Bibliographic Review on Distributed Kalman Filtering

In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area

Improved Distributed Estimation Method for Environmental\ud time-variant Physical variables in Static Sensor Networks

In this paper, an improved distributed estimation scheme for static sensor networks is developed. The scheme is developed for environmental time-variant physical variables. The main contribution of this work is that the algorithm in [1]-[3] has been extended, and a filter has been designed with weights, such that the variance of the estimation errors is minimized, thereby improving the filter design considerably\ud and characterizing the performance limit of the filter, and thereby tracking a time-varying signal. Moreover, certain parameter optimization is alleviated with the application of a particular finite impulse response (FIR) filter. Simulation results are showing the effectiveness of the developed estimation algorithm

Localization Of Sensors In Presence Of Fading And Mobility

The objective of this dissertation is to estimate the location of a sensor through analysis of signal strengths of messages received from a collection of mobile anchors. In particular, a sensor node determines its location from distance measurements to mobile anchors of known locations. We take into account the uncertainty and fluctuation of the RSS as a result of fading and take into account the decay of the RSS which is proportional to the transmitter-receiver distance power raised to the PLE. The objective is to characterize the channel in order to derive accurate distance estimates from RSS measurements and then utilize the distance estimates in locating the sensors. To characterize the channel, two techniques are presented for the mobile anchors to periodically estimate the channel\u27s PLE and fading parameter. Both techniques estimate the PLE by solving an equation via successive approximations. The formula in the first is stated directly from MLE analysis whereas in the second is derived from a simple probability analysis. Then two distance estimates are proposed, one based on a derived formula and the other based on the MLE analysis. Then a location technique is proposed where two anchors are sufficient to uniquely locate a sensor. That is, the sensor narrows down its possible locations to two when collects RSS measurements transmitted by a mobile anchor, then uniquely determines its location when given a distance to the second anchor. Analysis shows the PLE has no effect on the accuracy of the channel characterization, the normalized error in the distance estimation is invariant to the estimated distance, and accurate location estimates can be achieved from a moderate sample of RSS measurements