Statistical Signal and Data Processing

In many engineering applications, physical systems are controlled by a set of external inputs and their behavior is monitored by the system states. The system states may not be measured or observed directly. However, the system states and the external inputs influence the measurements of the system. Hence, in theory, the unknown system states can be characterized by the given inputs and the observed measurements under the assumptions that the system fulfills the observability and prior knowledge on initial conditions is known. In general, the measurements are corrupted by noise thus leading to the problem of estimating the state of the system. The problem of estimating the unknown states of a physical system based on the noisy measurements has been a focus of research for several decades. However, modern engineering applications such as wireless channel estimation, visual object tracking, radar, appliance detection and so on, equip current researchers with an ever-increasing scope to work on the estimation problem.

In our work, the Bayesian estimation methodologies are used to address the issues of robustness and efficient implementations in multi-sensor linear/non-linear state estimation algorithms. They can address the following constraints: lack of accurate knowledge about initial conditions of the system; lack of accurate process and measurement model; lack of statistical knowledge of process and measurement noise; ad hoc sensor networks with limited energy reservoirs.

There are many areas of application of statistical signal processing we are working on:

Object tracking in visual sensor networks: Object tracking is an extensively studied topic in visual sensor networks (VSN). A VSN is a network composed of smart cameras; VSN capture, process, and analyze the image data locally and exchange extracted information with each other. The task of the cameras in the VSN is to monitor the given environment and to identify and track an object. The main applications of VSN are indoor and/or outdoor surveillance, e.g., airports, forests, deserts, inaccessible locations, and natural environments.
Battery internal state estimation: In the modern age, portable and uninterrupted energy sources have become an integral part of human life. The usage of batteries as mobile energy sources has many potential applications such as smart phones, notebooks and electric vehicles. Battery management plays a critical role in achieving the ever-increasing demands of battery powered applications. Battery management systems (BMS) use many internal parameters such as the state-of-charge (SoC), the state-of-health (SoH), or the internal impedance to determine the health or the power delivery capability of a battery. In general, these quantities cannot be measured directly. Instead, they can only be estimated based on noisy measurements.
Appliance detection in energy distribution grids: The integration of smart meters into the current power grid improves it to be more efficient, reliable, and energy aware. The modern smart meters keep the consumers updated with the energy consumption of their households with a fine-grained and real-time load demand information. Thus, enabling the consumer to know, forecast and optimize his/her overall energy consumption. This is often referred as Appliance Load Disaggregation (ALD) or Appliance Load Monitoring (ALM).
Localization and navigation exploiting radio signals: radio signals can be exploited to determine the position of transmitting nodes and implement navigation solutions.

Selected Publications


V.P Bhuvana and A.M. Tonello, “Distributed Object Tracking Based on Information Weighted UAV Selection with Priory Objects,” to appear in 25th Proc. of European Signal Processing Conference, 2017

V.P Bhuvana, M. Schranz, C. Regazzoni, B. Rinner, A. Tonello, and M. Huemer, “Multi-Camera Object Tracking Using Surprisal Observations in Visual Sensor Networks,” Eurasip Journal on Advanced Signal Processing, 2016.

D. Egarter, V. P. Bhuvana, and W. Elmenreich, “PALDi: Online Load Disaggregation via Particle Filtering,” IEEE Transactions on Instrumentation and Measurement, 2015.

V.P Bhuvana, M. Huemer, and A. Tonello, “Battery Internal State Estimation Using a Mixed Kalman Cubature Filter,”  In Proc. Smart Grid Communications, 2015.

V.P Bhuvana, M. Huemer, and C. Regazzoni, “Distributed Camera Tracking Based on Square Root Cubature H-infinity Information Filter,” In Proc. International Conference on InformatioFusion, 2014.

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