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Improper Complex-Valued multiple-model Adaptive Estimation

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2015年第6期63卷 1528-1542页

作者： Mohammadi, Arash Plataniotis, Konstantinos N. Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada

Motivated by the problem of estimating the discrete and continuous states of an improper complex-valued stochastic hybrid system, the paper proposes a class of widely linear (augmented) multiple model adaptive estimation algorithms, referred to as the C/MMAE. We show that for an improper complex-valued signal, pseudo-covariance of the innovation sequence is not zero and, therefore, carries useful statistical information regarding the unknown behaviour mode of the hybrid system. A new Bayesian law is, therefore, derived as a function of the pseudo-covariance of the innovation sequence and used to compute the probability that a hypothesized model is in effect at a certain time. We show that the C/MMAE, which uses the new Bayesian law and utilizes the complete second-order statistical characterization of the complex-valued innovation sequence, convergences faster than its counterpart, which only uses the conventional covariance of the innovation sequence. In order to simplify the computational complexity, we develop two circularized versions of the C/MMAE using a preprocessing step, referred to as the circularizing filter (CF). The CF is incorporated to convert the improper observations/innovations into the proper ones in order to reduce the computational complexity of the hypothesis testing step. Finally, an interacting version of the C/MMAE, referred to as C/IMM, is developed for improper complex-valued systems with Markovian switching coefficients. Simulation results indicate that the proposed hybrid estimators provide improved performance and convergence properties over their traditional counterparts.

关键词： Augmented complex Kalman filter improper signals interactive multiple-model multiple-model adaptive estimation widely-linear estimation

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Distributed Widely Linear multiple-model Adaptive Estimation

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2015年第3期1卷 164-179页

作者： Mohammadi, Arash Plataniotis, Konstantinos N. Univ Toronto Dept Elect & Comp Engn Toronto ON Canada Univ Toronto Dept Elect & Comp Engn Biomed signal Proc Toronto ON Canada

The paper considers the problem of estimating the state of a complex-valued stochastic hybrid system observed distributively using an agent/sensor network (AN/SN) with complexvalued (possibly noncircular) observations. In several distributed estimation problems, a suitable model to describe the underlying system is unknown a priori, i.e., distributed state estimation with structural uncertainty. Motivated by application of widely linear processing techniques in such problems, the paper proposes a class of distributed multiple-model adaptive estimation algorithms, referred to as the CD/MMAE. By incorporating the particular structure of the complex-valued observations on the second moment, first we develop two hierarchical CD/MMAE implementations and then use them as the building blocks and develop a diffusion-based hybrid estimator for decentralized estimation without incorporation of a fusion centre. The paper derives a new form of the adaptation law and a new form of information fusion, which takes advantage of the full second-order statistical properties of the underlying observations. Convergence properties of the proposed diffusion-based CD/MMAE are then investigated. We show that the adaptive weight of all local nodes converges to the true mode with probability one. Simulation results indicate that the proposed hybrid estimators provide improved performance and convergence properties over their traditional counterparts.

关键词： Augmented complex Kalman filter Improper signals interactive multiple-model multiple-model adaptive estimation Widely-linear estimation

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Online Mission Planning for Cooperative Target Tracking of Marine Vehicles

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IFAC Proceedings Volumes 2009年第18期42卷 185-189页

作者： M. Bayat F. Vanni A. Pedro Aguiar Institute for Systems and Robotics (ISR) Instituto Superior Técnico (IST) Lisbon Portugal

In the cooperative target tracking maneuvering a group of vehicles is required to follow a target while maintaining a desired formation in space. For that effect, the trajectory of the target has to be estimated (for example by one of the vehicles in formation), translated into mission codes (to save communication resources) and transmitted to the other vehicles. This paper addresses the online mission planning task of converting the absolute or relative position data available from the positioning system installed on the target (e.g. INS, GPS) into a set of lines and constant curvature arcs. Two algorithms are proposed: one using a polynomial fitting and a more complex one using an interactive multiple-model Kalman filter. Simulation results using experimental data and a set of simulated data corrupted with noise and data loss are presented and discussed.

关键词： Target Tracking Mission Planning interactive multiple-model Polynomial Fitting

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IMM-EKF Based GPS/INS Combined Positioning Method for Drone

IMM-EKF Based GPS/INS Combined Positioning Method for Drone

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作者： Rongjian Wang The High School Affiliated to Renmin University Of China

Satellite navigation system is currently the most widely used positioning and navigation *** can provide users with outdoor real-time,high-precision location information,but it is susceptible to environmental occlusion and carrier motion interference,making location availability *** paper addresses the needs of UAVs for high accuracy and high availability of position information,using inertial devices and satellite navigation systems for integrated navigation,and proposes a multi-model filtering method for tracking different motion models and filtering to improve the fusion positioning Accuracy and robustness,enabling high-precision,highavailability positioning in complex *** by simulating the uniform velocity motion(CV),uniform acceleration motion(CA) and uniform turning motion(CT) of the UAV,the positioning performance of the method is *** the simulation results,the method improves the integration of inertial navigation and *** accuracy and reliability.

关键词： extended Kalman filter drone interactive multiple-model integrated navigation

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