work/mixpp/bdm/estim/libKF.h

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00001 
00013 #ifndef KF_H
00014 #define KF_H
00015 
00016 #include <itpp/itbase.h>
00017 #include "../stat/libFN.h"
00018 #include "../stat/libEF.h"
00019 
00020 
00021 using namespace itpp;
00022 
00027 class KalmanFull {
00028         int dimx, dimy, dimu;
00029         mat A, B, C, D, R, Q;
00030 
00031         //cache
00032         mat _Pp, _Ry, _iRy, _K;
00033 public:
00034         //posterior
00036         vec mu;
00038         mat P;
00039 
00040 public:
00042         KalmanFull ( mat A, mat B, mat C, mat D, mat R, mat Q, mat P0, vec mu0 );
00044         void bayes ( const vec &dt );
00046         friend std::ostream &operator<< ( std::ostream &os, const KalmanFull &kf );
00047 
00048 };
00049 
00050 
00058 template<class sq_T>
00059 
00060 class Kalman : public BM {
00061 protected:
00063         RV rvy;
00065         RV rvu;
00067         int dimx;
00069         int dimy;
00071         int dimu;
00073         mat A;
00075         mat B; 
00077         mat C;
00079         mat D;
00081         sq_T Q;
00083         sq_T R;
00084 
00086         enorm<sq_T> est;
00088         enorm<sq_T> fy;
00089 
00091         mat _K;
00093         vec* _yp;
00095         sq_T* _Ry;
00097         sq_T* _iRy;
00099         vec* _mu;
00101         sq_T* _P;
00103         sq_T* _iP;
00104 
00105 public:
00107         Kalman ( RV rvx0, RV rvy0, RV rvu0 );
00109         Kalman ( const Kalman<sq_T> &K0 );
00111         void set_parameters ( const mat &A0,const mat &B0,const mat &C0,const mat &D0,const sq_T &R0,const sq_T &Q0 );
00113         void set_est ( const vec &mu0, const sq_T &P0 ) {
00114                 sq_T pom(dimy);
00115                 est.set_parameters ( mu0,P0 );
00116                 P0.mult_sym(C,pom);
00117                 fy.set_parameters ( C*mu0, pom );
00118         };
00119 
00121         void bayes ( const vec &dt );
00123         epdf& _epdf() {return est;}
00124 };
00125 
00131 template<class sq_T>
00132 
00133 class EKF : public Kalman<ldmat> {
00135         diffbifn* pfxu;
00137         diffbifn* phxu;
00138 public:
00140         EKF ( RV rvx, RV rvy, RV rvu );
00142         void set_parameters ( diffbifn* pfxu, diffbifn* phxu, const sq_T Q0, const sq_T R0 );
00144         void bayes ( const vec &dt );
00145 };
00146 
00151 class KFcondQR : public Kalman<ldmat>, public BMcond {
00152 //protected:
00153 public:
00155         KFcondQR ( RV rvx, RV rvy, RV rvu, RV rvRQ ) : Kalman<ldmat> ( rvx, rvy,rvu ),BMcond ( rvRQ ) {};
00156 
00157         void condition ( const vec &RQ );
00158 };
00159 
00164 class KFcondR : public Kalman<ldmat>, public BMcond {
00165 //protected:
00166 public:
00168         KFcondR ( RV rvx, RV rvy, RV rvu, RV rvR ) : Kalman<ldmat> ( rvx, rvy,rvu ),BMcond ( rvR ) {};
00169 
00170         void condition ( const vec &R );
00171 };
00172 
00174 
00175 template<class sq_T>
00176 Kalman<sq_T>::Kalman ( const Kalman<sq_T> &K0 ) : BM ( K0.rv ),rvy ( K0.rvy ),rvu ( K0.rvu ),
00177                 dimx ( rv.count() ), dimy ( rvy.count() ),dimu ( rvu.count() ),
00178                 A ( dimx,dimx ), B ( dimx,dimu ), C ( dimy,dimx ), D ( dimy,dimu ),est ( rv ), fy ( rvy ) {
00179 
00180         this->set_parameters ( K0.A, K0.B, K0.C, K0.D, K0.R, K0.Q );
00181 
00182 //establish pointers
00183         _mu = est._mu();
00184         est._R ( _P,_iP );
00185 
00186 //fy
00187         _yp = fy._mu();
00188         fy._R ( _Ry,_iRy );
00189 
00190 //reset copy values in pointers
00191         *_mu = *K0._mu;
00192         *_P = *K0._P;
00193         *_iP = *K0._iP;
00194         *_yp = *K0._yp;
00195         *_iRy = *K0._iRy;
00196         *_Ry = *K0._Ry;
00197 
00198 }
00199 
00200 template<class sq_T>
00201 Kalman<sq_T>::Kalman ( RV rvx, RV rvy0, RV rvu0 ) : BM ( rvx ),rvy ( rvy0 ),rvu ( rvu0 ),
00202                 dimx ( rvx.count() ), dimy ( rvy.count() ),dimu ( rvu.count() ),
00203                 A ( dimx,dimx ), B ( dimx,dimu ), C ( dimy,dimx ), D ( dimy,dimu ),
00204                 Q(dimx), R (dimy),
00205                 est ( rvx ), fy ( rvy ) {
00206 //assign cache
00207 //est
00208         _mu = est._mu();
00209         est._R ( _P,_iP );
00210 
00211 //fy
00212         _yp = fy._mu();
00213         fy._R ( _Ry,_iRy );
00214 };
00215 
00216 template<class sq_T>
