The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
대규모 실내 주차장에서 WiFi를 기반으로 한 실내 지문 위치 확인은 차량 조회에 점점 더 널리 사용되고 있습니다. 그러나 실내 주차장 환경의 특수성과 복잡성으로 인해 위치 기능성을 확보하는 것이 과제입니다. 참조점(RP) 배포 필요성과 오프라인 샘플링 워크로드를 줄이기 위해 P-FP(파티션 피팅 지문 알고리즘)가 제안됩니다. 대상의 위치 정확도를 향상시키기 위해 P-FP 기반 임계값을 갖는 SIR(샘플링 중요도 재샘플링) 입자 필터인 PS-FP 알고리즘이 추가로 제안됩니다. 먼저, 다항식 피팅 모델을 이용하여 실내주차장 전체를 분할하고, 분할된 각 구간의 환경계수를 구한다. 오프라인 지문 데이터베이스의 품질을 향상시키기 위해 피팅 값과 실제 측정 값의 차이를 이용하여 오류 특성 매트릭스를 구축합니다. 따라서 가상 RP가 배포되고 C-평균 클러스터링이 활용되어 온라인 계산량을 줄입니다. 위치 좌표의 변동을 줄이기 위해 임계값 설정이 있는 SIR 입자 필터를 채택하여 위치 좌표를 최적화합니다. 마지막으로 평균 위치 오차를 비교하여 최적의 임계값을 구한다. 테스트 결과 PS-FP는 적은 RP로 높은 위치 정확도를 달성할 수 있었고 평균 위치 오류는 약 0.7m에 불과한 것으로 나타났습니다. 누적 분포 함수(CDF)는 PS-FP를 사용하여 98을 보여줍니다.% 위치 오류가 2m 이내입니다. WKNN(Weighted K-Nearest Neighbors) 알고리즘과 비교하여 PS-FP의 위치 정확도는 84를 나타냅니다.% 개선.
Weibo WANG
Xihua University
Jinghuan SUN
Xihua University
Ruiying DONG
Xihua University
Yongkang ZHENG
State Grid Sichuan Electric Power Research Institute
Qing HUA
Shandong Normal University
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
Weibo WANG, Jinghuan SUN, Ruiying DONG, Yongkang ZHENG, Qing HUA, "The Development of a High Accuracy Algorithm Based on Small Sample Size for Fingerprint Location in Indoor Parking Lot" in IEICE TRANSACTIONS on Communications,
vol. E101-B, no. 12, pp. 2479-2486, December 2018, doi: 10.1587/transcom.2018EBP3004.
Abstract: Indoor fingerprint location based on WiFi in large-scale indoor parking lots is more and more widely employed for vehicle lookup. However, the challenge is to ensure the location functionality because of the particularity and complexities of the indoor parking lot environment. To reduce the need to deploy of reference points (RPs) and the offline sampling workload, a partition-fitting fingerprint algorithm (P-FP) is proposed. To improve the location accuracy of the target, the PS-FP algorithm, a sampling importance resampling (SIR) particle filter with threshold based on P-FP, is further proposed. Firstly, the entire indoor parking lot is partitioned and the environmental coefficients of each partitioned section are gained by using the polynomial fitting model. To improve the quality of the offline fingerprint database, an error characteristic matrix is established using the difference between the fitting values and the actual measured values. Thus, the virtual RPs are deployed and C-means clustering is utilized to reduce the amount of online computation. To decrease the fluctuation of location coordinates, the SIR particle filter with a threshold setting is adopted to optimize the location coordinates. Finally, the optimal threshold value is obtained by comparing the mean location error. Test results demonstrated that PS-FP could achieve high location accuracy with few RPs and the mean location error is only about 0.7m. The cumulative distribution function (CDF) show that, using PS-FP, 98% of location errors are within 2m. Compared with the weighted K-nearest neighbors (WKNN) algorithm, the location accuracy by PS-FP exhibit an 84% improvement.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2018EBP3004/_p
부
@ARTICLE{e101-b_12_2479,
author={Weibo WANG, Jinghuan SUN, Ruiying DONG, Yongkang ZHENG, Qing HUA, },
journal={IEICE TRANSACTIONS on Communications},
title={The Development of a High Accuracy Algorithm Based on Small Sample Size for Fingerprint Location in Indoor Parking Lot},
year={2018},
volume={E101-B},
number={12},
pages={2479-2486},
