Adaptive Detection Probability for mmWave 5G SLAM

Wymeersch, Henk; Seco‐Granados, Gonzalo

doi:10.1109/6gsummit49458.2020.9083898

Cited by 16 publications

(10 citation statements)

References 25 publications

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“…, are utilized to avoid problems with misdetected landmarks outside the field-of-view (FOV) [21]. As in [13], it is assumed that g(•), Σ j k , P D k (•), P S k (•) and λ c (•) are known to the vehicle.…”

Section: B Measurement Modelmentioning

confidence: 99%

mmWave Simultaneous Localization and Mapping Using a Computationally Efficient EK-PHD Filter

Kaltiokallio¹,

Yu²,

Talvitie³

et al. 2021

Preprint

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Future cellular networks that utilize millimeter wave signals provide new opportunities in positioning and situational awareness. Large bandwidths combined with large antenna arrays provide unparalleled delay and angle resolution, allowing high accuracy localization but also building up a map of the environment. Even the most basic filter intended for simultaneous localization and mapping exhibits high computational overhead since the methods rely on sigma point or particle-based approximations. In this paper, a first order Taylor series based Gaussian approximation of the filtering distribution is used and it is demonstrated that the developed extended Kalman probability hypothesis density filter is computationally very efficient. In addition, the results imply that efficiency does not come with the expense of estimation accuracy since the method nearly achieves the position error bound.

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Section: B Measurement Modelmentioning

confidence: 99%

mmWave Simultaneous Localization and Mapping Using a Computationally Efficient EK-PHD Filter

Kaltiokallio¹,

Yu²,

Talvitie³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…), which refers to the probability that the PF can be detected or the probability that the PF can generate the measurement z (j) m,n in the channel estimation process [39]. The detection probability P…”

Section: Prior Distributions With Unknown Clutter Intensitymentioning

confidence: 99%

Robust Multipath-Assisted SLAM with Unknown Process Noise and Clutter Intensity

2021

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In multipath-assisted simultaneous localization and mapping (SLAM), the geometric association of specular multipath components based on radio signals with environmental features is used to simultaneously localize user equipment and map the environment. We must contend with two notable model parameter uncertainties in multipath-assisted SLAM: process noise and clutter intensity. Knowledge of these two parameters is critically important to multipath-assisted SLAM, the uncertainty of which will seriously affect the SLAM accuracy. Conventional multipath-assisted SLAM algorithms generally regard these model parameters as fixed and known, which cannot meet the challenges presented in complicated environments. We address this challenge by improving the belief propagation (BP)-based SLAM algorithm and proposing a robust multipath-assisted SLAM algorithm that can accommodate model mismatch in process noise and clutter intensity. Specifically, we describe the evolution of the process noise variance and clutter intensity via Markov chain models and integrate them into the factor graph representing the Bayesian model of the multipath-assisted SLAM. Then, the BP message passing algorithm is leveraged to calculate the marginal posterior distributions of the user equipment, environmental features and unknown model parameters to achieve the goals of simultaneous localization and mapping, as well as adaptively learning the process noise variance and clutter intensity. Finally, the simulation results demonstrate that the proposed approach is robust against the uncertainty of the process noise and clutter intensity and shows excellent performances in challenging indoor environments.

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“…The joint posterior distribution of the agent state, measurement biases, feature state, and data association vectors conditioned on measurements for all N time slots is f (u 1:N , α 1:N , ω 1:N , v 1:N , a 1:N , b 1:N |z 1:N ). According to (8), the joint posterior distribution is the product of ( 16), (21), and (22). As the factorizations of ( 21) and ( 22) are in the perspective of legacy and new features, we rewrite equations ( 21) and ( 22…”

Section: ) Data Fusionmentioning

confidence: 99%

Enabling Plug-and-Play and Crowdsourcing SLAM in Wireless Communication Systems

Yang

Wen

Jin

et al. 2021

Preprint

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Simultaneous localization and mapping (SLAM) during communication is emerging. This technology promises to provide information on propagation environments and transceivers' location, thus creating several new services and applications for the Internet of Things and environment-aware communication. Using crowdsourcing data collected by multiple agents appears to be much potential for enhancing SLAM performance. However, the measurement uncertainties in practice and biased estimations from multiple agents may result in serious errors. This study develops a robust SLAM method with measurement plug-and-play and crowdsourcing mechanisms to address the above problems.First, we divide measurements into different categories according to their unknown biases and realize a measurement plug-and-play mechanism by extending the classic belief propagation (BP)-based SLAM method. The proposed mechanism can obtain the time-varying agent location, radio features, and corresponding measurement biases (such as clock bias, orientation bias, and received signal strength model parameters), with high accuracy and robustness in challenging scenarios without any prior information on anchors and agents. Next, we establish a probabilistic crowdsourcing-based SLAM mechanism, in which multiple agents cooperate to construct and refine the radio map in a decentralized manner. Our study presents the first BP-based crowdsourcing that resolves the "double count" and "data reliability" problems through the flexible application of probabilistic data association methods.Numerical results reveal that the crowdsourcing mechanism can further improve the accuracy of the mapping result, which, in turn, ensures the decimeter-level localization accuracy of each agent in a challenging propagation environment.

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Adaptive Detection Probability for mmWave 5G SLAM

Cited by 16 publications

References 25 publications

mmWave Simultaneous Localization and Mapping Using a Computationally Efficient EK-PHD Filter

mmWave Simultaneous Localization and Mapping Using a Computationally Efficient EK-PHD Filter

Robust Multipath-Assisted SLAM with Unknown Process Noise and Clutter Intensity

Enabling Plug-and-Play and Crowdsourcing SLAM in Wireless Communication Systems

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