Italy condemns'drone attack' on Gaza aid flotilla and deploys frigate Italy's defence minister has condemned what he said was an overnight drone attack by unidentified perpetrators on a flotilla trying to breach Israel's naval blockade of Gaza to deliver aid. Guido Crosetto also said he had ordered an Italian Navy frigate to head towards the 52 boats in the Global Sumud Flotilla (GSF), which are mostly off the coast of Crete, to assist Italian citizens on board. The GSF said several boats reported explosions and unidentified objects being dropped, as well as drones overhead and communications jamming. It accused Israel of a dangerous escalation. Israel's government has not commented.

How dangerous is the situation in the West Bank? What does survival look like inside Gaza City? The Gaza-bound Global Sumud Flotilla (GSF) says a drone has struck its main ship in the Tunisian port of Sidi Bou Said, causing a fire, but that all its passengers and crew were safe. A spokesman for the GSF blamed Israel for the incident, which occurred late on Monday, but the Tunisian National Guard said reports of a drone attack were "completely unfounded". The GSF, however, insisted the incident was a drone attack and said it would provide more details on Tuesday morning.

Multi-robot systems must have the ability to accurately estimate relative states between robots in order to perform collaborative tasks, possibly with no external aiding. Three-dimensional relative pose estimation using range measurements oftentimes suffers from a finite number of non-unique solutions, or ambiguities. This paper: 1) identifies and accurately estimates all possible ambiguities in 2D; 2) treats them as components of a Gaussian mixture model; and 3) presents a computationally-efficient estimator, in the form of a Gaussian-sum filter (GSF), to realize range-based relative pose estimation in an infrastructure-free, 3D, setup. This estimator is evaluated in simulation and experiment and is shown to avoid divergence to local minima induced by the ambiguous poses. Furthermore, the proposed GSF outperforms an extended Kalman filter, demonstrates similar performance to the computationally-demanding particle filter, and is shown to be consistent.

Estimation of the number of components (or order) of a finite mixture model is a long standing and challenging problem in statistics. We propose the Group-Sort-Fuse (GSF) procedure---a new penalized likelihood approach for simultaneous estimation of the order and mixing measure in multidimensional finite mixture models. Unlike methods which fit and compare mixtures with varying orders using criteria involving model complexity, our approach directly penalizes a continuous function of the model parameters. More specifically, given a conservative upper bound on the order, the GSF groups and sorts mixture component parameters to fuse those which are redundant. For a wide range of finite mixture models, we show that the GSF is consistent in estimating the true mixture order and achieves the $n^{-1/2}$ convergence rate for parameter estimation up to polylogarithmic factors. The GSF is implemented for several univariate and multivariate mixture models in the R package GroupSortFuse. Its finite sample performance is supported by a thorough simulation study, and its application is illustrated on two real data examples.