This paper presents a factor graph formulation and particle-based sum-product algorithm (SPA) for robust sequential localization in multipath-prone environments. The proposed algorithm jointly performs data association, sequential estimation of a mobile agent position, and adapts all relevant model parameters. We derive a novel non-uniform false alarm (FA) model that captures the delay and amplitude statistics of the multipath radio channel. This model enables the algorithm to indirectly exploit position-related information contained in the multipath components (MPCs) for the estimation of the agent position without using any prior information such as floorplan information or training data. Using simulated and real measurements in different channel conditions, we demonstrate that the algorithm can provide high-accuracy position estimates even in fully obstructed line-of-sight (OLOS) situations and show that the performance of our algorithm constantly attains the posterior Cramér-Rao lower bound (P-CRLB), facilitating the additional information contained in the presented FA model. The algorithm is shown to provide robust estimates in both, dense multipath channels as well as channels showing specular, resolved MPCs, significantly outperforming state-of-the-art radio-based localization methods.

We present a factor graph formulation and particle-based sum-product algorithm for robust localization and tracking in multipath-prone environments. The proposed sequential algorithm jointly estimates the mobile agent’s position together with a time-varying number of multipath components (MPCs). The MPCs are represented by "delay biases" corresponding to the offset between line-of-sight (LOS) component delay and the respective delays of all detectable MPCs. The delay biases of the MPCs capture the geometric features of the propagation environment with respect to the mobile agent. Therefore, they can provide position-related information contained in the MPCs without explicitly building a map of the environment. We demonstrate that the position-related information enables the algorithm to provide high-accuracy position estimates even in fully obstructed line-of-sight (OLOS) situations. Using simulated and real measurements in different scenarios we demonstrate that the proposed algorithm significantly outperforms state-of-the-art multipath-aided tracking algorithms and show that the performance of our algorithm constantly attains the posterior Cramér-Rao lower bound (P-CRLB). Furthermore, we demonstrate the implicit capability of the proposed method to identify unreliable measurements and, thus, to mitigate lost tracks.

Indoor positioning is a thriving research area which is slowly gaining market momentum. Its applications are mostly customised, ad hoc installations; ubiquitous applications analogous to GNSS for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the Indoor Positioning and Indoor Navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing Tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site Tracks. In contrast with on-site Tracks where competitors bring their systems on site - which were impossible to organise during 2021 and 2022 - in off-site Tracks competitors download pre-recorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, Tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem.

This paper presents a neural-enhanced probabilistic model and corresponding factor graph-based sum-product algorithm for robust localization and tracking in multipath-prone environments. The introduced hybrid probabilistic model consists of physics-based and data-driven measurement models capturing the information contained in both, the line-of-sight (LOS) component as well as in multipath components (NLOS components). The physics-based and data-driven models are embedded in a joint Bayesian framework allowing to derive from first principles a factor graph-based algorithm that fuses the information of these models. The proposed algorithm uses radio signal measurements from multiple base stations to robustly estimate the mobile agent's position together with all model parameters. It provides high localization accuracy by exploiting the position-related information of the LOS component via the physics-based model and robustness by exploiting the geometric imprint of multipath components independent of the propagation channel via the data-driven model. In a challenging numerical experiment involving obstructed LOS situations to all anchors, we show that the proposed sequential algorithm significantly outperforms state-of-the-art methods and attains the posterior Cram'er-Rao lower bound even with training data limited to local regions.

Due to the use of wide-band analog front-ends in order to cover the many different frequency bands used in Long Term Evolution (LTE), blocker signals received by the antenna may reach the mixer input. As a consequence, squarewave mixer implementations, as e.g. the 25% duty-cycle currentdriven passive mixer, may lead to the down-conversion of these blocker signals by their harmonic response. This contribution describes a harmonic rejection (HR) strategy, which modifies the local oscillator (LO) waveform of a 25% duty-cycle currentdriven passive mixer, to suppress specific harmonics. Two HR control signal waveforms are proposed and their performance is evaluated by circuit simulations using a 28nm CMOS technology. By applying the proposed concept, the down-conversion of a blocker by the mixer’s 3rd order harmonic response can be suppressed by more than 30 dB, and the receiver noise figure (NF) is improved by 21.1 dB for a blocker power of -5dBm at the low-noise amplifier (LNA) input.

Reliable time of arrival (ToA) estimation in dense multipath (DM) environments is a difficult task, especially when strong interference is present. The increasing number of multiple services in a shared spectrum comes with the demand for interference mitigation techniques. Multiple receiver elements, even in low-energy devices, allow for interference mitigation by processing coherent signals, but computational complexity has to be kept at a minimum. We propose a low-complexity, linearly constrained minimum variance (LCMV) interference mitigation approach in combination with a detection-based ToA estimator. The performance of the method within a realistic multipath and interference environment is evaluated based on measurements and simulations. A statistical analysis of the ToA estimation error is provided in terms of the mean absolute error (MAE), and the results are compared to those of a band-stop filter-based interference blocking approach. While the focus is on receivers with only two elements, an extension to multiple elements is discussed as well. Results show that the influence of strong interference can be drastically reduced, even when the interference bandwidth exceeds 60% of the signal bandwidth. Moreover, the algorithm is robust to uncertainties in the angle of arrival (AoA) of the desired signal. Based on these results, the proposed mitigation method is well suited when the interference bandwidth is large and when computational power is a critical resource.