Subhash Challa | University of Melbourne (original) (raw)
Papers by Subhash Challa
2005 7th International Conference on Information Fusion, 2005
Wireless sensor networks are deployed for the purpose of sensing and monitoring an area of intere... more Wireless sensor networks are deployed for the purpose of sensing and monitoring an area of interest. Sensor measurements in sensor networks usually suffer from both random errors (noise) and systematic errors (drift and bias). Even when the sensors are properly calibrated at the time of deployment, they develop errors in their readings leading to erroneous inferences to be made by the network. In this paper we present a novel algorithm for detecting and correcting sensor measurement errors by utilising the spatio-temporal correlation among the neighbouring sensors. The algorithm is designed for sparsely deployed wireless sensor networks. It can follow and correct both slowly and suddenly changing sensor measurements. As a result, the algorithm can adapt for under sampling the sensor measurements. Therefore, it allows for reducing the communication between sensors to maintain the calibration which leads to reducing the energy consumed from the batteries. The algorithm runs recursivel...
In this paper, an algorithm is proposed for vision-based object identification and tracking by au... more In this paper, an algorithm is proposed for vision-based object identification and tracking by autonomous vehicles. In order to estimate the speed of the tracking object, this algorithm fuses information captured by on-board sensors such as camera and inertial sensors. To formulate the tracking algorithm it is necessary to use a proper model which describes the dynamics of the tracking object. However due to complex nature of the moving object, it is necessary to have different dynamic models. Here, several simple and basic linear dynamic models are combined to approximate unpredictable, complex dynamics of the moving target. With these basic linear dynamic models, a detailed description of the three dimensional (3D) target tracking scheme using an interacting multiple model (IMM)alongwithanExtendedKalmanFilteringispresented. Thefinal state of the target is estimated as a weighted combination of the outputs from each different dynamic model. Performance of the proposed interacting m...
Determining the output of the most relevant sensor is of crucial importance when heterogeneous se... more Determining the output of the most relevant sensor is of crucial importance when heterogeneous sensors are available for measuring a given process in an environment. In this paper, we describe an IEEE 1451 TEDS (Transducer Electronic Data Sheets) compliant sensor model for heterogeneous sensor networks. The proposed model uses the relevance feed back method to understand the context of a sensor learning application. We present results of a real time implementation of heterogeneous sensor networks using distributed multi-sensing 3D Real-time Robotics Software Player/Gazebo on an autonomous mobile robot’s navigation problem. The results show that the proposed model can be utilised in the real-time scenario and can help reduce the computational cost of a system.
Network-centric warfare (NCW) and the interoperability of joint and coalition forces lie among th... more Network-centric warfare (NCW) and the interoperability of joint and coalition forces lie among the future warfighting concepts that have been identified by defence. The purpose behind the introduction of such concepts is to “link sensors, engagement systems and decision-makers into an effective and responsive whole, through shared situation awareness, clear procedures and the information connectivity needed to synchronise the actions of the defence force to meet the commander’s intent [1].” To realise the goal of shared situation awareness for NCW, it has long been acknowledged that decentralised data fusion is a key enabling technology, and to this end it has been investigated in terms of distributed target tracking and identification, and the development of distributed agents and ontologies. However, the aspects of interoperability relating to the fusion of disparate types of uncertain (local) data from joint and coalition data fusion systems for shared situation awareness do not ...
IEEE Transactions on Image Processing
Fusing multiple independent sensor measurements and human intelligence reports are essential to s... more Fusing multiple independent sensor measurements and human intelligence reports are essential to support critical decisions in a timely manner for today's situation awareness systems. The problem of great significance is associated with fusing Human Originated Information (HOI) with the information from other sources. Ordered Weighted Average (OWA) algorithm was proposed recently as a means to assimilate uncertain human originated information.
Ieee Transactions on Aerospace and Electronic Systems, Apr 1, 2004
ABSTRACT For original paper see Challa and Pulford (2001). Joint target tracking and classificati... more ABSTRACT For original paper see Challa and Pulford (2001). Joint target tracking and classification (JTC) filter was presented in the above mentioned paper. A comment is made here to point out that JTC filter has no advantage over interacting multiple model (IMM) filter from a theoretical viewpoint.
