In the future generation of mobile communication systems, many scenarios will require high-resolution range-based positioning. However, the accuracy of time-of-arrival (TOA) estimation algorithms for single wideband systems is generally limited due to the constraints of available bandwidth. Currently, leveraging multiple available frequency bands and carrier frequency switching to obtain multi-band channel state information (CSI) for TOA estimation is gaining popularity, as it effectively constructs an equivalent wideband signal. In this paper, we proposed a subspace-based TOA estimation algorithm using multi-band CSI by constructing a correlation matrix and then exploiting its mathematical properties. The proposed algorithm eliminates the need for grid search and thus has lower computational complexity. We analyze the Cramér-Rao Bound (CRB) for the multi-band data model and derive a tighter lower bound for our algorithm. Simulation results show that our algorithm converges to the CRB at high SNR and closely follows the proposed lower bound across all SNR levels. Additionally, our algorithm demonstrates superior performance compared to single-band algorithms and offers advantages in either accuracy or complexity when compared to other multi-band algorithms.

In this letter, we introduce a novel carrier-phase-based frequency syntonization method for multi-band systems. Different from other methods, by leveraging the wide bandwidth characteristics of multi-band signals, our approach shows superior clock-skew estimation performance compared to single-band signals. We proposed a bidirectional communication protocol to collect multi-band phase difference of arrival (PDOA) measurements. Then, we derive an approximate maximum-likelihood estimator for clock-skew estimation. Finally, link-level simulations demonstrate that the proposed method's estimation results closely approach the Cramér-Rao bound (CRB) for high signal-to-noise ratio (SNR), validating the effectiveness of our estimator. The comparison with other methods further highlights the superiority of our algorithm.

The high-resolution TOA estimation of multipath channel is essential for many areas while the resolution of conventional algorithms is constrained by the system bandwidth. With the advancement of communication technology, more applications demand higher positioning accuracy, yet single-band systems are restricted by their limited bandwidth. To address this, we consider utilizing decentralized multi-band signals to obtain a larger available bandwidth for high-resolution TOA estimation. Taking dual-band signals as an example, we exploit the dual-band coherent subspace and propose a novel subspace-based high-resolution TOA estimation algorithm. The proposed algorithm does not require parameter matching with high complexity and is suitable for the existing dual-band communication systems. We also derive the Cramér-Rao Bound (CRB) for the dual-band signal model. Simulation results show that the performance of the proposed algorithm is better than existing subspace-based algorithms available for dual-band signals and converges to the derived CRB.

By leveraging geometric and probabilistic information contained in multipath components (MPCs), multipath-based simultaneous localization and mapping (SLAM) enables the localization of both mobile agents and a varying number of map features (MFs). Traditional solutions assume that each MPC is associated with a single MF, while focusing only on MFs’ positions. However, advancements in communication technologies provide higher-resolution multipath parameters (MPPs), resulting in large MFs generating multiple MPCs. This challenges the existing association assumptions and provides opportunities to estimate the extents and shapes of MFs. In this paper, we first integrate the many-for-one association relationship and random matrix-based extent modeling into the existing Bayesian SLAM framework. We then categorize MFs by shape, developing multiple shape and measurement models for each category. By exploring these models, we derive the joint posterior distribution and represent it using a factor graph, which serves as the foundation for our proposed message passing algorithm. Numerical results demonstrate that the proposed algorithm achieves superior localization and mapping performance, successfully classifying different types of MFs while estimating their orientations and sizes.

Loop closure detection is an important aspect of simultaneous localization and mapping (SLAM) to correct long-term drift by finding overlapping trajectories. However, in multipath SLAM using radio signals, due to the limitation of low-resolution sensors and few features, most of the existing works have not fully explored loop closure. In this paper, we propose a loop closure detection algorithm for multipath SLAM, Wi-Loop SLAM, that can reduce cumulative errors. To recognize previously visited places, we use the multipath parameters extracted from received signals as matching features without introducing new information. We use a delayed selection strategy to ensure that the most appropriate pairing is selected, and a confirmation of location drift is required to minimize unnecessary computation. Finally, we derive a Bayesian model to perform loop optimization with the particle-based sum-product algorithm (SPA). Simulation results show that the proposed Wi-Loop SLAM can correct the state estimate drift in the environment where propagation paths are often obscured.