多传感器系统含状态约束的分布式并行卡尔曼滤波算法

基于群智能建筑系统，提出一种含有状态约束的并行式卡尔曼滤波算法。算法通过物理约束建立方程，利用相邻节点间的约束关系和投影法计算出含有状态约束的卡尔曼滤波估计值，从而达到故障诊断与数据校核的目的。算法基于的分布式结构采用传感器网络节点的形式，每个节点有自身处理系统而不需要任何中心节点或中心通信设施。因此，本文提出的算法具有完全分布性，允许在多个测量节点之间独立计算。本文详细论述算法推导过程，并通过软件仿真与硬件测试，验证了算法的并行性、准确性和稳定性。

Decentralized Parallel Kalman Filter for Multi-sensor System with State Constraints

Based on the Insect Intelligent Building platform, a decentralized Kalman filter algorithm with state constraints is presented. The algorithm establishes the equation through physical constraints, and uses the equality constraint between neighboring nodes to calculate the Kalman filter estimation value with state constraints, so as to achieve the purpose of fault diagnosis and data checking. Based on the distributed structure, the algorithm takes the form of sensor network nodes, each node having its own processing system without any central nodes or central communication facilities. Therefore, the proposed algorithm is fully distributed, allowing independent calculations between multiple measurement nodes. The paper discusses the algorithm derivation process in detail, and verifies the parallelism, accuracy and stability of the algorithm through software simulation and hardware testing.