Multipath-based simultaneous localization and mapping (MP-SLAM) is a promising approach in wireless networks to jointly obtain position information of transmitters/receivers and information of the propagation environment. MP-SLAM models specular reflections at flat surfaces as virtual anchors (VAs), which are mirror images of base stations. Particle-based methods offer high flexibility and can approximate posterior probability density functions of the mobile agent state and the map feature states, (i.e., VA states) with complex shapes. However, they often require a large number of particles to counteract degeneracy in high-dimensional parameter spaces, leading to high computational complexity. Conversely using an insufficient number of particles leads to reduced estimation accuracy. In this paper, we introduce a low-complexity MP-SLAM algorithm using a sigma point (SP)-based implementation of the sum-product algorithm (SPA). We model the messages of continuous states of the agent and the VAs as Gaussian distributions and approximate nonlinearities via SP-transformations. This approach substantially reduces the computational complexity without decreasing accuracy. Since probabilistic data association yields Gaussian mixtures for the agent and VA states, we use moment matching to combine each mixture into a single Gaussian. Numerical results using synthetic and real data demonstrate that our method achieves significantly reduced computational runtimes compared to particle-based schemes, while exhibiting comparable (or even superior) localization and mapping performance.

We propose an sparse Bayesian learning (SBL)based method that leverages group sparsity and multiple parameterized dictionaries to detect the relevant dictionary entries and estimate their continuous parameters by combining data from multiple independent sensors. In a MIMO multi-radar setup, we demonstrate its effectiveness in jointly detecting and localizing multiple objects, while also emphasizing its broader applicability to various signal processing tasks. A key benefit of the proposed SBL-based method is its ability to resolve correlated dictionary entries-such as closely spaced objects-resulting in uncorrelated estimates that improve subsequent estimation stages. Through numerical simulations, we show that our method outperforms the newtonized orthogonal matching pursuit (NOMP) algorithm when two objects cross paths using a single radar. Furthermore, we illustrate how fusing measurements from multiple independent radars leads to enhanced detection and localization performance.

In this paper, we propose a direct multiobject tracking (MOT) approach for MIMO-radar signals that operates on raw sensor data via variational message passing (VMP). Unlike classical track-before-detect (TBD) methods, which often rely on simplified likelihood models and exclude nuisance parameters (e.g., object amplitudes, noise variance), our method adopts a superimposed signal model and employs a mean-field approximation to jointly estimate both object existence and object states. By considering correlations within in the radar signal due to closely spaced objects and jointly estimating nuisance parameters, the proposed method achieves robust performance for closeby objects and in low-signal-to-noise ratio (SNR) regimes. Our numerical evaluation based on MIMO-radar signals demonstrate that our VMP-based direct-MOT method outperforms a detect-then-track (DTT) pipeline comprising a super-resolution sparse Bayesian learning (SBL)-based estimation stage followed by classical MOT using global nearest neighbor data association and a Kalman filter.

Multipath-based simultaneous localization and mapping (MP-SLAM) is a well established approach to obtain position information of transmitters and receivers as well as information regarding the propagation environments in future multiple input multiple output (MIMO) communication systems. Conventional methods for MP-SLAM consider specular reflections of the radio signals occurring at smooth, flat surfaces, which are modeled by virtual anchors (VAs) that are mirror images of the physical anchors (PAs), with each VA generating a single multipath component (MPC). However, non-ideal reflective surfaces (such as walls covered by shelves or cupboards) cause dispersion effects that violate the VA model and lead to multiple MPCs that are associated to a single VA. In this paper, we introduce a Bayesian particle-based sum-product algorithm (SPA) for MP-SLAM in MIMO communications systems. Our method considers non-ideal reflective surfaces by jointly estimating the parameters of individual dispersion models for each detected surface in delay and angle domain leveraging multiple-measurement-to-feature data association. We demonstrate that the proposed SLAM method can robustly and jointly estimate the positions and dispersion extents of ideal and non-ideal reflective surfaces using numerical simulation.

Multipath-based simultaneous localization and mapping (SLAM) is a promising approach to obtain position information of transmitters and receivers as well as information regarding the propagation environments in future mobile communication systems. Usually, specular reflections of the radio signals occurring at flat surfaces are modeled by virtual anchors (VAs) that are mirror images of the physical anchors (PAs). In existing methods for multipath-based SLAM, each VA is assumed to generate only a single measurement. However, due to imperfections of the measurement equipment, such as non-calibrated antennas or model-mismatch due to roughness of the reflective surfaces, there are potentially multiple multipath components (MPCs) that are associated to one single VA. In this paper, we introduce a Bayesian particle-based sum-product algorithm (SPA) for multipath-based SLAM that can cope with multiple-measurements being associated to a single VA. Furthermore, we introduce a novel statistical measurement model that is strongly related to the radio signal. It introduces additional dispersion parameters into the likelihood function to capture additional MPCs-related measurements. We demonstrate that the proposed SLAM method can robustly fuse multiple measurements per VA based on numerical simulations.