A comparison of digital transmission techniques under multichannel conditions at 2.4 GHz in the ISM BAND

In order to meet the observation quality criteria of micro-UAVs, and particularly in the context of the « Trophée Micro-Drones », ISAE/SUPAERO is studying technical solutions to transmit a high data rate from a video payload onboard a micro-UAV. The laboratory has to consider the impact of multipath and shadowing effects on the emitted signal. Therefore fading resistant transmission techniques are considered. This techniques paper have to reveal an optimum trade-off between three parameters, namely: the characteristics of the video stream, the complexity of the modulation and coding scheme, and the efficiency of the transmission, in term of BER

Analysis of non ambiguous BOC signal acquisition performance Acquisition

The Binary Offset Carrier planned for future GNSS signal, including several GALILEO Signals as well as GPS M-code, presents a high degree of spectral separation from conventional signals. It also greatly improves positioning accuracy and enhances multipath rejection. However, with such a modulation, the acquisition process is made more complex. Specific techniques must be employed in order to avoid unacceptable errors. This paper assesses the performance of three method allowing to acquire and track BOC signal unambiguously : The Bump-jumping technique, The "BPSK-like" technique and the subcarrier Phase cancellation technique

A particle filtering approach for joint detection/estimation of multipath effects on GPS measurements

Multipath propagation causes major impairments to Global Positioning System (GPS) based navigation. Multipath results in biased GPS measurements, hence inaccurate position estimates. In this work, multipath effects are considered as abrupt changes affecting the navigation system. A multiple model formulation is proposed whereby the changes are represented by a discrete valued process. The detection of the errors induced by multipath is handled by a Rao-Blackwellized particle filter (RBPF). The RBPF estimates the indicator process jointly with the navigation states and multipath biases. The interest of this approach is its ability to integrate a priori constraints about the propagation environment. The detection is improved by using information from near future GPS measurements at the particle filter (PF) sampling step. A computationally modest delayed sampling is developed, which is based on a minimal duration assumption for multipath effects. Finally, the standard PF resampling stage is modified to include an hypothesis test based decision step

Performances of a GNSS receiver for space-based applications

Space Vehicle (SV) life span depends on its station keeping capability. Station keeping is the ability of the vehicle to maintain position and orientation. Due to external perturbations, the trajectory of the SV derives from the ideal orbit. Actual positioning systems for satellites are mainly based on ground equipment, which means heavy infrastructures. Autonomous positioning and navigation systems using Global Navigation Satellite Systems (GNSS) can then represent a great reduction in platform design and operating costs. Studies have been carried out and the first operational systems, based on GPS receivers, become available. But better availability of service could be obtained considering a receiver able to process GPS and Galileo signals. Indeed Galileo system will be compatible with the current and the modernized GPS system in terms of signals representation and navigation data. The greater availability obtained with such a receiver would allow significant increase of the number of point solutions and performance enhancement. For a mid-term perspective Thales Alenia Space finances a PhD to develop the concept of a reconfigurable receiver able to deal with both the GPS system and the future Galileo system. In this context, the aim of this paper is to assess the performances of a receiver designed for Geosynchronous Earth Orbit (GEO) applications. It is shown that high improvements are obtained with a receiver designed to track both GPS and Galileo satellites. The performance assessments have been used to define the specifications of the future satellite GNSS receiver

A rao-blackwellized particle filter for INS/GPS integration

The localization performance of a navigation system can be improved by coupling different types of sensors. This paper focuses on INS-GPS integration. INS and GPS measurements allow to dene a non-linear state space model, which is appropriate to particle ltering. This model being conditionally linear Gaussian, a Rao-Blackwellization procedure can be applied to reduce the variance of the estimates

Detection of variance changes and mean value jumps in measurement noise for multipath mitigation in urban navigation

This paper studies an urban navigation filter for land vehicles. Typical urban-canyon phenomena as multipath and GPS outages seriously degrade positioning performance. To deal with these scenarios a hybrid navigation system using GPS and dead-reckoning sensors is presented. This navigation system is complemented by a two-step detection procedure that classifies outliers according to their associated source of error. Two different situations will be considered in the presence of multipath. These situations correspond to the presence or absence of line of sight for the different GPS satellites. Therefore, two kinds of errors are potentially “corrupting” the pseudo-ranges, modeled as variance changes or mean value jumps in noise measurements. An original multiple model approach is proposed to detect, identify and correct these errors and provide a final consistent solution

Towards generic satellite payloads: software radio

Satellite payloads are becoming much more complex with the evolution towards multimedia applications. Moreover satellite lifetime increases while standard and services evolve faster, necessitating a hardware platform that can evolves for not developing new systems on each change. The same problem occurs in terrestrial systems like mobile networks and a foreseen solution is the software defined radio technology. In this paper we describe a way of introducing this concept at satellite level to offer to operators the required flexibility in the system. The digital functions enabling this technology, the hardware components implementing the functions and the reconfiguration processes are detailed. We show that elements of the software radio for satellites exist and that this concept is feasible

A flexible implementation of a Global Navigation Satellite System receiver for on-board satellite navigation

In this paper, we present the implementation of the acquisition algorithm of a versatile Global Navigation Satellite System (GNSS) receiver for satellite applications. For versatility purpose, the choice of the receiver algorithms has been motivated by 1) their capability to fulfill the application requirements with a moderate complexity, 2) their capability of being factorized in a small set of elementary modules that can be configured and combined in various ways in order to process both GPS and Galileo current and future signals. These algorithms have been specified using SystemC, a modeling language that can be common to hardware and software flow. The use of a virtual platform for simulation allows us to identify bottleneck of the architecture and to propose algorithm modification to solve them

Detecting, estimating and correcting multipath biases affecting GNSS signals using a marginalized likelihood ratio-based method

International audienceIn urban canyons, non-line-of-sight (NLOS) multipath interferences affect position estimation based on global navigation satellite systems (GNSS). This paper proposes to model the effects of NLOS multipath interferences as mean value jumps contaminating the GNSS pseudo-range measurements. The marginalized likelihood ratio test (MLRT) is then investigated to detect, identify and estimate the corresponding NLOS multipath biases. However, the MLRT test statistics is difficult to compute. In this work, we consider a Monte Carlo integration technique based on bias magnitude sampling. Jensen's inequal- ity allows this Monte Carlo integration to be simplified. The multiple model algorithm is also used to update the prior information for each bias magnitude sample. Some strategies are designed for estimating and correcting the NLOS multipath biases. In order to demonstrate the performance of the MLRT, experiments allowing several localization methods to be compared are performed. Finally, results from a measurement campaign conducted in an urban canyon are presented in order to evaluate the performance of the proposed algorithm in a representative environment