title: Adaptive sensor placement and boundary estimation for monitoring mass objects.
abstract: Sensor networks are widely used in monitoring and tracking a large number of objects. Without prior knowledge on the dynamics of object distribution, their density estimation could be learned in an adaptive manner to support effective sensor placement. After sensors observe the "current" locations of objects, the estimates of object distribution are updated with these new observations through a recursive distributed expectation-maximization algorithm. Based on the real-time estimates of object distribution, an adaptive sensor placement algorithm could be designed to achieve stable and high accuracy in tracking mass objects. This paper constructs a Gaussian mixture model to characterize the mixture distribution of object locations and proposes a novel methodology to adaptively update sensor placement. Our simulation results demonstrate the effectiveness of the proposed algorithm for adaptive sensor placement and boundary estimation of mass objects.
authors.affiliation: Countrywide Securities Corporation of Countrywide Financial, Calabasas, CA 91302, USA. zg2@njit.edu
authors.forename: Zhen
authors.initials: Z
authors.lastname: Guo
authors.forename: MengChu
authors.initials: M
authors.lastname: Zhou
authors.forename: Guofei
authors.initials: G
authors.lastname: Jiang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004784:Environmental Monitoring / Q000295:instrumentation* / Q000379:methods*; D007089:Image Enhancement / Q000295:instrumentation / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000295:instrumentation / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014159:Transducers