In this paper, we present a multipath-based simultaneous localization and mapping (SLAM) algorithm that continuously adapts mulitiple map feature (MF) models describing specularly reflected multipath components (MPCs) from flat surfaces and point-scattered MPCs, respectively. We develop a Bayesian model for sequential detection and estimation of interacting MF model parameters, MF states and mobile agent's state including position and orientation. The Bayesian model is represented by a factor graph enabling the use of belief propagation (BP) for efficient computation of the marginal posterior distributions. The algorithm also exploits amplitude information enabling reliable detection of weak MFs associated with MPCs of very low signal-to-noise ratios (SNRs). The performance of the proposed algorithm is evaluated using real millimeter-wave (mmWave) multiple-input-multiple-output (MIMO) measurements with single base station setup. Results demonstrate the excellent localization and mapping performance of the proposed algorithm in challenging dynamic outdoor scenarios.
The Cramér-Rao lower bound on the ranging error variance is revisited to quantify the influence of dense multipath in indoor environments. Our analytical results yield novel insight on the scaling of the ranging and positioning accuracy as a function of bandwidth and number of diversity branches. It also yields insight on the detectability of the useful line-of-sight signal component. It is found that the Fisher information scales faster than quadratically in bandwidth but only linearly in the number of independent diversity branches. We investigate the entire bandwidth-range from the flat-fading narrowband case up to ultra-wideband.
We apply joint probabilistic data association (JPDA) to multipath-assisted indoor navigation and tracking (MINT). In MINT, position-related information in multipath components (MPCs) is exploited to increase the accuracy and robustness of indoor tracking. Conventional MINT algorithms are based on deterministic data association and perform a global nearest-neighbor "hard" association of MPC-related delays with the room geometry. In such a setup, incorrect associations may lead to severe tracking errors and to divergence of the Bayesian filter. Here, we propose a JPDA-MINT algorithm that is able to handle difficult situations where MPC delays overlap and data association is ambiguous. The algorithm is based on a recently introduced loopy belief propagation scheme that performs probabilistic data association jointly with agent state estimation, scales well in all relevant systems parameters, and has a very low computational complexity. Using data from an ultra-wideband indoor measurement campaign, we demonstrate that the proposed JPDA-MINT algorithm is highly accurate and more robust than the conventional MINT algorithms based on deterministic data association.
Location awareness is a key factor for a wealth of wireless indoor applications. Its provision requires the careful fusion of diverse information sources. For agents that use radio signals for localization, this information may either come from signal transmissions with respect to fixed anchors, from cooperative transmissions inbetween agents, or from radar-like monostatic transmissions. Using a-priori knowledge of a floor plan of the environment, specular multipath components can be exploited, based on a geometric-stochastic channel model. In this paper, a unified framework is presented for the quantification of this type of position-related information, using the concept of equivalent Fisher information. We derive analytical results for the Cramer-Rao lower bound of multipath-assisted positioning, considering bistatic transmissions between agents and fixed anchors, monostatic transmissions from agents, cooperative measurements inbetween agents, and combinations thereof, including the effect of clock offsets. Awareness of this information enables highly accurate and robust indoor positioning. Computational results show the applicability of the framework for the characterization of the localization capabilities of a given environment, quantifying the influence of different system setups, signal parameters, and the impact of path overlap.
In a radio propagation channel, deterministic reflections carry important position-related information. With the help of prior knowledge such as a floor plan, this information can be exploited for indoor localization. This letter presents the improvement of a multipath-assisted tracking approach using information about the relevance of deterministic multipath components in an environment. This information is fed to a tracking filter as an observation noise model. It is estimated from a few training signals between anchors and an agent at known positions. Tracking results are presented for measurements in a partial non-line-of-sight environment. At a bandwidth of 2 GHz, an accuracy of 4 cm can be achieved for over 90% of the positions if additional channel information is available. Otherwise, this accuracy is only possible for about 45% of the positions. The covariance of the estimation matches closely to the corresponding Cramèr-Rao lower bound.
Multiple-Input Multiple-Output (MIMO) systems are essential for wireless communications. Since classical algorithms for symbol detection in MIMO setups require large computational resources or provide poor results, data-driven algorithms are becoming more popular. Most of the proposed algorithms, however, introduce approximations leading to degraded performance for realistic MIMO systems. In this paper, we introduce a neural-enhanced hybrid model, augmenting the analytic backbone algorithm with state-of-the-art neural network components. In particular, we introduce a self-attention model for the enhancement of the iterative Orthogonal Approximate Message Passing (OAMP)-based decoding algorithm. In our experiments, we show that the proposed model can outperform existing data-driven approaches for OAMP while having improved generalization to other SNR values at limited computational overhead.
Location awareness is a key factor for a wealth of wireless indoor applications. Its provision requires the careful fusion of diverse information sources. For agents that use radio signals for localization, this information may either come from signal transmissions with respect to fixed anchors, from cooperative transmissions inbetween agents, or from radar-like monostatic transmissions. Using a-priori knowledge of a floor plan of the environment, specular multipath components can be exploited, based on a geometric-stochastic channel model. In this paper, a unified framework is presented for the quantification of this type of position-related information, using the concept of equivalent Fisher information. We derive analytical results for the Cram\'er-Rao lower bound of multipath-assisted positioning, considering bistatic transmissions between agents and fixed anchors, monostatic transmissions from agents, cooperative measurements inbetween agents, and combinations thereof, including the effect of clock offsets. Awareness of this information enables highly accurate and robust indoor positioning. Computational results show the applicability of the framework for the characterization of the localization capabilities of a given environment, quantifying the influence of different system setups, signal parameters, and the impact of path overlap.
In this paper we propose a Bayesian agent network planning algorithm for information-criterion-based measurement selection for cooperative localization in static networks with anchors. This allows to increase the accuracy of the agent positioning while keeping the number of measurements between agents to a minimum. The proposed algorithm is based on minimizing the conditional differential entropy (CDE) of all agent states to determine the optimal set of measurements between agents. Such combinatorial optimization problems have factorial runtime and quickly become infeasible, even for a rather small number of agents. Therefore, we propose a Bayesian agent network planning algorithm that performs a local optimization for each state. Experimental results demonstrate a performance improvement compared to a random measurement selection strategy, significantly reducing the position RMSE at a smaller number of measurements between agents.
Exploiting single-shot channel measurements for estimating radio frequency channel parameters remains an active research topic. In presence of dense multipath, it is challenging to estimate accurately parameters of multipath components (MPCs). While standard methods identify MPCs with high energy, we propose to estimate MPCs which are uncorrupted by interfering dense multipath. Based on real and synthetic data, our evaluations demonstrate the potential performance gain with respect to standard methods. The results are compared to the Cramér-Rao bound showing the efficiency of the proposed method.
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