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

Crossed-dipole arrays for asynchronous DS-CDMA systems

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Multi-Step Knowledge-Aided Iterative ESPRIT for Direction Finding

In this work, we propose a subspace-based algorithm for DOA estimation which iteratively reduces the disturbance factors of the estimated data covariance matrix and incorporates prior knowledge which is gradually obtained on line. An analysis of the MSE of the reshaped data covariance matrix is carried out along with comparisons between computational complexities of the proposed and existing algorithms. Simulations focusing on closely-spaced sources, where they are uncorrelated and correlated, illustrate the improvements achieved.Comment: 7 figures. arXiv admin note: text overlap with arXiv:1703.1052

Multipath and interference errors reduction in gps using antenna arrays

The Global Positioning System (GPS) is a worldwide satellite based positioning system that provides any user with tridimensional position, speed and time information. The measured pseudorange is affected by the multipath propagation, which probably is the major source of errors for high precision systems. After a presentation of the GPS and the basic techniques employed to perform pseudorange measurements, the influence of the multipath components on the pseudorange measurement is explained. Like every system the GPS is also exposed to the errors that can be caused by the interferences, and a lot of civil applications need robust receivers to interferences for reasons of safety. In this paper some signal array processing techniques for reducing the code measurement errors due to the multipath propagation and the interferences are presented. Firstly, a non-adaptive beamforming is used. Secondly, a variant of the MUSIC and the maximum likelihood estimator can be used to estimate the DOA of the reflections and the interferences, and then a weight vector that removes these signals is calculated. In the third place, a beamforming with temporal reference is presented; the reference is not the GPS signal itself, but the output of a matched filter to the code. An interesting feature of the proposed techniques is that they can be applied to an array of arbitrary geometry.Peer ReviewedPostprint (published version

AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information

With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to an increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches