Martial Coulon

Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery

This paper studies a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra following the linear mixing model. The estimation of the unknown endmember spectra is conducted in a unified manner by generating the posterior distribution of abundances and endmember parameters under a hierarchical Bayesian model. This model assumes conjugate prior distributions for these parameters, accounts for nonnegativity and full-additivity constraints, and exploits the fact that the endmember proportions lie on a lower dimensional simplex. A Gibbs sampler is proposed to overcome the complexity of evaluating the resulting posterior distribution. This sampler generates samples distributed according to the posterior distribution and estimates the unknown parameters using these generated samples. The accuracy of the joint Bayesian estimator is illustrated by simulations conducted on synthetic and real AVIRIS images.

Theoretical performance for asynchronous multi-user chaos-based communication systems on fading channels

In this paper, we present a study of the bit error rate (BER) performance of chaos-based DS-CDMA system over an m-distributed fading channel. In this system, the perfect synchronization between receiver and transmitter is assumed. The transmitted bit energy after spreading by chaotic signal is not considered as constant in order to evaluate with precision the BER for such system. The performance of chaos-based DS-CDMA system is examined first in mono-user case. An approximation of the received bit energy by a known distribution leads to an accurate analytical BER expression. This BER computation approach is generalized for asynchronous multi-user case. The performance of DS-CDMA system using Gold sequence is compared and discussed to chaos-based DS-CDMA system. A perfect match between simulations and analytical BER expressions confirms the exactitude of our approach.

Resource Allocation in MIMO Radar With Multiple Targets for Non-Coherent Localization

In an MIMO radar network, the multiple transmit elements may emit waveforms that differ on power and bandwidth. In this paper, we are asking, given that these two resources are limited, what is the optimal power, optimal bandwidth, and optimal joint power and bandwidth allocation for best localization of multiple targets. The well-known Crámer-Rao lower bound for target localization accuracy is used as a figure of merit and approximate solutions are found by minimizing a sequence of convex problems. Their quality is assessed through extensive numerical simulations and with the help of a lower-bound on the true solution. Simulations results reveal that bandwidth allocation policies have a definitely stronger impact on performance than power.

Multi-user receivers for synchronous and asynchronous transmissions for chaos-based multiple-access systems

This paper addresses the problem of multi-user detection for a multiple-access system based on chaotic communications. In particular, one considers the differential chaos shift keying (DCSK) modulation. Two kinds of transmissions are considered: synchronous transmissions, where the bit epochs are aligned at the receiver, and asynchronous transmissions. Different detectors are studied for both kinds of transmissions: (i) an optimal receiver, which minimizes a least-squares criterion; this detector requires the knowledge of many user characteristics; (ii) an adaptive LMS detector, which converges to the previous detector without any a priori knowledge concerning the chaotic sequences; this detector is then better suited for practical applications; (iii) detectors based on the estimation of the chaotic sequences. Theoretical performance results are derived for all these detectors. Simulation results are reported, which confirm the theoretical analysis and allows one to compare the proposed detectors. These results show that the chaotic-sequence estimation (CSE)-based detectors are only fitted for synchronous transmissions, but give poor results for asynchronous transmissions. At the contrary, LMS-based receivers, which could also be used for standard direct-sequence spread-spectrum systems, present the best practical performance. In particular, the performances obtained for asynchronous transmissions remain close to those obtained for synchronous transmissions.

CRC-assisted error correction in a trellis coded system with bit stuffing

This paper introduces a new error correction strategy using cyclic redundancy checks (CRC) for a trellis coded system in the presence of bit stuffing. The proposed receiver is designed to simultaneously demodulate, decode and correct the received message in the presence of bit stuffing. It is based on a Viterbi algorithm exploiting the conditional transitions of an appropriate extended trellis. The receiver is evaluated with automatic identification system (AIS) messages constructed with a 16 bit CRC and a Gaussian Minimum Shift Keying (GMSK) modulation. The stuffed bits are inserted after any sequence of five consecutive bits 1 as requested by the AIS recommendation. Simulation results illustrate the algorithm performance in terms of packet error rate. A gain of more than 2.5dB is obtained when compared to the conventional GMSK receiver.

