Comparison of Correlation-Based OFDM Radar Receivers

Various correlation-based receivers have been proposed in passive bistatic and active monostatic radar exploiting information-bearing orthogonal frequency-division multiplexing (OFDM) transmissions, but too little has been dedicated to establishing their relations and advantages over each other. Accordingly, this paper provides an analytical comparison of the most commonly encountered filters, along with a performance analysis regarding three criteria: computational complexity, signal-to-interference-plus-noise-ratio and resilience to ground clutter. The last two especially assess the possible detrimental effects of the random sidelobes (or pedestal) induced by the data symbols in the range-Doppler map. Although simulations show that none of the filters performs unanimously better, the ones employing circular correlations globally evidence attractive results

Phase-coded Radar Waveform Design with Quantum Annealing

The Integrated Side Lobe Ratio (ISLR) problem we consider here consists in finding optimal sequences of phase shifts in order to minimize the mean squared cross-correlation side lobes of a transmitted radar signal and a mismatched replica. Currently, ISLR does not seem to be easier than the general polynomial unconstrained binary problem, which is NP-hard. In our work, we aim to take advantage of the exponential scalability of quantum computing to find new optima, by solving the ISLR problem on a quantum annealer. This quantum device is designed to solve quadratic optimization problems with binary variables (QUBO). After proposing suitable formulation for different instances of the ISLR, we discuss the performances and the scalability of our approach on the D-Wave quantum computer. More broadly, our work enlightens the limits and potential of the adiabatic quantum computation for the solving of large instances of combinatorial optimization problems.Comment: 11 pages, 4 figures, 1 table, to be published in IET Radar, Sonar and Navigatio

Synthetic Aperture Radar Image Segmentation with Quantum Annealing

In image processing, image segmentation is the process of partitioning a digital image into multiple image segment. Among state-of-the-art methods, Markov Random Fields (MRF) can be used to model dependencies between pixels, and achieve a segmentation by minimizing an associated cost function. Currently, finding the optimal set of segments for a given image modeled as a MRF appears to be NP-hard. In this paper, we aim to take advantage of the exponential scalability of quantum computing to speed up the segmentation of Synthetic Aperture Radar images. For that purpose, we propose an hybrid quantum annealing classical optimization Expectation Maximization algorithm to obtain optimal sets of segments. After proposing suitable formulations, we discuss the performances and the scalability of our approach on the D-Wave quantum computer. We also propose a short study of optimal computation parameters to enlighten the limits and potential of the adiabatic quantum computation to solve large instances of combinatorial optimization problems.Comment: 13 pages, 6 figures, to be published in IET Radar, Sonar and Navigatio

Spectral learning with proper probabilities for finite state automation

International audienceProbabilistic Finite Automaton (PFA), Probabilistic Finite State Transducers (PFST) and Hidden Markov Models (HMM) are widely used in Automatic Speech Recognition (ASR), Text-to-Speech (TTS) systems and Part Of Speech (POS) tagging for language mod-eling. Traditionally, unsupervised learning of these latent variable models is done by Expectation-Maximization (EM)-like algorithms, as the Baum-Welch algorithm. In a recent alternative line of work, learning algorithms based on spectral properties of some low order moments matrices or tensors were proposed. In comparison to EM, they are orders of magnitude faster and come with theoretical convergence guarantees. However, returned models are not ensured to compute proper distributions. They often return negative values that do not sum to one, limiting their applicability and preventing them to serve as an initialization to EM-like algorithms. In this paper, we propose a new spectral algorithm able to learn a large range of models constrained to return proper distributions. We assess its performances on synthetic problems from the PAutomaC challenge and real datasets extracted from Wikipedia. Experiments show that it outperforms previous spectral approaches as well as the Baum-Welch algorithm with random restarts, in addition to serve as an efficient initialization step to EM-like algorithms

