2A2-L03 Robustness Improvement of Localization Using Particle Filter by Sensor Fusion Method Based on KL-Divergence

2015 ◽  
Vol 2015 (0) ◽  
pp. _2A2-L03_1-_2A2-L03_4
Author(s):  
Keita SUYAMA ◽  
Yuki FUNABORA ◽  
Shinji DOKI ◽  
Kae DOKI
Keyword(s):  
Particle Filter ◽  
Sensor Fusion ◽  
Fusion Method ◽  
Kl Divergence

scholarly journals Research on the Application of User Recommendation Based on the Fusion Method of Spatially Complex Location Similarity

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lili Wang ◽  
Ting Shi ◽  
Shijin Li
Keyword(s):  
Collaborative Filtering ◽  
Similarity Measure ◽  
Recall Rate ◽  
Cosine Similarity ◽  
Fusion Method ◽  
Complex Matrix ◽  
Recommendation Algorithm ◽  
Kl Divergence ◽  
Experimental Effect ◽  
User Recommendation

Since the user recommendation complex matrix is characterized by strong sparsity, it is difficult to correctly recommend relevant services for users by using the recommendation method based on location and collaborative filtering. The similarity measure between users is low. This paper proposes a fusion method based on KL divergence and cosine similarity. KL divergence and cosine similarity have advantages by comparing three similar metrics at different K values. Using the fusion method of the two, the user’s similarity with the preference is reused. By comparing the location-based collaborative filtering (LCF) algorithm, user-based collaborative filtering (UCF) algorithm, and user recommendation algorithm (F2F), the proposed method has the preparation rate, recall rate, and experimental effect advantage. In different median values, the proposed method also has an advantage in experimental results.

scholarly journals An evidential sensor fusion method in fault diagnosis

2016 ◽  
Vol 8 (3) ◽  
pp. 168781401664182 ◽  
Author(s):  
Wen Jiang ◽  
Boya Wei ◽  
Chunhe Xie ◽  
Deyun Zhou
Keyword(s):  
Fault Diagnosis ◽  
Sensor Fusion ◽  
Fusion Method

Sensor Fusion based on the Particle Filter and Multi-Rate Kalman Filter

2017 ◽  
Vol 23 (11) ◽  
pp. 969-980
Author(s):  
Geon-Il Lee ◽  
Chang Mook Kang ◽  
Seung-Hi Lee ◽  
Chung Choo Chung
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Sensor Fusion
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