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.

We revisit herein the problem of time-difference-of-arrival (TDOA) based localization under the mixed line-of-sight/non-line-of-sight propagation conditions. Adopting the strategy of statistically robustifying the non-outlier-resistant l ₂ loss, we formulate it as the minimization of a possibly non-differentiable generalized robust cost function, which is rooted in the analog locally competitive algorithm (LCA) for sparse approximation. We then present a Lagrange programming neural network to address the optimization formulation, with the non-differentiability issues being handled by grafting thereon the LCA concept of internal state dynamics. Compared with the existing algorithms, our approach is computationally less expensive, less reliant on the use of a priori error information, and observed to be capable of producing higher localization 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.

In this contribution, we explore further possibilities for statistical robustification of the traditional $\ell _{2}$ -space based time-of-arrival location estimator under impulsive noise conditions. We replace the non-robust $\ell _{2}$ loss by the $\ell _{p}$ counterpart with $1 \leq p < 2$ , and devise an iteratively reweighted least squares (IRLS) type approach to tackle the $\ell _{p}$ -minimization formulation in $\mathcal {O}(N_{\text{IRLS}}L)$ time. Here, the iteration number $N_{\text{IRLS}}$ is a constant typically of several tens and $L$ represents the number of sensors. The key enabler for the rapid but reliable update of location estimate at each iteration, is the sum-product message passing implemented in an acyclic factor graph derived from the corresponding subproblem. Numerical results demonstrate the superiority of our algorithm over several existing statistical robustification methods in terms of computational simplicity and positioning accuracy in the presence of impulsive noise.

Layer thickness and the speed of sound are important parameters for nondestructive testing applications. If one of the parameters is known, the other one can be determined by simple time-of-flight (TOF) measurement of ultrasound. However, often these parameters are both unknown. In this contribution, we examine and adapt ultrasonic imaging techniques using coded excitation signals to simultaneously measure the thickness and the speed of sound of homogeneous elastic plates of unknown material. Good axial resolution is required to measure thin samples. We present a new approach for transmission signal conditioning to improve axial resolution. This conditioning consists of enhancing spectral components that are damped by the transducer prior to transmit. Due to the long duration of coded excitation signals, pulse compression techniques are required for TOF measurements. Common pulse compression filters are discussed, and appropriate filtering of the compression waveform is designed to keep the sidelobe level (SLL) acceptably low. An experimental assessment of the presented measurement techniques reveals that the signal conditioning substantially increases the axial resolution. However, a tapered Wiener filter should be used for the best tradeoff between SLL and axial resolution. We used the proposed method to measure different plates of steel, aluminum, and polymethylmethacrylate of various thicknesses, and the results show very good agreement with the reference values, which we obtained with a micrometer screw and by standard TOF measurement, respectively. The relative error for the plate thickness is smaller than 2.2% and that for the speed of sound is smaller than 3%. It is remarkable that plate thickness could be measured down to 60% of the wavelength.