2010/09/22 by Boris N. Oreshkin, Xuan Liu, Mark Coates +1 · 23 citations
Computer Science · Engineering · Mathematics · #Algorithm #Artificial intelligence #Computational complexity theory #Computer science #Distributed Sensor Networks and Detection Algorithms #Fault Detection and Control Systems #Identification (biology) #Kalman filter #Mathematical optimization #Mathematics #Optimization problem #Particle filter #Reduction (mathematics) #Selection (genetic algorithm) #Sequence (biology) #State (computer science) #Target Tracking and Data Fusion in Sensor Networks #Task (project management) #cs.MA #stat.AP
paper · pdf · doi:10.1109/tsp.2011.2140110
published in IEEE Transactions on Signal Processing 59(7), 3369-3381 (Institute of Electrical and Electronics Engineers)
arxiv created 2010/09/22 · openalex publication_date 2011/04/08 · arxiv updated 2015/05/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
This paper proposes a novel framework for delay-tolerant particle filtering that is computationally efficient and has limited memory requirements. Within this framework the informativeness of a delayed (out-of-sequence) measurement (OOSM) is estimated using a lightweight procedure and uninformative measurements are immediately discarded. The framework requires the identification of a threshold that separates informative from uninformative; this threshold selection task is formulated as a constrained optimization problem, where the goal is to minimize state estimation error whilst controlling the computational requirements. We develop an algorithm that provides an approximate solution for the optimization problem. Simulation experiments provide an example where the proposed framework processes less than 40% of all OOSMs with only a small reduction in state estimation accuracy.