Direct Localization for Massive MIMO

Large-scale MIMO systems are well known for their advantages in communications, but they also have the potential for providing very accurate localization, thanks to their high angular resolution. A difficult problem arising indoors and outdoors is localizing users over multipath channels. Localization based on angle of arrival (AOA) generally involves a two-step procedure, where signals are first processed to obtain a users AOA at different base stations, followed by triangulation to determine the users position. In the presence of multipath, the performance of these methods is greatly degraded due to the inability to correctly detect and/or estimate the AOA of the line-of-sight (LOS) paths. To counter the limitations of this two-step procedure which is inherently suboptimal, we propose a direct localization approach in which the position of a user is localized by jointly processing the observations obtained at distributed massive MIMO base stations. Our approach is based on a novel compressed sensing framework that exploits channel properties to distinguish LOS from non-LOS signal paths, and leads to improved performance results compared to previous existing methods.

Multi-user receivers for a multiple-access system based on chaotic sequences on unknown asynchronous frequency-selective channels

This article deals with the problem of multi-user detection for a chaos-based multiple-access system, using a Differential Chaos Shift Keying (DCSK) modulation. The transmission channels are frequency-selective (multipath), and the channel characteristics (gains and delays) are unknown at the receiver side. It is only assumed that estimates of the minimum and maximum delays are available, only for the channel corresponding to the user of interest. Under such assumptions, a least-mean-square detector is derived, whose theoretical performances are provided. This detector is compared with the detector obtained when the delays are known, and with the LMMSE detector, for which all system parameters are available. The theoretical analysis is confirmed by the simulation results, which show that the LMS detector is not dramatically degraded with respect to the LMMSE detector, and that it is quite robust with respect to poor accuracy of the delay estimation.

Joint phase-recovery and demodulation-decoding of AIS signals received by satellite

This paper presents a demodulation algorithm for automatic identification system (AIS) signals received by a satellite. The main contribution of this work is to consider the phase recovery problem for an unknown modulation index, coupled with a time-varying phase shift. The proposed method is based on a demodulator introduced in a previous paper based on a Viterbi-type algorithm applied to an extended trellis. The states of this extended trellis are composed of a trellis-code state and of a cyclic redundancy check state. The bit stuffing mechanism is taken into account by defining special conditional transitions in the extended trellis. This algorithm estimates and tracks the phase shift by modifying the Euclidean distance used in the trellis. Simulation results obtained with and without phase tracking are presented and compared in the context of the AIS system.

Extended constrained Viterbi algorithm for AIS signals received by satellite

This paper addresses the problem of error correction of AIS messages by using the a priori knowledge of some information in the messages. Indeed, the AIS recommendation sets a unique value or a range of values for certain fields in the messages. Moreover, the physics can limit the range of fields, such as the speed of the vessel or its position (given the position of the receiver). The repetition of the messages gives also some information. Indeed, the evolution of the ship position is limited between messages and the ship ID is known. The constrained demodulation algorithm presented in this article is an evolution of the constrained Viterbi algorithm (C-VA). It is based on a modified Viterbi algorithm that allows the constraints to be considered in order to correct transmission errors by using some new registers in the state variables. The constraints can be either a single value or a range of values for the message fields. Simulation results illustrate the algorithm performance in terms of bit error rate and packet error rate. The performance of the proposed algorithm is 2 dB better than that obtained with the receiver without constraints.

Joint detection of variance changes using hierarchical Bayesian analysis

This paper addresses the problem of detecting variance changes in time-series coming from two different sensors. The two sequences are modeled as zero-mean white Gaussian sequences with piecewise constant variances. Bayesian inference allows to define interesting priors which reflect the correlations between the two change-point sequences. Unfortunately, the Bayesian estimators for the change-point parameters cannot be expressed in closed-form. A Metropolis-within-Gibbs algorithm allows to generate samples distributed according to the posterior distributions of the unknown parameters. The hierarchical structure of the Bayesian model is also used to estimate the unknown hyperparameters.