基于卡尔曼滤波的交通流预测 带数据

%卡尔曼滤波原理 % x = Ax + Bu + w  meaning the state vector x evolves during one time %                  step by premultiplying by the "state transition %                  matrix" A. There is optionally (if nonzero) an input %                  vector u which affects the state linearly, and this %                  linear effect on the state is represented by %                  premultiplying by the "input matrix" B. There is also %                  gaussian process noise w. % z = Hx + v       meaning the observation vector z is a linear function %                  of

基于卡尔曼滤波的交通流预测
KFData2.xls
TravelTimePre2.m