Inequality Constrained State-Space Models
标准卡尔曼滤波无法处理状态变量的不等式约束，本文提出一种Rao-Blackwellised粒子滤波器，利用最优重要性函数进行前向滤波和似然函数评估，蒙特卡洛实验证明其性能优异。
The standard Kalman filter cannot handle inequality constraints imposed on the state variables, as state truncation induces a non-linear and non-Gaussian model. We propose a Rao-Blackwellised particle filter with the optimal importance function for forward filtering and the likelihood function evaluation. The particle filter effectively enforces the state constraints when the Kalman filter violates them. Monte Carlo experiments demonstrate excellent performance of the proposed particle filter with Rao-Blackwellisation, in which the Gaussian linear sub-structure is exploited at both the cross-sectional and temporal levels.