2008/02/01 by Дорон Эзри, D. Ezri, B.Z. Bobrovsky +5
Computer Science · Decision Sciences · Engineering · Mathematics · #60G35 #93E10 #94A05 #Control Systems and Identification #FOS: Computer and information sciences #FOS: Mathematics #Information Theory (cs.IT) #Optimization and Control (math.OC) #Probabilistic and Robust Engineering Design #Target Tracking and Data Fusion in Sensor Networks #cs.IT #math.IT #math.OC #msc:60G35 #msc:93E10 #msc:94A05
paper · pdf · doi:10.48550/arxiv.0802.0130
Non-causal estimation
arxiv created 2008/02/01 · openalex publication_date 2008/02/01 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We employ the variational formulation and the Euler-Lagrange equations to study the steady-state error in linear non-causal estimators (smoothers). We give a complete description of the steady-state error for inputs that are polynomial in time. We show that the steady-state error regime in a smoother is similar to that in a filter of double the type. This means that the steady-state error in the optimal smoother is significantly smaller than that in the Kalman filter. The results reveal a significant advantage of smoothing over filtering with respect to robustness to model uncertainty.