in space domain

Joint smoothed l0-norm DOA estimation algorithm for multiple measurement vectors in MIMO radar

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. Direction-of-arrival (DOA) estimation is usually confronted with a multiple measurement vector (MMV) case. In this paper, a novel fast sparse DOA estimation algorithm, named the joint smoothed l0-norm algorithm, is proposed for multiple measurement vectors in multiple-input multiple-output (MIMO) radar. To eliminate the white or colored Gaussian noises, the new method first obtains a low-complexity high-order cumulants based data matrix. Then, the proposed algorithm designs a joint smoothed function tailored for the MMV case, based on which joint smoothed l0-norm sparse representation framework is constructed. Finally, for the MMV-based joint smoothed function, the corresponding gradient-based sparse signal reconstruction is designed, thus the DOA estimation can be achieved. The proposed method is a fast sparse representation algorithm, which can solve the MMV problem and perform well for both white and colored Gaussian noises. The proposed joint algorithm is about two orders of magnitude faster than the l1-norm minimization based methods, such as l1-SVD (singular value decomposition), RV (real-valued) l1-SVD and RV l1-SRACV (sparse representation array covariance vectors), and achieves better DOA estimation performance

Improved Ambiguity-Resolving for Virtual Baseline

A novel phase interferometer method based on virtual baseline is proposed for technical difficulty in resolving angle ambiguity and antenna layout. In this method, only two baselines are set to solve the problem of angle ambiguity. In high noise areas, there are large numbers of outliers which lead to angle error in the measured data, and a way to detect and eliminate the outliers is applied to improve the effect of solving ambiguity. The simulation results show that the improved method could effectively correct the error of fuzzy phase difference and increase the probability of ambiguity-resolving. Duo to its simple equipment and easy to implement, the proposed method might have certain guiding significance to engineering application

Signal Processing in Arrayed MIMO Systems

Multiple-Input Multiple-Output (MIMO) systems, using antenna arrays at both receiver and transmitter, have shown great potential to provide high bandwidth utilization efficiency. Unlike other reported research on MIMO systems which often assumes independent antennas, in this thesis an arrayed MIMO system framework is proposed, which provides a richer description of the channel charac- teristics and additional degrees of freedom in designing communication systems. Firstly, the spatial correlated MIMO system is studied as an array-to-array system with each array (Tx or Rx) having predefined constrained aperture. The MIMO system is completely characterized by its transmit and receive array man- ifolds and a new spatial correlation model other than Kronecker-based model is proposed. As this model is based on array manifolds, it enables the study of the effect of array geometry on the capacity of correlated MIMO channels. Secondly, to generalize the proposed arrayed MIMO model to a frequency selective fading scenario, the framework of uplink MIMO DS-CDMA (Direct- Sequence Code Division Multiple Access) systems is developed. DOD estimation is developed based on transmit beamrotation. A subspace-based joint DOA/TOA estimation scheme as well as various spatial temporal reception algorithms is also proposed. Finally, the downlink MIMO-CDMA systems in multiple-access multipath fading channels are investigated. Linear precoder and decoder optimization problems are studied under different criterions. Optimization approaches with different power allocation schemes are investigated. Sub-optimization approaches with close-form solution and thus less computation complexity are also proposed