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.

This letter focuses on time-of-arrival based localization using multidimensional similarity (MDS) analysis under non-line-of-sight (NLOS) propagation. To handle row-column structured outliers in the MDS matrix introduced by NLOS errors, we present a novel robust matrix approximation scheme with the use of ℓ _2,1 -norm and apply the alternating direction method of multipliers to solve the resultant nonlinear constrained optimization problem. The proposed method does not require any prior knowledge of NLOS information and can benefit from a comparatively low complexity. Simulation results show that our algorithm is superior to several existing approaches in mild and moderate NLOS environments.

We investigate the problem of time-of-arrival (TOA)-based localization under possible non-line-of-sight (NLOS) propagation conditions. To robustify the squared-range-based location estimator, we follow the maximum correntropy criterion, essentially the Welsch M-estimator with a redescending influence function which behaves like $$\ell _0$$ℓ 0 -minimization toward the grossly biased measurements, to derive the formulation. The half-quadratic technique is then applied to settle the resulting optimization problem in an alternating maximization (AM) manner. By construction, the major computational challenge at each AM iteration boils down to handling an easily solvable generalized trust region subproblem. It is worth noting that the implementation of our localization method requires nothing but merely the TOA-based range measurements and sensor positions as prior information. Simulation and experimental results demonstrate the competence of the presented scheme in outperforming several state-of-the-art approaches in terms of positioning accuracy, especially in scenarios, where the percentage of NLOS paths is not large enough.

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.

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.