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Conference Paper
Estimation OF Instrument Spectral Response Functions using Sparse Representations in a Dictionary
In Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Athens, Greece, July 7-12, 2024, 3rd price of the Best Student Paper Award.
Understanding greenhouse gas fluxes at the Earth’s surface is becoming crucial in the context of climate change. The aim of the CNES/UKSA MicroCarb mission is therefore to map, on a planetary scale, the sources and sinks of carbon, the main greenhouse gas in the atmosphere. To do this, a spectrometer will be sent in space to acquire spectra in 4 narrow bands around wavelengths associated with O2 and CO2. However, measurement errors can occur due to the instrument used, and induce errors in the resulting trace gas concentrations. It is therefore crucial to estimate the spectral response of the instrument as accurately as possible. This paper investigates a new estimation method for this spectral response that uses a sparse representation in a dictionary of appropriate basis functions. This sparse representation is performed using the LASSO and Orthogonal Matching Pursuit (OMP) algorithms. Simulations conducted on data mimicking observations resulting from the MicroCarb instrument allow the performance of this method to be appreciated.
Signal and image processing / Earth observation
Journal Paper
Robust error-state Kalman-type filters for attitude estimation
EURASIP Journal on Advances in Signal Processing, vol. 2024, art. 75, July, 2024.
State estimation techniques appear in a plethora of engineering fields, in particular for the attitude estimation application of interest in this contribution. A number of filters have been devised for this problem, in particular Kalman-type ones, but in their standard form they are known to be fragile against outliers. In this work, we focus on error-state filters, designed for states living on a manifold, here unit-norm quaternions. We propose extensions based on robust statistics, leading to two robust M-type filters able to tackle outliers either in the measurements, in the system dynamics or in both cases. The performance and robustness of these filters is explored in a numerical experiment. We first assess the outlier ratio that they manage to mitigate, and second the type of dynamics outliers that they can detect, showing that the filter performance depends on the measurements’ properties.
Signal and image processing / Localization and navigation
Conference Paper
On Time-Delay Estimation Accuracy Limit Under Phase Uncertainty
In Proc. 27th International Conference on Information Fusion, Venise, Italia, July 7-11, 2024.
Accurately determining signal time-delay is crucial across various domains, such as localization and communication ystems. Understanding the achievable optimal estimation peformance of such technologies, especially during design phases, is essential for benchmarking purposes. One common approach is to derive bounds like the Cramer-Rao Bound (CRB), which directly reflects the minimum achievable estimation error for unbiased estimators. Different studies vary in their approach to deal with the degree of misalignment in the global phase originating from both the transmitter and the receiver in a single input, single output (SISO) link during time-delay estimation assessment. While some treat this phase term as unknown, others assume ideal calibration and compensation. As an alternative to these two opposing approaches, this study adopts a more balanced approach by considering that such a phase can be estimated with a defined uncertainty, a measure that could be mplemented in many practical applications. The primary contribution provided lies in the derivation of a closed-form CRB expression for this alternative signal model, which, as observed, exhibits an asymptotic behavior transitioning between the results observed in previous studies, influenced by the uncertainty assumed for the mentioned phase term.
Signal and image processing / Localization and navigation
A Statistical Method for Near Real-Time Deforestation Monitoring using Time Series of Sentinel-1 Images
In Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Athens, Greece, July 7-12, 2024, Best Student Paper Award (1rst price).
In this paper, we propose an unsupervised statistical approach for near real-time monitoring of forest loss, leveraging Bayesian inference. We address the identification of forest loss as a change-point detection problem within non-filtered Sentinel-1 single polarization time series data. Each new observation contributes to the probability of deforestation occurrence, utilizing prior knowledge and a data model. Our method offers the advantage of detecting small-scale deforestation without resorting to spatial filtering techniques, thus preserving the native spatial resolution of the Sentinel-1 measurements. To assess its effectiveness, we conducted comparative evaluations against existing operational deforestation monitoring systems. The validation campaign revealed that our method exhibits enhanced detection performance with low false alarm rates with respect to existing systems across diverse landscapes, including dense forest regions such as the Brazilian Amazon, as well as seasonality-dependent areas like the Cerrado, which is strongly under-monitored by existing technology. This robustness stems from the sequential adaptive process inherent in our approach, which enables effective monitoring even in the presence of backscatter variations.
