Sensor models and multisensor integration
International Journal of Robotics Research - Special Issue on Sensor Data Fusion
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Neural Computation and Self-Organizing Maps; An Introduction
Neural Computation and Self-Organizing Maps; An Introduction
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Traderbots: a new paradigm for robust and efficient multirobot coordination in dynamic environments
Traderbots: a new paradigm for robust and efficient multirobot coordination in dynamic environments
Cooperative Observation of Multiple Moving Targets: an algorithm and its formalization
International Journal of Robotics Research
An auction-based approach to complex task allocation for multirobot teams
An auction-based approach to complex task allocation for multirobot teams
An heterogeneous, endogenous and coevolutionary GP-based financial market
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
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The active surveillance of public and private sites is increasingly becoming a very important and critical issue. It is therefore, imperative to develop mobile surveillance systems to protect these sites. Modern surveillance systems encompass spatially distributed mobile and static sensors in order to provide effective monitoring of persistent and transient objects. The realization of the potential of mobile surveillance requires the solution of different challenging problems such as task allocation, mobile sensor deployment, multisensor management, and cooperative object tracking. This paper proposes a market-based framework that can be used to handle different problems of mobile surveillance systems. Task allocation and cooperative target-tracking are studied using the proposed framework as two challenging problems of mobile surveillance systems. These challenges are addressed individually and collectively.