Stéphane Avrillon

Pattern Reconfigurable Cubic Antenna

A new single-feed reconfigurable antenna for pattern diversity is presented in this paper. The proposed structure is based on a metallic cubic cavity which radiates through rectangular slots. The pattern reconfiguration is achieved with PIN diode switches by short-circuiting slots in their center. The designed antenna can switch between three different radiation patterns which radiate in a 4pi steradian range and can receive any incident field polarizations. A prototype of the antenna, including PIN diodes and operating in the 5 GHz band, has been built to demonstrate the feasibility of the concept. Measurements have been conducted and three-dimensional radiation patterns are provided. Diversity performances are evaluated by calculating the envelope correlation coefficient.

Enhancing Positioning Accuracy through Direct Position Estimators Based on Hybrid RSS Data Fusion

In this paper, localization based on Received Signal Strength (RSS) is investigated assuming a path loss log normal shadowing model. On the one hand, indirect RSS-based estimation schemes are investigated; these schemes are based on two steps of estimation: estimation of ranges from RSS and then estimation of position using weighted least square approximation. We show that the performances of this type of schemes depend on the used estimator in the first step. We suggest that typical median estimator must be replaced by maximum likelihood estimator (mode) to enhance the positioning accuracy. On the other hand, a new direct RSS-based estimation scheme of position is proposed; Monte Carlo simulations show that the new estimator performs better than indirect estimators and can be reliable in future hybrid localization systems.

Hybrid Data Fusion techniques for localization in UWB networks

In this paper, we exploit the concept of data fusion in UWB (Ultra Wide Band) localization systems by using different location-dependent observables. We combine ToA (Time of Arrival) and RSS (Received Signal Strength) in order to get accurate positioning algorithms.We assume that RSS observables are usually available and we study the effect of adding ToA observables on the positioning accuracy. The proposed architecture of Hybrid Data Fusion (HDF) is based on two stages: Ranging using RSS and ToA; and Estimation of position by the fusion of estimated ranges. In the first stage, we propose a new estimator of ranges from RSS observables assuming a path loss model. In the second stage, a new ML estimator is developed to merge different ranges with different variances. In order to evaluate these algorithms, simulations are carried out in a generic indoor environment and Cramer Rao Lower Bounds (CRLB) are investigated. Those algorithms show enhanced positioning results at reasonable noise levels.

Collocated Microstrip Antennas for MIMO Systems With a Low Mutual Coupling Using Mode Confinement

Collocated antennas for mutiple input multiple output (MIMO) systems are presented. The structure is composed of a dual-polarized microstrip square patch combined to a dual-polarized microstrip square ring. Consequently, four different radiation patterns are available simultaneously. With a length of about one guided wavelength, the proposed structure is well suited to reduce the MIMO terminal size. The structure operates in the 5.25 GHz band and exhibits a mutual coupling between the four input ports less than -23.5 dB in simulation and -20 dB in measurement. MIMO channel capacity measurements have been performed and demonstrate that the proposed antenna system can replace a classical system based on space diversity.

Fingerprinting localization based on neural networks and ultra-wideband signals

Fingerprinting techniques have been proved as an effective techniques for determining the position of a mobile user in an indoor environment and in challenging environments such as mines, canyons, and tunnels where common localization techniques based on time of arrival (TOA) or received signal strength (RSS) are subject to big positioning errors. In this paper, a fingerprinting based localization technique using neural networks and ultra-wideband signals (UWB) is presented as an alternative. The fingerprinting database is built with signatures extracted from channel impulse responses (CIR) obtained by processing an IR-UWB indoor propagation measurement campaign. The construction of the neural networks and the adopted approach are described. Positioning performances are evaluated with different selected signatures and different sizes of the fingerprinting database.

A New Multimode Antenna for MIMO Systems Using a Mode Frequency Convergence Concept

A new multimode antenna is proposed in this paper. The structure is based on a mode frequency convergence concept. A microstrip square patch antenna is modified in order to force different modes to resonate at the same frequency. Then, with a common/differential excitation, these modes can be fed independently. Thus, three radiation patterns are available simultaneously, thereby producing diversity. With dimensions less than a guided wavelength (≈ 0.88λg), the proposed structure is well suited to reduce the size of multiple antennas used in diversity applications such as MIMO systems. The antenna has been designed in the 2.45 GHz band for Wi-Fi applications.

A Maximum Likelihood TOA Based Estimator For Localization in Heterogeneous Networks

In this paper, we exploit the concept of data fusion in hybrid localization systems by combining different TOA (Time of Arrival) observables coming from different RATs (Radio Access Technology) and characterized by different precisions in order to enhance the positioning accuracy. A new Maximum Likelihood estimator is developed to fuse different measured ranges with different variances. In order to evaluate this estimator, Monte Carlo simulations are carried out in a generic environment and Cramer Rao Lower Bounds (CRLB) are investigated. This algorithm shows enhanced positioning accuracy at reasonable noise levels comparing to the typical Weighted Least Square estimator. The CRLB reveals that the choice of the number, and the configuration of Anchor nodes, and the type of RAT may enhance positioning accuracy.

On the bandwidth enhancement of a multipolarization and reconfigurable pattern antenna for adaptive MIMO systems

A multipolarization cubic antenna concept which introduces pattern diversity for adaptive MIMO systems has been presented. The frequency bandwidth has been improved keeping radiation characteristics constant over the whole band.

Four co-located antennas for MIMO systems with a low mutual coupling using mode confinement

Multiple input multiple output (MIMO) systems can drastically improve wireless communication capacity and robustness by exploiting multipath effects. Performances of these multiple antenna systems largely depend on the correlation between received signals. To maximize the capacity, the correlation must be as low as possible. Spatial diversity is usually used to decrease this correlation. The antenna system presented in this paper is composed of four co-located antennas which are able to produce uncorrelated signals by using both magnitude and polarization diversities. Furthermore, the structure is planar and contains a ground plane which is well-suited to be integrated in a communicant terminal. The diversity potential of the structure is quantify by calculating the envelope correlation between each radiation pattern. The coupling between each access has been decreased by using vias for confinement modes.

Overcoming singularities in TDoA Based location estimation using Total Least Square

In this paper, we propose an estimation scheme to overcome singularities which may deteriorate Time Difference of Arrival (TDoA) based localization accuracy when using Least Square (LS) estimator. The solution is based on Total Least Square (TLS) estimator. We provide an algorithm of localization which exploits the rank and the condition number of matrices in order to enhance accuracy. The Monte Carlo simulations show that the proposed algorithm outperforms the typical LS algorithm. It is also more robust to the erroneous TDoAs.