Abstract
We propose centralized and distributed fusion algorithms for estimation of nonlinear cost function (NCF) in multisensory mixed continuous-discrete stochastic systems. The NCF represents a nonlinear multivariate functional of state variables. For polynomial NCFs, we propose a closed-form estimation procedure based on recursive formulas for high-order moments for a multivariate normal distribution. In general case, the unscented transformation is used for calculation of nonlinear estimates of a cost functions. To fuse local state estimates, the mixed differential difference equations for error cross-covariance between local estimates are derived. The subsequent application of the proposed fusion estimators for a multisensory environment demonstrates their effectiveness.
Highlights
Multisensor data fusion is typically motivated by reducing the overall redundant information obtained from different sensors, increasing information gain by using multiple sensors, increasing the accuracy, and decreasing the uncertainty of the system
We propose centralized and distributed fusion algorithms for estimation of nonlinear cost function (NCF) in multisensory mixed continuous-discrete stochastic systems
For polynomial NCFs, we propose a closed-form estimation procedure based on recursive formulas for high-order moments for a multivariate normal distribution
Summary
Multisensor data fusion is typically motivated by reducing the overall redundant information obtained from different sensors, increasing information gain by using multiple sensors, increasing the accuracy, and decreasing the uncertainty of the system. Multisensor data fusion can give benefits such as extended temporal and spatial coverage, reduced ambiguity, enhanced spatial resolution, and increased dimensionality of the measurement space This process has attracted growing interest for potential applications in many fields including guidance, robotics, aerospace, target tracking, signal processing, and control [1,2,3]. In this paper, the estimation fusion problem of NCFs of state variables is considered for mixed continuousdiscrete linear systems under a multisensory environment. The continuous-discrete approach allows system to avoid discretization by propagating the estimate and error covariance between measurements in continuous time using an integration routine such as Runge-Kutta This approach yields the optimal or suboptimal estimate continuously at all times, including times between the data arrival instants.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
