The resilience of indoor localization systems is a main concern of their industrial application. A combination of different techniques can enhance the overall robustness of such systems. In this work, we present fusion possibilities of coarse Bluetooth Low Energy localization based on the received signal strength indicator and the finer ultrasound time difference of arrival (TDOA) technique. This approach offers the advantage to robustify the high-accuracy ultrasonic localization in areas with non-optimal coverage. Moreover, the data fusion enables to enhance the overall localization area in a cost effective manner. This contribution proposes and evaluates (i) novel methods of how the ultrasonic system can be extended to a constrained area and (ii) a novel possibility to incorporate available Bluetooth signal strength information in the TDOA algorithm to improve accuracy.

In this article, we propose a new robust, stable, accurate, and efficient time-delay estimation scheme for the angle-of-arrival (AoA) ultrasonic indoor single-source localization systems. The AoA technique employing microphone arrays and ultrasonic signal emitting sources has turned out to be a promising alternative to the conventional distance-based ultrawideband systems for indoor localization. The crux of AoA estimation lies in the derivation of time-delay estimates, which is typically handled by advanced digital signal processing approaches based on cross-correlation and matching algorithms. The calculation of the channel response for each received signal and the conversion from the time to the time-delay domain consent our echo suppression delay estimator (ESDE) to apply echo suppression, in order to overcome the typical challenges introduced by the channel in indoor environments. Moreover, the optimization of the cross-correlation algorithm through oversampling the cross power spectrum and the curve-fitting algorithm allows to ensure subsample precision results. The performances of our developed AoA estimation system are evaluated by carrying out real-world experiments and compared with the state-of-the-art generalized cross correlators. Our ESDE algorithm is shown to outperform several generalized solutions.

Angle-of-Arrival (AoA), as well as Angle-of-Departure (AoD) localization are general principles for performing localization based on angular measurements. In recent years, these principles have been gaining interest over more traditional techniques in the field of indoor localization and positioning. In contrast to Time-of-Arrival (ToA) and Time Difference of Arrival (TDoA)-based localization, AoA may do so without requiring a high number of anchor nodes or effective synchronization between them, with the trade-off of higher hardware and algorithmic complexity. Research on angular-based localization encompasses a wide range of technologies, each with its own advantages and disadvantages for dealing with the generally more complex propagation channels of indoor environments, as opposed to those in outdoor settings. In this contribution, we perform a systematic survey and meta analysis of the field of angularbased indoor localization systems, covering the technologies: Acoustic Positioning, Wi-Fi, Bluetooth, Ultra-Wideband (UWB), Light-based Positioning, Positioning in Cellular Networks (like 5G), mmWave, Terahertz-band Localization, Radio Frequency Identification (RFID), and RADAR. The research and literature on algorithms are examined as well. Furthermore, a new set of design considerations for angular-based localization systems is proposed, as well as several trade-offs between technologies, are discussed, and future research directions presented. Since angular-based localization is expected to play a key role in the future of indoor localization, this contribution provides a structure for researchers, as well as practitioners, to find a unified perspective on the topic.

The demand for a replacement of global navigation satellite systems in indoor environments has led researchers to investigate into the field of indoor localization. In this contribution, we propose RAILS, a three-dimensional Real-Time Angle of Arrival Ultrasonic Indoor Localization System. The proposed system is capable of accurately and precisely locating a source in three-dimensions using angle of arrival measurements. Our acoustic angle of arrival technique relies on a constellation of spatially distributed arrays of microphones and on a source emitting inaudible ultrasonic chirp signals. Real-time features are enabled by introducing a localization chirp detector constantly listening to the medium and triggering the localization algorithms only when signals of interest are approaching. The core of the three-dimensional (3-D) localization method are the time delay estimates, aiming to compute the 3-D direction vectors. In order to overcome the challenges introduced by the acoustic indoor channel and to provide accuracy and robustness in the delay estimates, we have designed a time delay estimator. Furthermore, an ad-hoc 3-D positioning algorithm based on 3-D vectors intersection has been developed to furnish reliable 3-D source positions. The performance of our proposed ultrasonic indoor localization system has been evaluated through static single-source real-world experiments in a warehouse-like scenario, covering an area of 40 m ² with a maximum source-receiver range of 10 m. The system achieved an outstanding average of 10 cm 3-D positioning accuracy, with an average standard deviation of 1 cm over all the measurements. Furthermore, dynamic single-source experiments have been carried out demonstrating that our system is capable not only of locating a source, but also to track it.

Automated and autonomous industrial inspection is a longstanding research field, driven by the necessity to enhance safety and efficiency within industrial settings. In addressing this need, we introduce an autonomously navigating robotic system designed for comprehensive plant inspection. This innovative system comprises a robotic platform equipped with a diverse array of sensors integrated to facilitate the detection of various process and infrastructure parameters. These sensors encompass optical (LiDAR, Stereo, UV/IR/RGB cameras), olfactory (electronic nose), and acoustic (microphone array) capabilities, enabling the identification of factors such as methane leaks, flow rates, and infrastructural anomalies. The proposed system underwent individual evaluation at a wastewater treatment site within a chemical plant, providing a practical and challenging environment for testing. The evaluation process encompassed key aspects such as object detection, 3D localization, and path planning, achieving an average precision of around 0.7662 in the 2D object detection. In addition, specific evaluations were conducted for optical methane leak detection and localization, demonstrating the system’s capability to detect leaks as small as 40 mL min ^-1 . Furthermore, acoustic assessments focusing on pump equipment and gas leak localization yielded an absolute localization error of around 50 cm.

In this letter, we consider the problem of 2-D elliptic localization, where multiple spatially separated sensors, including the transmitters and receivers, are exploited to locate the signal reflecting/relaying target in the mixed line-of-sight/nonline-of-sight (NLOS) environments. We begin by revisiting a plain closed-form linear least squares (LS) solution. As it is vulnerable to the existence of erroneous time-sum-of-arrival (TSOA) measurements under the NLOS conditions, we then devise two new data-selective LS methods, by which the outliers can be identified and mitigated and a higher level of resistance to the NLOS bias errors can be provided. To conduct data selection, the first algorithm combines the use of the traditional linear LS estimator and an additional cost function, whereas the second relies on the parameterization of the TSOA-defined ellipses and follows a nonlinear LS estimation criterion. Based on the simulations, we demonstrate the effectiveness of the proposed methods in NLOS error mitigation at acceptable computational costs.