Map-Aware Models for Indoor Wireless Localization Systems: An Experimental Study

The accuracy of indoor wireless localization systems can be substantially enhanced by map-awareness, i.e., by the knowledge of the map of the environment in which localization signals are acquired. In fact, this knowledge can be exploited to cancel out, at least to some extent, the signal degradation due to propagation through physical obstructions, i.e., to the so called non-line-of-sight bias. This result can be achieved by developing novel localization techniques that rely on proper map-aware statistical modelling of the measurements they process. In this manuscript a unified statistical model for the measurements acquired in map-aware localization systems based on time-of-arrival and received signal strength techniques is developed and its experimental validation is illustrated. Finally, the accuracy of the proposed map-aware model is assessed and compared with that offered by its map-unaware counterparts. Our numerical results show that, when the quality of acquired measurements is poor, map-aware modelling can enhance localization accuracy by up to 110% in certain scenarios.Comment: 13 pages, 11 figures, 1 table. IEEE Transactions on Wireless Communications, 201

Statistical Characterization and Mitigation of NLOS Errors in UWB Localization Systems

In this paper some new experimental results about the statistical characterization of the non-line-of-sight (NLOS) bias affecting time-of-arrival (TOA) estimation in ultrawideband (UWB) wireless localization systems are illustrated. Then, these results are exploited to assess the performance of various maximum-likelihood (ML) based algorithms for joint TOA localization and NLOS bias mitigation. Our numerical results evidence that the accuracy of all the considered algorithms is appreciably influenced by the LOS/NLOS conditions of the propagation environment

Reduced-Complexity Algorithms for Indoor Map-Aware Localization Systems

The knowledge of environmental maps (i.e., map-awareness) can appreciably improve the accuracy of optimal methods for position estimation in indoor scenarios. This improvement, however, is achieved at the price of a significant complexity increase with respect to the case of map-unawareness, specially for large maps. This is mainly due to the fact that optimal map-aware estimation algorithms require integrating highly nonlinear functions or solving nonlinear and nonconvex constrained optimization problems. In this paper, various techniques for reducing the complexity of such estimators are developed. In particular, two novel strategies for restricting the search domain of map-aware position estimators are developed and the exploitation of state-of-the-art numerical integration and optimization methods is investigated; this leads to the development of a new family of suboptimal map-aware localization algorithms. Our numerical and experimental results evidence that the accuracy of these algorithms is very close to that offered by their optimal counterparts, despite their significantly lower computational complexity

Identification of Soft Tissue-Mimicking Materials and Application in the Characterization of Sensors for Lung Sounds

Early diagnosis of pulmonary implications is fundamental for the treatment of several diseases, such as idiopathic pulmonary fibrosis, rheumatoid arthritis, connective tissue diseases and interstitial pneumonia secondary to COVID-19 among the many. Recent studies prove that a wide class of pulmonary diseases can be early detected by auscultation and suitably developed algorithms for the analysis of lung sounds. Indeed, the technical characteristics of sensors have an impact on the quality of the acquired lung sounds. The availability of a fair and quantitative approach to sensors’ comparison is a prerequisite for the development of new diagnostic tools. In this work the problem of a fair comparison between sensors for lung sounds is decoupled into two steps. The first part of this study is devoted to the identification of a synthetic material capable of mimicking the acoustic behavior of human soft tissues; this material is then adopted as a reference. In the second part, the standard skin is exploited to quantitatively compare several types of sensors in terms of noise floor and sensitivity. The proposed methodology leads to reproducible results and allows to consider sensors of different nature, e.g. laryngophone, electret microphone, digital MEMS microphone, mechanical phonendoscope and electronic phonendoscope. Finally, the experimental results are interpreted under the new perspective of equivalent sensitivity and some important guidelines for the design of new sensors are provided. These guidelines could represent the starting point for improving the devices for acquisition of lung sounds

"Velcro-type" crackles predict specific radiologic features of fibrotic interstitial lung disease

Background: "Velcro-type" crackles on chest auscultation are considered a typical acoustic finding of Fibrotic Interstitial Lung Disease (FILD), however whether they may have a role in the early detection of these disorders has been unknown. This study investigated how "Velcro-type" crackles correlate with the presence of distinct patterns of FILD and individual radiologic features of pulmonary fibrosis on High Resolution Computed Tomography (HRCT). Methods: Lung sounds were digitally recorded from subjects immediately prior to undergoing clinically indicated chest HRCT. Audio files were independently assessed by two chest physicians and both full volume and single HRCT sections corresponding to the recording sites were extracted. The relationships between audible "Velcro-type" crackles and radiologic HRCT patterns and individual features of pulmonary fibrosis were investigated using multivariate regression models. Results: 148 subjects were enrolled: bilateral "Velcro-type" crackles predicted the presence of FILD at HRCT (OR 13.46, 95% CI 5.85-30.96, p < 0.001) and most strongly the Usual Interstitial Pneumonia (UIP) pattern (OR 19.8, 95% CI 5.28-74.25, p < 0.001). Extent of isolated reticulation (OR 2.04, 95% CI 1.62-2.57, p < 0.001), honeycombing (OR 1.88, 95% CI 1.24-2.83, < 0.01), ground glass opacities (OR 1.74, 95% CI 1.29-2.32, p < 0.001) and traction bronchiectasis (OR 1.55, 95% CI 1.03-2.32, p < 0.05) were all independently associated with the presence of "Velcro-type" crackles. Conclusions: "Velcro-type" crackles predict the presence of FILD and directly correlate with the extent of distinct radiologic features of pulmonary fibrosis. Such evidence provides grounds for further investigation of lung sounds as an early identification tool in FILD