Signal and image processing / Earth observation
An Intrinsic Modified Cramér-Rao Bound on Lie Groups
In Proc. 2024 27th International Conference on Information Fusion (FUSION), Venice, Italy, July 7-11, 2024.
The Modified Cramér-Rao Bound (MCRB) proves to be of significant importance in non-standard estimation scenarios, when in addition to unknown deterministic parameters to be estimated, observations also depend on random nuisance parameters. Given the interest of applications that involve estimation on Lie Groups (LGs), as well as the relevance of nonstandard estimation problems in many practical scenarios, the main concern in this communication is to derive an intrinsic MCRB on LGs (LG-MCRB). For this purpose, a modified unbiasedness constraint must be defined, yielding a modified Barankin Bound. A closed-form formula of the LG-MCRB is then provided for a LG Gaussian model on $S O(2)$, representing $2 D$ rotation matrices, while considering non-Gaussian random nuisance parameters. The validity of this expression is then assessed through numerical simulations, and compared with the intrinsic CRB on LGs for a simplified illustrative scenario, involving a concentrated Gaussian prior distribution on the random nuisance parameters.
Signal and image processing / Localization and navigation
Talk
Robust Multi Sensor Fusion for State Estimation
Seminar of TeSA, Toulouse, July 5, 2024.
Signal and image processing / Localization and navigation
Conference Paper
Novel Bayesian Approach Based on Infinite State Markov Chains for Prompt Detection of Forest Loss Using Sentinel-1 Time Series
In Proc. ESA Dragon Symposium, Lisbon, Portugal, June 24-28, 2024, Best poster (Ecosystems Track).
Forest loss is a global issue that requires real-time surveillance to prevent further vegetation loss. This study presents an unsupervised SAR-based technique that leverages Bayesian inference and infinite state Markov chains to identify forest loss, overcoming the limitations of current methods. Our approach significantly improves accuracy and reduces false alarm rates compared to existing Near Real-Time (NRT) forest loss monitoring systems and enlarges the conditions of operability.
Signal and image processing / Earth observation
Exploiting Redundant Measurements for Time Scale Generation in a Swarm of Nanosatellites
In Proc. European Frequency and Time Forum (EFTF), Neufchâtel, Switzerland, June 25-27, 2024.
The computation of a common reference time for a swarm of nanosatellites is restricted by the quality and availability of the timing measurements made with inter-satellite links. The presence of anomalies or absence of communication links is demonstrated to harm the stability of the time scale. The Least Squares (LS) estimator is introduced as a method of preprocessing measurement noise by using all available clock comparisons in the swarm. This estimator also provides filtered measurements when inter-satellite links are missing as long as each satellite maintains at least one link with another. Anomaly detection and removing corrupted satellite links are shown to be compatible with the LS estimator to mitigate the impact of anomalous measurements. When a satellite becomes completely isolated for some period of time, a correction at the beginning and the end of the isolation period are both detailed. The correction is simple and just requires resetting the weights of missing clocks and clocks being reintroduced. Continuity is shown to be maintained when a large portion of clocks are removed and later reintroduced at the same time.
Signal and image processing / Localization and navigation
Talk
High Precision Satellite-based Navigation
Seminar of TeSA, Toulouse, June 14, 2024.
Signal and image processing / Localization and navigation
Conference Paper
Model-Based Fuzz Testing for GNSS Receiver
In Proc. Conference Approches Formelles dans l'Assistance au Développement Logiciel (AFADL), Strasbourg, France, June 3-6, 2024.
GNSS receivers are vital for aircraft navigation system reliability and safety. However, traditional test methods recommended by norms have limitations in verifying their expected properties. This article presents a thesis aimed at enhancing the testing process by integrating Model-Based Testing (MBT) and Fuzz Testing approaches. Our approach involves leveraging formal models, including behavioral models (e.g., Finite State Machines, Sequence diagrams) and static ones (e.g., OCL, BNF-based grammar descriptions), to generate relevant test cases through a dedicated mutation process. We intend to validate our approach by developing a dedicated framework for GNSS receiver verification, focusing on the critical RAIM (Receiver Autonomous Integrity Monitoring) function.
Digital communications / Localization and navigation
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