Sensor Control for Multi-Object Tracking Using Labeled Multi-Bernoulli Filter

The recently developed labeled multi-Bernoulli (LMB) filter uses better approximations in its update step, compared to the unlabeled multi-Bernoulli filters, and more importantly, it provides us with not only the estimates for the number of targets and their states, but also with labels for existing tracks. This paper presents a novel sensor-control method to be used for optimal multi-target tracking within the LMB filter. The proposed method uses a task-driven cost function in which both the state estimation errors and cardinality estimation errors are taken into consideration. Simulation results demonstrate that the proposed method can successfully guide a mobile sensor in a challenging multi-target tracking scenario

Multi-Bernoulli Sensor-Control via Minimization of Expected Estimation Errors

This paper presents a sensor-control method for choosing the best next state of the sensor(s), that provide(s) accurate estimation results in a multi-target tracking application. The proposed solution is formulated for a multi-Bernoulli filter and works via minimization of a new estimation error-based cost function. Simulation results demonstrate that the proposed method can outperform the state-of-the-art methods in terms of computation time and robustness to clutter while delivering similar accuracy

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Feature Space Modeling for Accurate and Efficient Learning From Non-Stationary Data

A non-stationary dataset is one whose statistical properties such as the mean, variance, correlation, probability distribution, etc. change over a specific interval of time. On the contrary, a stationary dataset is one whose statistical properties remain constant over time. Apart from the volatile statistical properties, non-stationary data poses other challenges such as time and memory management due to the limitation of computational resources mostly caused by the recent advancements in data collection technologies which generate a variety of data at an alarming pace and volume. Additionally, when the collected data is complex, managing data complexity, emerging from its dimensionality and heterogeneity, can pose another challenge for effective computational learning. The problem is to enable accurate and efficient learning from non-stationary data in a continuous fashion over time while facing and managing the critical challenges of time, memory, concept change, and complexity simultaneously. Feature space modeling is one of the most effective solutions to address this problem. For non-stationary data, selecting relevant features is even more critical than stationary data due to the reduction of feature dimension which can ensure the best use a computational resource to produce higher accuracy and efficiency by data mining algorithms. In this dissertation, we investigated a variety of feature space modeling techniques to improve the overall performance of data mining algorithms. In particular, we built Relief based feature sub selection method in combination with data complexity iv analysis to improve the classification performance using ovarian cancer image data collected in a non-stationary batch mode. We also collected time series health sensor data in a streaming environment and deployed feature space transformation using Singular Value Decomposition (SVD). This led to reduced dimensionality of feature space resulting in better accuracy and efficiency produced by Density Ration Estimation Method in identifying potential change points in data over time. We have also built an unsupervised feature space modeling using matrix factorization and Lasso Regression which was successfully deployed in conjugate with Relative Density Ratio Estimation to address the botnet attacks in a non-stationary environment. Relief based feature model improved 16% accuracy of Fuzzy Forest classifier. For change detection framework, we observed 9% improvement in accuracy for PCA feature transformation. Due to the unsupervised feature selection model, for 2% and 5% malicious traffic ratio, the proposed botnet detection framework exhibited average 20% better accuracy than One Class Support Vector Machine (OSVM) and average 25% better accuracy than Autoencoder. All these results successfully demonstrate the effectives of these feature space models. The fundamental theme that repeats itself in this dissertation is about modeling efficient feature space to improve both accuracy and efficiency of selected data mining models. Every contribution in this dissertation has been subsequently and successfully employed to capitalize on those advantages to solve real-world problems. Our work bridges the concepts from multiple disciplines ineffective and surprising ways, leading to new insights, new frameworks, and ultimately to a cross-production of diverse fields like mathematics, statistics, and data mining

Particle filter approximations for general open loop and open loop feedback sensor management

Sensor management is a stochastic control problem where the control mechanism is directed at the generation of observations. Typically, sensor management attempts to optimize a certain statistic derived from the posterior distribution of the state, such as covariance or entropy. However, these statistics often depend on future measurements which are not available at the moment the control decision is taken, making it necessary to consider their expectation over the entire measurement space. Though the idea of computing such expectations using a particle filter is not new, so far it has been applied only to specific sensor management problems and criterions. In this memorandum, for a considerably broad class of problems, we explicitly show how particle filters can be used to approximate general sensor management criterions in the open loop and open loop feedback cases. As examples, we apply these approximations to selected sensor management criterions. As an additional contribution of this memorandum, we show that every performance metric can be used to define a corresponding estimate and a corresponding task-driven sensor management criterion, and both of them can be approximated using particle filters. This is used to propose an approximate sensor management scheme based on the OSPA metric for multi-target tracking, which is included among our examples

Trust reality-mining: evidencing the role of friendship for trust diffusion

Value sensitive design is driven by the motivation of making social and moral values central to the development of ICT systems. Among the most challenging concerns when imparting shared values like accountability, transparency, liberty, fairness and trust into information technology are reliable and comprehensive formal and computational models of those values. This paper, educated by trust theories and models from cognitive science, social sciences and artificial intelligence, proposes a novel stochastic computational model of trust, encapsulating abstractions of human cognitive capabilities and empirically evidenced social interaction patterns. Qualitative and quantitative features of trust are identified, upon which our formal model is phrased. Reality mining methods are used to validate the model based on a real life community dataset. We analyze the time-varying dynamics of the interaction and communication patterns of the community, consider varying types of relationships as well as their symmetry. Social network data analysis shows that our model better fits the evolved friendships compared to a well designed synthetic trust model, which is used as the baseline.</p