00217 void Kalman<sq_T>::set_parameters ( const mat &A0,const  mat &B0, const mat &C0, const mat &D0, const sq_T &R0, const sq_T &Q0 ) {
00218         it_assert_debug ( A0.cols() ==dimx, "Kalman: A is not square" );
00219         it_assert_debug ( B0.rows() ==dimx, "Kalman: B is not compatible" );
00220         it_assert_debug ( C0.cols() ==dimx, "Kalman: C is not square" );
00221         it_assert_debug ( ( D0.rows() ==dimy ) || ( D0.cols() ==dimu ), "Kalman: D is not compatible" );
00222         it_assert_debug ( ( R0.cols() ==dimy ) || ( R0.rows() ==dimy ), "Kalman: R is not compatible" );
00223         it_assert_debug ( ( Q0.cols() ==dimx ) || ( Q0.rows() ==dimx ), "Kalman: Q is not compatible" );
00224 
00225         A = A0;
00226         B = B0;
00227         C = C0;
00228         D = D0;
00229         R = R0;
00230         Q = Q0;
00231 }
00232 
00233 template<class sq_T>
00234 void Kalman<sq_T>::bayes ( const vec &dt ) {
00235         it_assert_debug ( dt.length() == ( dimy+dimu ),"KalmanFull::bayes wrong size of dt" );
00236 
00237         vec u = dt.get ( dimy,dimy+dimu-1 );
00238         vec y = dt.get ( 0,dimy-1 );
00239         //Time update
00240         *_mu = A* ( *_mu ) + B*u;
00241         //P  = A*P*A.transpose() + Q; in sq_T
00242         _P->mult_sym ( A );
00243         ( *_P ) +=Q;
00244 
00245         //Data update
00246         //_Ry = C*P*C.transpose() + R; in sq_T
00247         _P->mult_sym ( C, *_Ry );
00248         ( *_Ry ) +=R;
00249 
00250         mat Pfull = _P->to_mat();
00251 
00252         _Ry->inv ( *_iRy ); // result is in _iRy;
00253         fy._cached ( true );
00254         _K = Pfull*C.transpose() * ( _iRy->to_mat() );
00255 
00256         sq_T pom ( ( int ) Pfull.rows() );
00257         _iRy->mult_sym_t ( C*Pfull,pom );
00258         ( *_P ) -= pom; // P = P -PC'iRy*CP;
00259         ( *_yp ) = C* ( *_mu ) +D*u; //y prediction
00260         ( *_mu ) += _K* ( y- ( *_yp ) );
00261 
00262 
00263         if ( evalll==true ) { //likelihood of observation y
00264                 ll=fy.evalpdflog ( y );
00265         }
00266 
00267 //cout << "y: " << y-(*_yp) <<" R: " << _Ry->to_mat() << " iR: " << _iRy->to_mat() << " ll: " << ll <<endl;
00268 
00269 };
00270 
00271 //TODO why not const pointer??
00272 
00273 template<class sq_T>
00274 EKF<sq_T>::EKF ( RV rvx0, RV rvy0, RV rvu0 ) : Kalman<ldmat> ( rvx0,rvy0,rvu0 ) {}
00275 
00276 template<class sq_T>
00277 void EKF<sq_T>::set_parameters ( diffbifn* pfxu0,  diffbifn* phxu0,const sq_T Q0,const sq_T R0 ) {
00278         pfxu = pfxu0;
00279         phxu = phxu0;
00280 
00281         //initialize matrices A C, later, these will be only updated!
00282         pfxu->dfdx_cond ( *_mu,zeros ( dimu ),A,true );
00283 //      pfxu->dfdu_cond ( *_mu,zeros ( dimu ),B,true );
00284         B.clear();
00285         phxu->dfdx_cond ( *_mu,zeros ( dimu ),C,true );
00286 //      phxu->dfdu_cond ( *_mu,zeros ( dimu ),D,true );
00287         D.clear();
00288 
00289         R = R0;
00290         Q = Q0;
00291 }
00292 
00293 template<class sq_T>
00294 void EKF<sq_T>::bayes ( const vec &dt ) {
00295         it_assert_debug ( dt.length() == ( dimy+dimu ),"KalmanFull::bayes wrong size of dt" );
00296 
00297         vec u = dt.get ( dimy,dimy+dimu-1 );
00298         vec y = dt.get ( 0,dimy-1 );
00299         //Time update
00300         *_mu = pfxu->eval ( *_mu, u );
00301         pfxu->dfdx_cond ( *_mu,u,A,false ); //update A by a derivative of fx
00302 
00303         //P  = A*P*A.transpose() + Q; in sq_T
00304         _P->mult_sym ( A );
00305         ( *_P ) +=Q;
00306 
00307         //Data update
00308         phxu->dfdx_cond ( *_mu,u,C,false ); //update C by a derivative hx
00309         //_Ry = C*P*C.transpose() + R; in sq_T
00310         _P->mult_sym ( C, *_Ry );
00311         ( *_Ry ) +=R;
00312 
00313         mat Pfull = _P->to_mat();
00314 
00315         _Ry->inv ( *_iRy ); // result is in _iRy;
00316         fy._cached ( true );
00317         _K = Pfull*C.transpose() * ( _iRy->to_mat() );
00318 
00319         sq_T pom ( ( int ) Pfull.rows() );
00320         _iRy->mult_sym_t ( C*Pfull,pom );
00321         ( *_P ) -= pom; // P = P -PC'iRy*CP;
00322         *_yp = phxu->eval ( *_mu,u ); //y prediction
00323         ( *_mu ) += _K* ( y-*_yp );
00324 
00325         if ( evalll==true ) {ll+=fy.evalpdflog ( y );}
00326 };
00327 
00328 
00329 #endif // KF_H
00330 
00331 

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