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Journal Paper

On the accuracy limits of misspecified delay-Doppler estimation

Authors: Mc Phee Hamish Scott, Ortega Espluga Lorenzo, Vilà-Valls Jordi and Chaumette Eric

Signal Procesing, 108872, vol. 205, April, 2023.

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This work derives compact closed-form expressions of the misspecified Cramér–Rao bound and pseudo-true parameters of time-delay and Doppler for a high dynamics signal model. Those expressions are validated by analyzing the mean square error (MSE) of the misspecified maximum likelihood estimator. A noteworthy outcome of these MSE results is that, for some magnitudes of acceleration and signal-to-noise ratios, neglecting the acceleration is beneficial in the MSE sense. The variance performance improvement is obtained at the cost of a systematic error in the true parameter estimation. This can be seen as a specific case of the trade-off between bias and variance. Neglecting the acceleration can improve the Doppler estimation when the error induced on the misspecified model is less than the variance increase due to including an extra parameter to estimate. Then, for some non-zero acceleration magnitudes and short integration times, the Doppler estimation using a misspecified model outperforms a correctly specified model in the MSE sense.

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Signal and image processing / Localization and navigation and Space communication systems

Untangling first and second order statistics contributions in multipath scenarios

Authors: Lubeigt Corentin, Ortega Espluga Lorenzo, Vilà-Valls Jordi and Chaumette Eric

Signal Processing, vol. 205, pp. 108868.

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In ranging-based applications, ignoring the presence of multipath often leads to a bias upon the estimated range, which actually originates from misspecified estimation problem because the assumed data signal model, here without multipath, is not equal to the true one. Such misspecification also results in an error covariance matrix around the biased estimates, so-called pseudotrue parameters, that differs from the Cramér–Rao bound applied to the true model. This error covariance matrix can be lower bounded by a misspecified Cramér–Rao bound (MCRB). In this work, a closed-form expression of the MCRB under multipath conditions is proposed, which only depends on the baseband signal samples and both delay, Doppler and complex amplitude pseudotrue parameters. These MCRB expressions are fundamental (i) to understand and characterize the impact of multipath conditions when not taken into account, (ii) for system/signal design, and (iii) to derive new robust estimators. The proposed MCRBs are validated for a representative navigation signal, comparing the resulting bounds with the mean square error obtained by the misspecified maximum likelihood estimator with respect to the pseudotrue parameters.

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Signal and image processing / Localization and navigation

Talk

Matched, mismatched and semiparametric inference in elliptical distributions

Author: Fortunati Stefano

Seminar of TeSA, Toulouse, November 17, 2022.

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Signal and image processing / Aeronautical communication systems, Earth observation, Localization and navigation and Space communication systems

Journal Paper

Delay Optimization of Conventional Non-Coherent Differential CPM Detection

Authors: Jerbi Anouar, Amis Karine, Guilloud Frédéric and Benaddi Tarik

IEEE Communications Letters, vol. 27, issue 1, pp. 234-238, January, 2023.

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The conventional non-coherent differential detection of continuous phase modulations (CPM) is quite robust to channel impairments such as phase and Doppler shifts. Its implementation is on top of that simple. It consists in multiplying the received baseband signal by its conjugate version delayed by one symbol period. However it suffers from a signal-to-noise ratio gap compared to the optimum coherent detection. In this paper, we improve the error rate performance of the conventional differential detection by using a delay higher than one symbol period. We derive the trellis description as well as the branch and cumulative metrics that take into account a delay of K symbol periods. We then determine an optimized delay K opt based on the minimum Euclidean distance between two differential signals for some popular CPM formats. The optimized values are confirmed by error rate simulations.

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Digital communications / Aeronautical communication systems and Space communication systems

Talk

Data Driven Optical Coding Optimization in Computational Imaging

Author: Arguello Fuentes Henry

Seminar of TeSA, Toulouse, October 25, 2022.

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Signal and image processing / Aeronautical communication systems, Earth observation, Localization and navigation and Space communication systems

PhD Thesis

Apprentissage profond pour la compression embarquée d'images d'observation de la Terre.

Author: Alves de Oliveira Vinicius

Defended on October 21, 2022.

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The new generation of satellite instruments enables the acquisition of images with evergrowing spectral and spatial resolutions. The counterpart is that an increasing amount of data must be processed and transmitted to the ground. Onboard image compression becomes thus crucial to preserve transmission channel bandwidth and reduce data transmission time. Recently, convolutional neural networks have shown outstanding results for lossy image compression compared to traditional compression schemes, however, at the cost of a high computational complexity. Autoencoder architectures are trained end-to-end, taking beneĄt from extensive datasets and computing power available on mighty clusters. Consequently, the potential contributions and feasibility of deep learning techniques for onboard compression are arousing great interest. In this context, nevertheless, computational resources are subject to severe limitations: a trade-off between compression performance and complexity must be established. In this thesis, the main objective is to adapt learned compression frameworks to onboard compression, simplifying them and training them with speciĄc images. In a Ąrst step, we propose simplifying these architectures as much as possible while preserving high performance, particularly maintaining the adaptability to handle diverse input images. In a second step, we investigate how such architectures can further be improved by aggregating other functionalities such as denoising. Thus, we intend to incorporate denoising, either considering the above mentioned compression architectures for joint compression and denoising concurrently or as a sequential approach. The sequential approach consists in using, on the ground, a different architecture to denoise the images issued from the preceding learned compression framework. By running experiments on simulated but realistic satellite images, we show that the proposed simpliĄcations to the learned compression framework result in considerably lower complexity while maintaining high performance. Concerning learned compression and denoising, the joint and sequential approaches are beneĄcial and complementary, allowing to surpass the CNES imaging system performance, and thus opening the path towards operational compression and denoising pipelines for satellite images.

