Several visual feature extraction algorithms have recently appeared in the literature, with the goal of reducing the computational complexity of state-of-the-art solutions (e.g., SIFT and SURF). Therefore, it is necessary to evaluate the performance of these emerging visual descriptors in terms of processing time, repeatability and matching accuracy, and whether they can obtain competitive performance in applications such as image retrieval. This paper aims to provide an up-to-date detailed, clear, and complete evaluation of local feature detector and descriptors, focusing on the methods that were designed with complexity constraints, providing a much needed reference for researchers in this field. Our results demonstrate that recent feature extraction algorithms, e.g., BRISK and ORB, have competitive performance requiring much lower complexity and can be efficiently used in low-power devices

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I. INTRODUCTIONT HANKS to the increasing availability of inexpensive small-scale hardware devices with wireless communication capabilities, distributed sensor networks are steadily gaining popularity in a wide range of application scenarios, including security, environmental monitoring and elderly care. Acoustic source localization is a feature that is often required Manuscript

Abstract. In this manuscript, we formulate the problem of source localization based on Time Differences of Arrival (TDOAs) in the TDOA space, i.e. the Euclidean space spanned by TDOA measurements. More specifically, we show that source localization can be interpreted as a denoising problem of TDOA measurements. As this denoising problem is difficult to solve in general, our analysis shows that it is possible to resort to a relaxed version of it. The solution of the relaxed problem through linear operations in the TDOA space is then discussed, and its analysis leads to a parallelism with other stateof-the-art TDOA denoising algorithms. Additionally, we extend the proposed solution also to the case where only TDOAs between few pairs of microphones within an array have been computed. The reported denoising algorithms are all analytically justified, and numerically tested thorough simulative campaign. TDOA space and TDOA denoising and TDOA redundancy and Source localization

The curse of outlier measurements in estimation problems is a well known issue in a variety of fields. Therefore, outlier removal procedures, which enables the identification of spurious measurements within a set, have been developed for many different scenarios and applications. In this paper, we propose a statistically motivated outlier removal algorithm for time differences of arrival (TDOAs), or equivalently range differences (RD), acquired at sensor arrays. The method exploits the TDOA-space formalism and works by only knowing relative sensor positions. As the proposed method is completely independent from the application for which measurements are used, it can be reliably used to identify outliers within a set of TDOA/RD measurements in different fields (e.g. acoustic source localization, sensor synchronization, radar, remote sensing, etc.). The proposed outlier removal algorithm is validated by means of synthetic simulations and real experiments. Index TermsTDOA space, TDOA measurements, range differences, outlier removal.