abstract={Indoor fingerprint location based on WiFi in large-scale indoor parking lots is more and more widely employed for vehicle lookup. However, the challenge is to ensure the location functionality because of the particularity and complexities of the indoor parking lot environment. To reduce the need to deploy of reference points (RPs) and the offline sampling workload, a partition-fitting fingerprint algorithm (P-FP) is proposed. To improve the location accuracy of the target, the PS-FP algorithm, a sampling importance resampling (SIR) particle filter with threshold based on P-FP, is further proposed. Firstly, the entire indoor parking lot is partitioned and the environmental coefficients of each partitioned section are gained by using the polynomial fitting model. To improve the quality of the offline fingerprint database, an error characteristic matrix is established using the difference between the fitting values and the actual measured values. Thus, the virtual RPs are deployed and C-means clustering is utilized to reduce the amount of online computation. To decrease the fluctuation of location coordinates, the SIR particle filter with a threshold setting is adopted to optimize the location coordinates. Finally, the optimal threshold value is obtained by comparing the mean location error. Test results demonstrated that PS-FP could achieve high location accuracy with few RPs and the mean location error is only about 0.7m. The cumulative distribution function (CDF) show that, using PS-FP, 98% of location errors are within 2m. Compared with the weighted K-nearest neighbors (WKNN) algorithm, the location accuracy by PS-FP exhibit an 84% improvement.},
keywords={},
doi={10.1587/transcom.2018EBP3004},
ISSN={1745-1345},
month={December},}
부
TY - JOUR
TI - The Development of a High Accuracy Algorithm Based on Small Sample Size for Fingerprint Location in Indoor Parking Lot
T2 - IEICE TRANSACTIONS on Communications
SP - 2479
EP - 2486
AU - Weibo WANG
AU - Jinghuan SUN
AU - Ruiying DONG
AU - Yongkang ZHENG
AU - Qing HUA
PY - 2018
DO - 10.1587/transcom.2018EBP3004
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E101-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 2018
AB - Indoor fingerprint location based on WiFi in large-scale indoor parking lots is more and more widely employed for vehicle lookup. However, the challenge is to ensure the location functionality because of the particularity and complexities of the indoor parking lot environment. To reduce the need to deploy of reference points (RPs) and the offline sampling workload, a partition-fitting fingerprint algorithm (P-FP) is proposed. To improve the location accuracy of the target, the PS-FP algorithm, a sampling importance resampling (SIR) particle filter with threshold based on P-FP, is further proposed. Firstly, the entire indoor parking lot is partitioned and the environmental coefficients of each partitioned section are gained by using the polynomial fitting model. To improve the quality of the offline fingerprint database, an error characteristic matrix is established using the difference between the fitting values and the actual measured values. Thus, the virtual RPs are deployed and C-means clustering is utilized to reduce the amount of online computation. To decrease the fluctuation of location coordinates, the SIR particle filter with a threshold setting is adopted to optimize the location coordinates. Finally, the optimal threshold value is obtained by comparing the mean location error. Test results demonstrated that PS-FP could achieve high location accuracy with few RPs and the mean location error is only about 0.7m. The cumulative distribution function (CDF) show that, using PS-FP, 98% of location errors are within 2m. Compared with the weighted K-nearest neighbors (WKNN) algorithm, the location accuracy by PS-FP exhibit an 84% improvement.
ER -