Pilot allocation based on simulated annealing for sparse channel estimation in UWB OFDM systems

In ultra wideband (UWB) orthogonal frequency-division multiplexing (OFDM) systems, compressive sensing (CS) is often employed to produce a pilot-assisted estimate of the sparse channel. The corresponding estimation performance depends to a large extent on the considered pilot allocation (PA) method, i.e., the way to select which OFDM subcarriers are best used to transmit the pilot symbols. The development of good practical PA methods has recently received a lot of attention in the scientific literature. The main challenge is to provide an attractive trade-off between the complexity of the PA method and the achieved channel estimation performance (and by extension the achieved bit error rate). In this paper, we propose a novel PA method based on simulated annealing (SA). Simulations are conducted to confirm the validity of our approach. Compared to the state-of-the-art method, the proposed PA method is shown to achieve better performance with a lower complexity

MIMO SAR Imaging for Wide-Swath Based on Compressed Sensing

To reduce the amount of data to be stored and software/hardware complexity and suppress range ambiguity, a novel MIMO SAR imaging based on compressed sensing is proposed under the condition of wide-swath imaging. Random phase orthogonal waveform (RPOW) is designed for MIMO SAR based on compressed sensing (CS). Echo model of sparse array in range and compressive sampling is reconstructed with CS theory. Resolution in range imaging is improved by using the techniques of digital beamforming (DBF) in transmit. Zero-point technique based on CS is proposed with DBF in receive and the range ambiguity is suppressed effectively. Comprehensive numerical simulation examples are performed. Its validity and practicality are validated by simulations

Échantillonnage spatiotemporel parcimonieux pour l’angiographie de localisation ultrasonore compressée du cerveau à faible coût

Au cours de la dernière décennie, la microscopie de localisation ultrasonore (ULM) a permis d’imager le système vasculaire cérébral in vivo comme jamais auparavant, avec une résolution d’environ dix microns. Cependant, avec une cadence d’imagerie pouvant atteindre 20.000 images par seconde, cette méthode nécessite l’acquisition, la transmission, le stockage et le traitement d’une grande quantité de données. Chacune de ces étapes peut devenir difficile sans les ordinateurs et échographes adaptés à cette application. Nous proposons ici une nouvelle méthode de reconstruction, baptisée Sparse-ULM, pour diminuer cette quantité de données et la complexité du matériel nécessaire, en sous-échantillonnant de manière aléatoire les canaux d’une sonde linéaire. L’évaluation des performances de la méthode ainsi que l’optimisation des paramètres ont été principalement réalisées in silico dans un fantôme anatomiquement réaliste, puis comparées aux acquisitions sur un cerveau de rat avec craniotomie. La réduction du nombre d’éléments actifs en réception détériore le rapport signal à bruit des données post reconstruction et peut conduire à de fausses détections de microbulles, diminuant le contraste des angiogrammes obtenus. Cependant, cela n’impacte que faiblement la précision de localisation des microbulles. Ces résultats montrent qu’il est possible de trouver un compromis entre le nombre de canaux et la qualité du réseau vasculaire reconstruit, et démontrent la faisabilité de réaliser la microscopie de localisation avec un nombre de canaux en réception considérablement réduit, ouvrant la voie à des dispositifs peu coûteux permettant une cartographie vasculaire à haute résolution.----------ABSTRACT Over the past decade, Ultrasound Localisation Microscopy (ULM) has made it possible to image cerebral vasculature in vivo as never before, with a resolution of about ten microns. However, with frame rate up to 20.000 frames per second, this method requires large amount of data to be acquired, transmitted, stored, and processed. Each of these steps can become challenging without computers or ultrasound scanners provided for this application. Herein, we propose a novel reconstruction framework, named Sparse-ULM for decrease this quantity of data and the complexity of the required hardware by randomly sub-sampling the channels of a linear probe. Method’s performance evaluation as well as parameters optimization were mainly performed in silico in an anatomically realistic phantom and then compared to the acquisitions on a rat brain with craniotomy. Reducing the number of active elements deteriorates the signal-to-noise ratio of post-beamforming data, and could lead to false microbubbles detections, decreasing the contrast of the angiograms obtained. However, it has little effect on localization accuracy of microbubbles. These results show that a compromise can be found between the number of channels and the quality of the reconstructed vascular network, and demonstrate feasibility of performing ULM with a drastically reduced number of channels in receive, paving the way for low-cost devices enabling high-resolution vascular mapping