A Kriging-based Interacting Particle Kalman Filter for the simultaneous estimation of temperature and emissivity in Infra-Red imaging

International audienceTemperature estimation through infrared thermography is facing the lack of knowledge of the observed material's emissivity. The derivation of the physical equations lead to an ill-posed problem. A new Kriged Interacting Particle Kalman Filter is proposed. A state space model relates the measurements to the temperature and the Kalman filter equations yield a filter tracking the temperature over time. Moreover, a particle filter associated to Kriging prediction is interacting with a bank of Kalman filters to estimate the time-varying parameters of the system. The efficiency of the algorithm is tested on a simulated sequence of infrared thermal images

Étude comparative de quatre méthodes statistiques pour l’estimation conjointe de l’émissivité et de la température par thermographie infrarouge multispectrale

International audienceThis study addresses the simultaneous estimation of the surface temperature and emissivity of objects observed through in-situ infrared thermography. The conversion of the radiative flux to temperature is facing the lack of knowledge of the radiative properties of the scene and particularly the emissivity. The irradiance received at cameras’ sensors from a virtual target made of four known materials is simulated. Then, four statistical methods that estimate simultaneously the emissivity and the temperature are compared and their sensitivity is evaluated.Ces travaux portent sur l’estimation conjointe de la température de surface et de l’émissivité d’objets observés par thermographie infrarouge in-situ. La conversion du flux radiatif en température se heurte au manque de connaissance des propriétés radiatives de la scène réelle et en particulier de l’émissivité. L’éclairement reçu par la caméra depuis une cible virtuelle composée de quatre matériaux connus est simulé. Ensuite, une comparaison de quatre méthodes statistiques pour estimer simultanément l’émissivité et la température et ainsi évaluer leur sensibilité est proposée

Generating Water Quality Maps of Klang River Based on Geographic Information System (GIS) and Water Quality Index (WQI)

Water quality index (WQI) and Geographic Information Systems (GIS) play a critical role in managing and modelling a variety of water resource issues, including urban drainage, point and non-point source pollution. Historically, the evaluation of water quality has been a domain reserved for experts, necessitating laborious and time-consuming in situ sampling and laboratory analysis. However, the integration of WQI and GIS has democratized this information, making it accessible to non-experts, thereby enhancing the comprehension of Klang River's water quality. The objective is to employ WQI and GIS to create comprehensive water quality maps. While WQI offers a straightforward numerical evaluation, the incorporation of graphical data provides a nuanced understanding of river pollution. Therefore, hourly WQI data observed at every week in 2 months from October to November 2021 over four stations (Kampung Medan, Kampung Lombong, Taman Pengkalan Batu and Jeti Sungai Udang) in Malaysia was acquired from the Selangor Maritime Gateway (SMG) website and the Malaysian National Water Quality Standard (NWQS). Adopting Inverse Distance Weighting (IDW) interpolation method, WQI parameters at unsampled locations were estimated based on values of nearby sampled points. Database was built to depict the water quality of the Klang River, particularly during the two-month monitoring. Mapping provides a clear indication of the river's water quality. The WQI mapping outcome fall between class II and class IV. The findings indicate varying water quality classes along the Klang River, revealing potential pollution sources in industrial and development areas. It was concluded from the study that the water pollution may be due to its proximity to industrial and development regions

Spatio-temporal field estimation using kriged kalman filter (KKF) with sparsity-enforcing sensor placement

We propose a sensor placement method for spatio-temporal field estimation based on a kriged Kalman filter (KKF) using a network of static or mobile sensors. The developed framework dynamically designs the optimal constellation to place the sensors. We combine the estimation error (for the stationary as well as non-stationary component of the field) minimization problem with a sparsity-enforcing penalty to design the optimal sensor constellation in an economic manner. The developed sensor placement method can be directly used for a general class of covariance matrices (ill-conditioned or well-conditioned) modelling the spatial variability of the stationary component of the field, which acts as a correlated observation noise, while estimating the non-stationary component of the field. Finally, a KKF estimator is used to estimate the field using the measurements from the selected sensing locations. Numerical results are provided to exhibit the feasibility of the proposed dynamic sensor placement followed by the KKF estimation method.Circuits and System

Spatio-Temporal Environment Monitoring Leveraging Sparsity

Reliable prediction and monitoring of dynamically changing environments are essential for a safer and healthier society. Sensor networks play a significant role in fulfilling this task. The two fundamental aspects of environmental sensor networks (ESNs) include the need for accuracy as well as low-complexity and energy efficient sensing modalities. One of the wonted challenges of ESNs is high resolution environment monitoring in the presence of sensing overheads (such as number of sensors, battery life, maintenance). Limiting the number of sensing resources yet still guarantee a desired resolution of the unknown environmental field necessitates resource-efficient sensing framework. On the other hand, the physical behavior of many environmental fields can be predicted using statistical models. Cognizance of the physical properties of environmental fields motivates opportunistic sensor placement to dynamically monitor the environment. In this thesis, we present signal processing methods for resource-efficient environment monitoring exploiting the physical properties of environmental fields. We mainly focus on a general class of environmental fields that obey standard physical properties (such as diffusion, advection) responsible for the spatio-temporal evolution of the field. We first discuss different mathematical representations to link the sensor measurements with the unknown field intensities. Statistical characterizations of different physical properties of environmental fields such as space-time correlation and the dynamics of field propagation are also discussed. A comprehensive environment monitoring framework is presented that encompasses sensor management, measurement accumulation, and field estimation. We propose a spatio-temporal sensor management method which can be applied for stationary as well as non-stationary environmental fields. We formulate a unified optimization framework that provides the number and the most informative sensing locations to deploy sensors guaranteeing a desired estimation accuracy in terms of the mean square error (MSE). The main objective is to implement “sparse-sensing” in an environment monitoring perspective while also achieving a prescribed accuracy. We also propose different strategies to solve the proposed optimization problem for both online and offline applications. We present a practical example of environment monitoring, i.e., dynamic rainfall monitoring using rain-induced attenuation measurements from commercial microwave links. We describe different methods to incorporate some physical properties of rainfall (such as the physics behind the rainfall propagation, spatial effects such as sparsity, correlation etc.) in the dynamic monitoring setup. We also compare the estimation performance of the developed technique with standard estimators such as an extended Kalman filter (EKF). We extend the proposed sparsity-enforcing spatio-temporal sensor management method for a broader class of environmental fields consisting of a combination of both stationary and non-stationary components. We develop an algorithm for sensor placement followed by field estimation using a kriged Kalman filter (KKF), which is used for the estimation of the aforementioned type of field. We also consider the scenario, where the prior physical knowledge regarding the environmental field is either unavailable or inaccurate. In these circumstances, we discuss some methods to estimate the underlying dynamics of the field, i.e., the state/process model using the observed measurements. While estimating the process model, we consider both the scenario, where the true value/ground truth of the field is known as well as the scenario where it is unknown.Circuits and System