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Low-Complexity 5g Slam with CKF-PHD Filter
- Kim, Hyowon;
- Granstrom, Karl;
- Kim, Sunwoo;
- Wymeersch, Henk
Citations
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17초록
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 significantly reduces the complexity while retaining the SLAM accuracy of the RBPF-PHD approach.
키워드
5G mmWave; CKF; cooperative SLAM; message passing; multi-model PHD; Audio signal processing; Message passing; Speech communication; Cubature kalman filters; Filter-based; High complexity; Map representations; Position estimates; Probability hypothesis density; Rao-blackwellized particle filter; Simultaneous localization and mapping; Kalman filters
- 제목
- Low-Complexity 5g Slam with CKF-PHD Filter
- 저자
- Kim, Hyowon; Granstrom, Karl; Kim, Sunwoo; Wymeersch, Henk
- 발행일
- 2020-05
- 유형
- Conference Paper
- 권
- 2020
- 호
- May
- 페이지
- 5220 ~ 5224