2009 12th International Conference on Information Fusion, Jul 6, 2009
Abstract Random Finite Set approach is a mathemat-ically rigorous framework for multi-target tr... more Abstract Random Finite Set approach is a mathemat-ically rigorous framework for multi-target tracking. It provides a Bayesian recursion of multi-target distribution through Finite Set calculus. But practical implementation of multi-target posterior recursion is difficult ...
Computer Vision and Image Understanding, 2008
2008 11th International Conference on Information Fusion, 2008
Recent Patents on Computer Science, 2010
... the outliers in the scene contribute false measurements for estimation, they introduce a ster... more ... the outliers in the scene contribute false measurements for estimation, they introduce a stereovision-based method ... Thus several methods must be integrated on autonomous vehicles. ... The cameras are mounted on an autonomous vehicle moving in the environment as shown in ...
Isspa 96 Fourth International Symposium on Signal Processing and Its Applications Proceedings Vols 1 and 2, 1996
The solution of the Fokker-Planck-Kolmogorov (FPK) forward diffusion equation in conjunction with... more The solution of the Fokker-Planck-Kolmogorov (FPK) forward diffusion equation in conjunction with Bayes’ conditional density lemma provides optimal (minimum variance) state estimates of any general stochastic dynamic system (SDS). It has been well documented in non-linear filtering literature that the analytical solution for the FPK equation is extremely difficult to obtain except in a few special cases. In this paper we propose the use of numerical solution of FPK to obtain the optimal state estimates of a non-linear dynamic system. The proposed method provides the conditional densities &om which the conditional means (the optimal state estimates) can be easily evaluated. The estimated conditional densities clearly violate the assump tions of Gaussianity implicitly required by Extended Kalman Filter (EKF) based approaches. The performance of the proposed method is compared with that of the EKF. Monte-Carlo simulation results are provided to show the superior performance of the density evolution method.
Spie Proceedings Series, 2003
2005 7th International Conference on Information Fusion, 2005
Wireless sensor networks are deployed for the purpose of sensing and monitoring an area of intere... more Wireless sensor networks are deployed for the purpose of sensing and monitoring an area of interest. Sensor measurements in sensor networks usually suffer from both random errors (noise) and systematic errors (drift and bias). Even when the sensors are properly calibrated at the time of deployment, they develop errors in their readings leading to erroneous inferences to be made by the network. In this paper we present a novel algorithm for detecting and correcting sensor measurement errors by utilising the spatio-temporal correlation among the neighbouring sensors. The algorithm is designed for sparsely deployed wireless sensor networks. It can follow and correct both slowly and suddenly changing sensor measurements. As a result, the algorithm can adapt for under sampling the sensor measurements. Therefore, it allows for reducing the communication between sensors to maintain the calibration which leads to reducing the energy consumed from the batteries. The algorithm runs recursivel...
In this paper, an algorithm is proposed for vision-based object identification and tracking by au... more In this paper, an algorithm is proposed for vision-based object identification and tracking by autonomous vehicles. In order to estimate the speed of the tracking object, this algorithm fuses information captured by on-board sensors such as camera and inertial sensors. To formulate the tracking algorithm it is necessary to use a proper model which describes the dynamics of the tracking object. However due to complex nature of the moving object, it is necessary to have different dynamic models. Here, several simple and basic linear dynamic models are combined to approximate unpredictable, complex dynamics of the moving target. With these basic linear dynamic models, a detailed description of the three dimensional (3D) target tracking scheme using an interacting multiple model (IMM)alongwithanExtendedKalmanFilteringispresented. Thefinal state of the target is estimated as a weighted combination of the outputs from each different dynamic model. Performance of the proposed interacting m...
