In this paper, we present two Data-Aided channel estimators for Continuous Phase Modulation (CPM) in the case of transmissions over doubly-selective channels. They both capitalize on the Basis Expansion Model (BEM), widely used for OFDM systems and for Single Carrier transmission with linear modulation. However, in the case of CPM signals, we need to work on a over-sampled received signal (fractionally-spaced representation) as the equalization techniques are also working on the over-sampled received signal. The first one is a classical Least Squares (LS) estimation of the BEM parameters whereas the second channel estimator introduces first a parametric dependence on the paths delays. Indeed, in the case where those delays are known (by estimation or by geometrical consideration as for the aeronautical channel by satellite), the second LS estimation on the BEM parameters is improved and less computationally demanding. Simulations results are provided and show good performance of our parametric LS estimation.

Non-coherent trellis based receiver (TBR) is an effective method to demodulate noncoherent continuous-phase modulated sequences. However it requires to increase the observation length to reach the performance of classical coherent TBRs. Furthermore it appears that both receivers offers different behaviours when considered in a bit-interleaved coded modulation (BICM) system using iterative decoding. Indeed, trellis based outer coding schemes performing well in coherent regime generate error floors in non-coherent regime. In this paper, we show that precoding of the continuous phase modulation (CPM) encoder can deal with the latter issue. The optimization of this non-coherent precoding relies on different objectives than the existing precoding methods introduced in the coherent case. The optimization relies on some asymptotic arguments enabling an efficient BICM scheme using iterative decoding. Using the proposed precoding approach enables to remove error floors in the non-coherent regime while enabling transparent use in the coherent case.

This paper studies a statistical approach to detect and diagnose a particular type of vibration impacting the control surfaces of civil aircraft. The considered phenomenon is called Limit Cycle Oscillation (LCO). It consists of an unwanted sustained oscillation of a control surface due to the combined effect of aeroelastic phenomena and an increased level of mechanical free play in the elements that connect the control surface to the aerodynamic surface. The state-of-the-art for LCO prevention is mainly based on regular free play checks performed on ground during maintenance operations. The detection is mainly achieved by the crew, and especially the pilot who can fill in a so-called "vibration reporting sheet" to describe the phenomena felt during the flight. Thus, the pilot sensitivity to vibration is still the only reference for LCO detection. In the Flight Control System (FCS) of modern aircraft there exist already several certified algorithms for the detection of vibrations of different nature, which use dedicated local sensors to monitor the control surface behaviour. The same kind of sensors have been chosen in a local approach, which eases the isolation of the vibration sources. This paper studies a new statistical approach based on the Generalized Likelihood Ratio Test (GLRT) in order to improve the state-of-the-art for LCO detection and diagnosis. The test and its theoretical performance are derived and validated. A straightforward method compliant with real-time implementation constraint for LCO prediction is proposed. A Monte Carlo test campaign is performed in order to assess the robustness and the detection/diagnosis performance of the proposed algorithm under different operating conditions.

Precise positioning (based on Precise Point Positioning, PPP, or Real Time Kinematics, RTK) is steadily gaining momentum. The main difficulty when using carrier phase measurements remains to correctly estimate their ambiguities: not only is it a computationally intensive process, but it can be affected by cycle slips (CS), which are brutal variations in ambiguity values, due to receiver’s dynamics or unfortunate reception events. As GNSS constellations are now able to provide users with signals on three different frequencies, the concept of Triple Carrier Ambiguity Resolution has become widespread. It typically relies on the use of widelane signals, which are combinations of raw signals and are defined as to have an apparent wavelength much higher than original signals, thus making accelerating the ambiguity fixing process and reducing the frequency of cycle slips. However, CS may remain a problem for the availability of precise positioning services. The present paper therefore focuses on a cycle slip detection method, based on a hypothesis test. The main idea consists in using both code and widelane phase measurements to compute a geometry- and ionospheric-free test vector, theoretically containing only noise and possible cycle slips. The latter can be detected by looking for brutal changes on the average of the test vector. Performance is assessed on simulated and Rinex data.

In the recent times, the interest of reducing the time to retrieve the Clock and Ephemerides Data (CED) has provided an open subject of study to design the structure of the message along with the channel coding scheme of the GNSS signals. As a direct consequence, a new methodology to co-design the navigation message and the channel coding scheme structure is proposed in this paper. This new co-design enables both to reduce the time to retrieve the CED and enhanced error correction capabilities under degraded channel conditions. In order to accomplish such as requirements, codes, which provide both maximum distance separable and full diversity properties under the non-ergodic channel assumption, are designed.

Multipath is an important source of error when using global navigation satellite systems (GNSS) in urban environment, leading to biased measurements and thus to false positions. This paper treats the GNSS navigation problem as the resolution of an overdetermined system, which depends on the receiver’s position, velocity, clock bias, clock drift, and possible biases affecting GNSS measurements. We investigate a sparse estimation method combined with an extended Kalman filter to solve the navigation problem and estimate the multipath biases. The proposed sparse estimation method assumes that only a part of the satellites are affected by multipath, i.e., that the unknown bias vector is sparse in the sense that several of its components are equal to zero. The natural way of enforcing sparsity is to introduce an l1 regularization ensuring that the bias vector has zero components. This leads to a least absolute shrinkage and selection operator (LASSO) problem, which is solved using a reweighted-l1 algorithm. The weighting matrix of this algorithm is defined as functions of the carrier to noise density ratios and elevations of the different satellites. Moreover, the smooth variations of multipath biases versus time are enforced using a regularization based on total variation. For estimating the noise covariance matrix, we use an iterative reweighted least squares strategy based on the so-called Danish method. The performance of the proposed method is assessed via several simulations conducted on different real datasets.