김효원 교수

5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular networks when the base station and vehicles are equipped with large antenna arrays. However, radio-based positioning suffers from multipath signals generated by different types of objects in the physical environment. Multipath can be turned into a benefit, by building up a radio map (comprising the number of objects, object type, and object state) and using this map to exploit all available signal paths for position

In the upcoming sixth generation (6G) of wireless communication systems, reconfigurable intelligent surfaces (RISs) are regarded as one of the promising technological enablers, which can provide programmable signal propagation. Therefore, simultaneous radio localization and mapping (SLAM) with RISs appears as an emerging research direction within the 6G ecosystem. In this paper, we propose a novel framework of RIS-enabled radio SLAM for wireless operation without the intervention of access point

Radio-based vehicular simultaneous localization and mapping (SLAM) aims to localize vehicles while mapping the landmarks in the environment. We propose a sequence of three Poisson multi-Bernoulli mixture (PMBM) based SLAM filters, which handle the entire SLAM problem in a theoretically optimal manner. The complexity of the three proposed SLAM filters is progressively reduced while sustaining high accuracy by deriving SLAM density approximation with the marginalization of nuisance parameters (eit

Vehicle positioning based on GPS is limited due to multipath and blockage. 5G mmWave signals can provide an attractive complement, as it is possible to estimate the state of a vehicle (position and heading) from transmissions from a single base station. We propose a Bayesian 5G mmWave tracking filter, which explicitly relies on mapping the radio environment. The filter thus solves a novel type of simultaneous localization and mapping problem, which enables estimating not only the vehicle heading

5G new radio will provide a new paradigm in high accurate vehicle localization, reinforced by the use of large antenna arrays along with carefully designed broadband radio technology. However, a high computational load still remains unravelled in the context of cooperative localization, albeit with its advantages of high-precision localization. To alleviate such the computational burden, we develop a reliable technique of cooperative localization, addressed as a constraint satisfaction problem.

This paper presents a cooperative localization strategy via a distributed optimization technique known as the alternating direction method of multipliers (ADMM). In a Vehicular Ad hoc Network (VANET) where a vehicle communicates with neighboring vehicles via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, the developed algorithm utilizes three types of measurements, which are the pairwise relative distance, angle of arrival, and absolute positions for a subset of vehi

This letter proposes a cooperative localization algorithm based on connectivity information-aided belief propagation that can enhance belief propagation, which relies on the mutual exchange of location information with uncertainty. The connectivity information in the network is used to reduce the location uncertainty in the belief. As a result, it is possible to prevent the message flow of unnecessary location information which makes the location distribution too broad. The proposed algorithm is

In 5G mmWave, simultaneous localization and mapping (SLAM) allows devices to exploit map information to improve their position estimate. Even the most basic SLAM filter based on a Rao-Blackwellized particle filter (RBPF) combined with a probability hypothesis density (PHD) map representation exhibits high complexity. This paper proposes a new implementation method for the 5G SLAM using message passing (MP) and the cubature Kalman filter (CKF). We demonstrate that the proposed method significantl

5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular networks when the base station and vehicles are equipped with large antenna arrays. However, radio-based positioning suffers from multipath signals generated by different types of objects in the physical environment. Multipath can be turned into a benefit, by building up a radio map (comprising the number of objects, object type, and object state) and using this map to exploit all available signal paths for position

5G is expected to enable simultaneous vehicle localization and environment mapping (SLAM). Furthermore, vehicular networks will be covered with 5G small cells, wherein the map information is collected at each base station (BS) and then fused so as to promote the overall performance of SLAM. In 5G multi-cell SLAM, there are challenges such as the unknown number of targets, uncertainty regarding the association between the targets and the measurements, unknown types of targets, as well as map mana

We propose a framework for monostatic sensing by a user equipment (UE), aided by a reconfigurable intelligent surface (RIS) in environments with single- and double-bounce signal propagation. We design appropriate UE-side precoding and combining, to facilitate signal separation. We derive the adaptive detection probabilities of the resolvable signals, based on the geometric channel parameters of the links. Then, we estimate the passive objects using both the double-bounce signals via passive RIS

Belief propagation (BP) is a useful probabilistic inference algorithm for efficiently computing approximate marginal probability densities of random variables. However, in its standard form, BP is only applicable to the vector-type random variables with a fixed and known number of vector elements, while certain applications rely on random finite sets (RFSs) with an unknown number of vector elements. In this paper, we develop BP rules for factor graphs defined on sequences of RFSs where each RFS