Distributed Particle Filtering over Sensor Networks for Autonomous Navigation of UAVs

A derivative-free distributed filtering approach for sensorless control of nonlinear systems

“…The results have also been extended to the case of nonlinear non-Gaussian dynamical systems (Sun and Deng 2005;Makarenko and Durrant-Whyte 2006;Rigatos 2010a;Sun, Zhang, and Guo 2011).…”

Target Tracking for Mobile Sensor Networks Using Distributed Motion Planning and Distributed Filtering

“…Each vehicle can be equipped with various sensors, such as odometric sensors, cameras and non-imaging sensors such as sonar, radar and thermal signature sensors. These vehicles can be considered as mobile sensors while the ensemble of the autonomous vehicles constitutes a mobile sensor network (Rigatos, 2010a), (Olfati-Saber, 2005), (Olfati-Saber, 2007), (Elston & Frew, 2007). At each time instant each vehicle can obtain a measurement of the target's cartesian coordinates and orientation.…”

“…Again the local information matrices and the local information state vectors are transferred to an aggregation filter which produces the global estimation of the system's state vector. Using distributed EKFs and fusion through the Extended Information Filter or distributed UKFs through the Unscented Information Filter is more robust comparing to the centralized Extended Kalman Filter, or similarly the centralized Unscented Kalman Filter since, (i) if a local filter is subject to a fault then state estimation is still possible and can be used for accurate localization of the target, (ii) communication overhead remains low even in the case of a large number of distributed measurement units, because the greatest part of state estimation is performed locally and only information matrices and state vectors are communicated between the local filters, (iii) the aggregation performed also compensates for deviations in the state estimates of the local filters (Rigatos, 2010a). The structure of the paper is as follows: in Section 2 the problem of target tracking in mobile sensor networks is studied.…”

Distributed Nonlinear Filtering Under Packet Drops and Variable Delays for Robotic Visual Servoing

“…It has been shown that scalable distributed state estimation can be achieved for robotic models, when the measurements are linear functions of the state and the associated process and measurement noise models follow a Gaussian distribution (Mahler, 2007), (Nettleton et al, 2003). The results have been also extended to the case of nonlinear non-Gaussian dynamical systems (Rigatos, 2010a), (Makarenko & Durrant-Whyte, 2006). An issue which is associated to the implementation of such networked control systems is how to compensate for random delays and packet losses so as to enhance the accuracy of estimation and consequently to improve the stability of the control loop.…”

“…First, the chapter examines the problem of distributed nonlinear filtering over a communication/sensors network, and the use of the estimated state vector in a control loop. As a possible filtering approach, the Extended Information Filter is proposed (Rigatos, 2010a). In the Extended Information Filter the local filters do not exchange raw measurements but send to an aggregation filter their local information matrices (local inverse covariance matrices which can be also associated to Fisher Information Matrices) and their associated (Lee, 2008).…”