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Signal and image processing / Earth observation

PhD Defense Slides

Apprentissage profond pour la compression embarquée d'images d'observation de la Terre.

Author: Alves de Oliveira Vinicius

Defended on October 21, 2022.

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The new generation of satellite instruments enables the acquisition of images with evergrowing spectral and spatial resolutions. The counterpart is that an increasing amount of data must be processed and transmitted to the ground. Onboard image compression becomes thus crucial to preserve transmission channel bandwidth and reduce data transmission time. Recently, convolutional neural networks have shown outstanding results for lossy image compression compared to traditional compression schemes, however, at the cost of a high computational complexity. Autoencoder architectures are trained end-to-end, taking beneĄt from extensive datasets and computing power available on mighty clusters. Consequently, the potential contributions and feasibility of deep learning techniques for onboard compression are arousing great interest. In this context, nevertheless, computational resources are subject to severe limitations: a trade-off between compression performance and complexity must be established. In this thesis, the main objective is to adapt learned compression frameworks to onboard compression, simplifying them and training them with speciĄc images. In a Ąrst step, we propose simplifying these architectures as much as possible while preserving high performance, particularly maintaining the adaptability to handle diverse input images. In a second step, we investigate how such architectures can further be improved by aggregating other functionalities such as denoising. Thus, we intend to incorporate denoising, either considering the above mentioned compression architectures for joint compression and denoising concurrently or as a sequential approach. The sequential approach consists in using, on the ground, a different architecture to denoise the images issued from the preceding learned compression framework. By running experiments on simulated but realistic satellite images, we show that the proposed simpliĄcations to the learned compression framework result in considerably lower complexity while maintaining high performance. Concerning learned compression and denoising, the joint and sequential approaches are beneĄcial and complementary, allowing to surpass the CNES imaging system performance, and thus opening the path towards operational compression and denoising pipelines for satellite images.

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Signal and image processing / Earth observation

Conference Paper

Theoretical Evaluation of the GNSS Synchronization Performance Degradation under Interferences

Authors: Ortega Espluga Lorenzo, Vilà-Valls Jordi and Chaumette Eric

In Proc. 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022), Denver, Colorado, USA, September 19-23, 2022.

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Global Navigation Satellite Systems (GNSS) are a key player in a plethora of applications, ranging from navigation and timing, to Earth observation or space weather characterization. For navigation purposes, interference scenarios are among the most challenging operation conditions, which clearly impact the maximum likelihood estimates (MLE) of the signal synchronization parameters. While several interference mitigation techniques exist, a theoretical analysis on the GNSS MLE performance degradation under interference, being fundamental for system/receiver design, is a missing tool. The main goal of this contribution is to provide such analysis, by deriving closed-form expressions of the estimation bias, for a generic GNSS signal corrupted by an interference. The proposed bias are validated for a tone interference and a linear frequency modulation chirp interference.

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Signal and image processing / Localization and navigation and Space communication systems

Non-coherent CPM Detection under Gaussian Channel affected with Doppler Shift

Authors: Jerbi Anouar, Guilloud Frédéric, Amis Karine and Benaddi Tarik

In Proc. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Virtual, September 12-15, 2022.

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We consider the transmission of a continuous phase modulated (CPM) signal through a Gaussian channel affected by Doppler shifts. We propose a receiver robust to the Doppler shifts derived from a non-coherent detection criterion. We compare its performance to another non-coherent receiver based on a linear approximation of the CPM signal (Laurent decomposition) to which we add a Doppler compensation. Simulation results show that the first algorithm is robust to low-moderate Doppler shifts, while the second is robust to any one. We finally compare these two algorithms to delay-optimized differential detectors which do not require any Doppler shift estimation. We also provide complexity estimations to guide the possible complexity-performance trade-offs.

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Digital communications / Space communication systems

Détection Non-cohérente des Modulations CPM en Présence d’un Décalage Doppler.

Authors: Jerbi Anouar, Amis Karine, Guilloud Frédéric and Benaddi Tarik

In Proc. Groupement de Recherche en Traitement du Signal et des Images (GRETSI), Nancy, France, September 6-9, 2022.

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We consider the transmission of a continuous phase modulated (CPM) signal through a Gaussian channel affected by Doppler shifts. We focus on a receiver robust to the Doppler shift by proposing two different types of receiver derived from a non-coherent detection criterion : one based on a linear approximation of the CPM signal (Laurent decomposition) and the other based on its exact expression. Simulation results show that the first algorithm is robust to low-moderate Doppler shifts, while the second is robust to any one.

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Digital communications / Space communication systems

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