Determining the output of the most relevant sensor is of crucial importance when heterogeneous se... more Determining the output of the most relevant sensor is of crucial importance when heterogeneous sensors are available for measuring a given process in an environment. In this paper, we describe an IEEE 1451 TEDS (Transducer Electronic Data Sheets) compliant sensor model for heterogeneous sensor networks. The proposed model uses the relevance feed back method to understand the context of a sensor learning application. We present results of a real time implementation of heterogeneous sensor networks using distributed multi-sensing 3D Real-time Robotics Software Player/Gazebo on an autonomous mobile robot’s navigation problem. The results show that the proposed model can be utilised in the real-time scenario and can help reduce the computational cost of a system.
Network-centric warfare (NCW) and the interoperability of joint and coalition forces lie among th... more Network-centric warfare (NCW) and the interoperability of joint and coalition forces lie among the future warfighting concepts that have been identified by defence. The purpose behind the introduction of such concepts is to “link sensors, engagement systems and decision-makers into an effective and responsive whole, through shared situation awareness, clear procedures and the information connectivity needed to synchronise the actions of the defence force to meet the commander’s intent [1].” To realise the goal of shared situation awareness for NCW, it has long been acknowledged that decentralised data fusion is a key enabling technology, and to this end it has been investigated in terms of distributed target tracking and identification, and the development of distributed agents and ontologies. However, the aspects of interoperability relating to the fusion of disparate types of uncertain (local) data from joint and coalition data fusion systems for shared situation awareness do not ...
IEEE Transactions on Image Processing
Fusing multiple independent sensor measurements and human intelligence reports are essential to s... more Fusing multiple independent sensor measurements and human intelligence reports are essential to support critical decisions in a timely manner for today's situation awareness systems. The problem of great significance is associated with fusing Human Originated Information (HOI) with the information from other sources. Ordered Weighted Average (OWA) algorithm was proposed recently as a means to assimilate uncertain human originated information.
Ieee Transactions on Aerospace and Electronic Systems, Apr 1, 2004
ABSTRACT For original paper see Challa and Pulford (2001). Joint target tracking and classificati... more ABSTRACT For original paper see Challa and Pulford (2001). Joint target tracking and classification (JTC) filter was presented in the above mentioned paper. A comment is made here to point out that JTC filter has no advantage over interacting multiple model (IMM) filter from a theoretical viewpoint.
2009 12th International Conference on Information Fusion, Jul 6, 2009
Abstract Random Finite Set approach is a mathemat-ically rigorous framework for multi-target tr... more Abstract Random Finite Set approach is a mathemat-ically rigorous framework for multi-target tracking. It provides a Bayesian recursion of multi-target distribution through Finite Set calculus. But practical implementation of multi-target posterior recursion is difficult ...
Computer Vision and Image Understanding, 2008
2008 11th International Conference on Information Fusion, 2008
Recent Patents on Computer Science, 2010
... the outliers in the scene contribute false measurements for estimation, they introduce a ster... more ... the outliers in the scene contribute false measurements for estimation, they introduce a stereovision-based method ... Thus several methods must be integrated on autonomous vehicles. ... The cameras are mounted on an autonomous vehicle moving in the environment as shown in ...
Isspa 96 Fourth International Symposium on Signal Processing and Its Applications Proceedings Vols 1 and 2, 1996
The solution of the Fokker-Planck-Kolmogorov (FPK) forward diffusion equation in conjunction with... more The solution of the Fokker-Planck-Kolmogorov (FPK) forward diffusion equation in conjunction with Bayes’ conditional density lemma provides optimal (minimum variance) state estimates of any general stochastic dynamic system (SDS). It has been well documented in non-linear filtering literature that the analytical solution for the FPK equation is extremely difficult to obtain except in a few special cases. In this paper we propose the use of numerical solution of FPK to obtain the optimal state estimates of a non-linear dynamic system. The proposed method provides the conditional densities &om which the conditional means (the optimal state estimates) can be easily evaluated. The estimated conditional densities clearly violate the assump tions of Gaussianity implicitly required by Extended Kalman Filter (EKF) based approaches. The performance of the proposed method is compared with that of the EKF. Monte-Carlo simulation results are provided to show the superior performance of the density evolution method.
Spie Proceedings Series, 2003