Experimental Measurement of Time Difference Of Arrival

International audienceIn this paper is described an experimental passive localization system based on SDR (Software Defined Radio) components. This system is designed to measure Time Differences of Arrival (TDOA) of radar pulses between two platforms. For a TDOA system, time error between the two receivers must be kept very low, which requires a very accurate way to synchronize the time bases. In this purpose, a custom offline synchronization method is proposed. The overall performances of the system are analyzed. In a small scale outdoor experiment, it has been shown to perform TDOA measurements accurately. The performances measured during this experiment are then extrapolated to a more realistic electronic warfare scenario

Clairvoyant Clutter Mitigation in a Symbol-Based OFDM Radar Receiver

This paper investigates clutter rejection techniques in an OFDM symbol-based radar receiver. Two rejection filters that assume known the clutter covariance matrix are proposed. These aim at mitigating not only the clutter main peak but also its noise-like pedestal that leads to target masking issues. Performance is assessed with synthetic data on filters outputs and in terms of signal-to-clutter-plus-noise-ratio. Results show that the proposed methods succeed, to some extent, in uncovering exo-clutter targets. Rejecting clutter within the symbol-based architecture (instead of prior to) is advantageous for slowly-moving targets

Correlation-Based Radar Receivers with Pulse-Shaped OFDM Signals

In waveform sharing scenarios, various radar receivers have been developed for orthogonal frequency-division multiplexing (OFDM) signals. More general waveforms, such as pulse-shaped multicarrier modulations received little attention so far, despite their increased robustness to high-Doppler scatterers. In this paper, we compare the performance of two correlation-based radar receivers, namely the matched filter and the symbol-based technique, when used with different pulse-shaped multicarrier waveforms. We express the signal-to-interference-plus-noise-ratio in the range-Doppler map, taking into account the pedestal (or random sidelobes) induced by the symbols. Benefits of pulse shaping is further illustrated in a realistic vehicular scenario, in presence of multiple targets and ground clutter. In this context, the symbol-based approach outperforms the matched filter while enjoying a low-computational complexity. More generally, our results reveal the multicarrier pulse shape as a relevant degree of freedom in waveform co-design approaches (e.g., cognitive radar/communication systems)

Finding optimal Pulse Repetion Intervals with Many-objective Evolutionary Algorithms

In this paper we consider the problem of finding Pulse Repetition Intervals allowing the best compromises mitigating range and Doppler ambiguities in a Pulsed-Doppler radar system. We revisit a problem that was proposed to the Evolutionary Computation community as a real-world case to test Many-objective Optimization algorithms. We use it as a baseline to compare several Evolutionary Algorithms for black-box optimization with different metrics. Resulting data is aggregated to build a reference set of Pareto optimal points and is the starting point for further analysis and operational use by the radar designer.Comment: 6 pages, 4 figures, submitted to IEEE Radar 2021 conferenc

Classification par filtrage de volterra optimal pour la déflexion. Application à l'identification de données radar

Un défi actuel majeur des Radars aéroportés est la capacité à identifier automatiquement les signaux reçus, tout en ayant de fortes contraintes de temps de calcul. Une contrainte supplémentaire consiste à ne pas classer des signaux ne correspondant pas aux classes décrites dans la base de données d'apprentissage. Dans cette thèse, le problème de classification est traité en optimisant un critère de séparation entre deux classes, sous un modèle non-linéaire de filtrage des données. Cette approche est étudiée sur données synthétiques et réelles par paires de classes. L'extension à la classification de plus de deux classes est ensuite considérée. La méthode développée est validée en l'appliquant à la classification d'échos Radar et d'images SAR. Peu coûteuse en temps de calcul, elle permet de gérer le rejet de classes non apprises et d'associer plusieurs classes à des données difficiles à reconnaître.AIX-MARSEILLE3-BU Sc.St Jérô (130552102) / SudocSudocFranceF

Subspace Identification for Predictive State Representation by Nuclear Norm Minimization

International audienc