Combined extended FIR/Kalman filtering for indoor robot localization via triangulation

A combined unbiased finite impulse response (UFIR) and Kalman filtering algorithm is proposed for mobile robot localization via triangulation utilizing noisy measurements. We consider a mobile robot travelling on an indoor floorspace with three nodes in a view. Under the not well-known initial robot state and noise statistics, the extended Kalman filter (EKF) may produce unacceptable estimates. The iterative extended UFIR (EFIR) filter ignores the noise statistics, but requires N initial points of linear measurements which are unavailable. The combined EFIR/Kalman algorithm utilizes N first EKF estimates with approximately set initial conditions and noise statistics as linear measurements for EFIR filter. It is shown that the combined algorithm is more accurate than EKF in robot localization under the real operation conditions. Simulations are provided for piecewise and circular robot trajectories

Industrial applications of the Kalman filter:a review

International audienc

Hessian Estimation Based Adaptive and Cooperative Extremum Localization

The thesis is on Hessian estimation based adaptive and cooperative extremum localization via a single mobile sensory agent as well as a network of multiple such agents. First, we develop a continuous time adaptive extremum localization of an arbitrary quadratic function F(·) based on Hessian estimation, using the measured signal intensity via a single mobile sensory agent. A gradient based adaptive Hessian parameter estimation and extremum localization scheme is developed considering a linear parametric model of field variations. Next, we extend the proposed single agent based Hessian estimation and extremum localization scheme to consensus based cooperative distributed scheme to be implemented by a network of such sensory agents.For the networked multi-agent case, a consensus term is added to the base adaptive laws to obtain enhanced estimation cooperatively. Stability and convergence analysis of the proposed scheme is studied, establishing asymptotic convergence of the Hessian parameters and location estimates to their true values robustly, provided that the motion of agent(s) satisfies certain persistence of excitation(PE) conditions. Furthermore, we show that for a network of connected agents, the PE requirements can be distributed to the agents so that the requirement on each agent is more relaxed and feasible. Later, we design an adaptive motion control scheme for steering a mobile sensory agent in 2D toward the source of a signal field F(·) using the signal intensity the agent continuously measures at its current location. The proposed adaptive control design is based on the Hessian estimation based adaptive extremum localization. Results are displayed to verify that the proposed scheme is stable, provides asymptotic convergence of the Hessian parameter and extremum location estimates to their true values and the agent location to the source location, robustly to signal measurement noises

Mobile Robot Localization Using Biased Chirp Spread Spectrum Ranging

In this paper, we propose a method of mobile robot localization based on chirp-spread-spectrum (CSS) ranging. By using the CSS system, the distances between a mobile robot and CSS nodes fixed at known coordinates can be measured according to the time of flight of radio frequency signals. Based on the measured distances, the coordinates of a mobile robot can be calculated by the method of trilateration. To deal with measurement noise, an extended Kalman filter (EKF) can be applied to estimate the coordinate of the mobile robot. These measured distances, however, are not only noisy but also biased. Therefore, the estimated coordinates of the mobile robot represent inconsistent values. To solve the problem of bias, we define a scaling factor, which corresponds to the change of the magnitude of a measured distance vector that is due to biases. Based on the scaling factor, we develop a new biased measurement model and apply the EKF to our model for estimating the coordinates of a mobile robot. Through localization experiments, we evaluate the performance of the proposed algorithm.X115059sciescopu

Cognitive radar network design and applications

PhD ThesisIn recent years, several emerging technologies in modern radar system design are attracting the attention of radar researchers and practitioners alike, noteworthy among which are multiple-input multiple-output (MIMO), ultra wideband (UWB) and joint communication-radar technologies. This thesis, in particular focuses upon a cognitive approach to design these modern radars. In the existing literature, these technologies have been implemented on a traditional platform in which the transmitter and receiver subsystems are discrete and do not exchange vital radar scene information. Although such radar architectures benefit from these mentioned technological advances, their performance remains sub-optimal due to the lack of exchange of dynamic radar scene information between the subsystems. Consequently, such systems are not capable to adapt their operational parameters “on the fly”, which is in accordance with the dynamic radar environment. This thesis explores the research gap of evaluating cognitive mechanisms, which could enable modern radars to adapt their operational parameters like waveform, power and spectrum by continually learning about the radar scene through constant interactions with the environment and exchanging this information between the radar transmitter and receiver. The cognitive feedback between the receiver and transmitter subsystems is the facilitator of intelligence for this type of architecture. In this thesis, the cognitive architecture is fused together with modern radar systems like MIMO, UWB and joint communication-radar designs to achieve significant performance improvement in terms of target parameter extraction. Specifically, in the context of MIMO radar, a novel cognitive waveform optimization approach has been developed which facilitates enhanced target signature extraction. In terms of UWB radar system design, a novel cognitive illumination and target tracking algorithm for target parameter extraction in indoor scenarios has been developed. A cognitive system architecture and waveform design algorithm has been proposed for joint communication-radar systems. This thesis also explores the development of cognitive dynamic systems that allows the fusion of cognitive radar and cognitive radio paradigms for optimal resources allocation in wireless networks. In summary, the thesis provides a theoretical framework for implementing cognitive mechanisms in modern radar system design. Through such a novel approach, intelligent illumination strategies could be devised, which enable the adaptation of radar operational modes in accordance with the target scene variations in real time. This leads to the development of radar systems which are better aware of their surroundings and are able to quickly adapt to the target scene variations in real time.Newcastle University, Newcastle upon Tyne: University of Greenwich

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods