Frobenius Norm-based Unbiased FIR Fusion Filtering for Wireless Sensor Networks
Abstract : This article presents a new approach to designing the Frobenius norm-based weighted unbiased finite impulse response (FIR) fusion filter for wireless sensor networks. The weighted Frobenius norm is employed as a cost function to design a local unbiased FIR filter. The design problem is converted into a constrained optimization problem subject to an equality constraint. The Lagrange multiplier method is used to derive the local FIR filter gain. An alternative Frobenius norm is introduced to determine weights for the local unbiased FIR filters in the design of a global fusion FIR filter. The developed FIR fusion filter is demonstrated to have higher robustness against uncertainties than Kalman filter-based methods, such as the optimal fusion Kalman filter, distributed Kalman filter, and distributed weighted Kalman filter, through a numerical example of moving-target tracking employing seven smart sensors and an experiment with temperature and humidity estimation using eight sensors.
? In order to overcome the poor observability of yaw measurement for foot-mounted inertial measurement unit (IMU), an integrated IMU+Compass scheme for self-contained pedestrian navigation is presented.
? However, it should be pointed out that although the RFID- and WiFi-based methods mentioned above are able to provide the position information in indoor environment, the accuracy of such approaches is on meter-level.
? However, it should be pointed that although the ultrasonic-based approaches are on centimeter-level, it is easy to be outage.
? In this paper, we propose a self-contained pedestrian navigation by fusing the IMU and compass measurements via UFIR filtering.
? Sufficient conditions are established for the solvability of the addressed switching topology-dependent distributed filtering design in terms of certain convex optimization problem.
? Therefore, some initiatives have been taken to address the problems of distributed filtering based on WSNs in recent years, see.
? Furthermore, the developed algorithm has been applied to estimate the unknown information distribution in a certain area of the optimal coverage control problem over sensor networks.
? There are several techniques including linear matrix inequality (LMI) approach, parameter-dependent/recursive LMI method, and Riccati difference equation method for distributed filtering or estimation problems.
• In order to provide the accurate position information of the person in indoor environment, many approaches have been proposed.
• However, it should be pointed out that although the RFID- and WiFi-based methods mentioned above are able to provide the position information in indoor environment, the accuracy of such approaches is on meter-level. In order to improve the positioning accuracy, ultrasonic-based approaches are proposed in.
• One of the famous examples is the navigation shoe proposed in, which employs the foot-mounted inertial measurement unit (IMU) to correct the error drift of the INS solution.
• In this paper, we employ a real indoor test to verify the performance of the proposed self-contained pedestrian navigation using the IMU and compass measurements via UFIR filter.
? Malicious attacks do cause filter performance degradation, whether it is a full or reduced-order filter.
? By utilizing the Markovian switched Lyapunov functional method and the LMI technique, sufficient conditions on the designed distributed estimator have been obtained to ensure the prescribed energy-to-peak performance with given filter parameters.
? In , the distributed state estimator, which was used to defend against false data injection attacks over sensor networks, has been embedded with event-triggering transmission scheme.
? Additionally, a random variable obeying the Bernoulli distribution is used to describe the phenomenon of the randomly occurring deception attacks.
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