Variable structure multiple model fixed-interval smoothing

This paper focuses on fixed-interval smoothing for stochastic hybrid systems.When the truth-mode mismatch is encountered,existing smoothing methods based on fixed structure of model-set have significant performance degradation and are inapplicable.We develop a fixedinterval smoothing method based on forward-and backward-filtering in the Variable Structure Multiple Model(VSMM)framework in this paper.We propose to use the Simplified Equivalent model Interacting Multiple Model(SEIMM)in the forward and the backward filters to handle the difficulty of different mode-sets used in both filters,and design a re-filtering procedure in the model-switching stage to enhance the estimation performance.To improve the computational efficiency,we make the basic model-set adaptive by the Likely-Model Set(LMS)algorithm.It turns out that the smoothing performance is further improved by the LMS due to less competition among models.Simulation results are provided to demonstrate the better performance and the computational efficiency of our proposed smoothing algorithms.

Periodic acoustic source tracking using propagation delayed measurements

There exist a large class of acoustic sources which have an underlying periodic phenomenon. Unlike the well-studied Bearings-Only Tracking(BOT) of an aperiodic acoustic source,this paper considers the problem of tracking a periodic acoustic source. For periodic acoustic tracking, the signal emission time is known. However, the true measurement reception time is unknown because it is corrupted by noise due to propagation delay. We augment the sensor’s signal reception time onto bearing measurements, and the information of the delay constraint is included in the original bearing measurements to compensate for the propagation delay. A Cubature Kalman Filter(CKF) is used for periodic acoustic source tracking, in which measurement prediction cannot be obtained directly because the sensor’s position at the true measurement reception time is unknown.We solve this problem by using the implicit Gauss-Helmert Sensor Model(GHSM) for estimating the sensor’s position, which consists of the sensor’s motion equation and the known measured sensor’s signal reception time equation related to the state. Then a CKF based on the GHSM(CF-GHSM) is developed for periodic acoustic tracking. Illustrative examples demonstrate that the CF-GHSM algorithm is better than other algorithms for periodic acoustic source tracking.

A review on DFACS(II):Modeling and analysis of disturbances and noises

This paper presents Part II of a review on DFACS,which specifically focuses on the modeling and analysis of disturbances and noises in DFACSs.In Part I,the system composition and dynamics model of the DFACS were presented.In this paper,we discuss the effects of disturbance forces and noises on the system,and summarize various analysis and modeling methods for these interferences,including the integral method,frequency domain analysis method,and magnitude evaluation method.By analyzing the impact of disturbances and noises on the system,the paper also summarizes the system’s performance under slight interferences.Additionally,we highlight current research difficulties in the field of DFACS noise analysis.Overall,this paper provides valuable insights into the modeling and analysis of disturbances and noises in DFACSs,and identifies key areas for future research.

A review on DFACS(I):System design and dynamics modeling

The Drag-Free and Attitude Control System(DFACS)is a critical platform for various space missions,including high precision satellite navigation,geoscience and gravity field measurement,and space scientific experiments.This paper presents a comprehensive review of over sixty years of research on the design and dynamics model of DFACS.Firstly,we examine the open literature on DFACS and its applications in Drag-Free missions,providing readers with necessary background information on the field.Secondly,we analyze the system configurations and main characteristics of different DFACSs,paying particular attention to the coupling mechanism between the system configuration and dynamics model.Thirdly,we summarize the dynamics modeling methods and main dynamics models of DFACS from multiple perspectives,including common fundamentals and specific applications.Lastly,we identify current challenges and technological difficulties in the system design and dynamics modeling of DFACS,while suggesting potential avenues for future research.This paper aims to provide readers with a comprehensive understanding of the state-of-the-art in DFACS research,as well as the future prospects and challenges in this field.