EKFfixed Class Reference
Extended Kalman Filter with full matrices in fixed point arithmetic. More...
#include <ekf_obj.h>
Public Member Functions | |
| void | init_ekf (double Tv) |
| void | ekf (double ux, double uy, double isxd, double isyd) |
| void | prediction (int *ux) |
| void | correction (void) |
| void | update_psi (void) |
| EKFfixed () | |
| Default constructor. | |
| void | bayes (const vec &dt) |
| Here dt = [yt;ut] of appropriate dimensions. | |
| epdf & | posterior () |
| dummy! | |
| void | condition (const vec &Q0) |
Substitute val for rvc. | |
Constructors | |
| virtual BM * | _copy_ () const |
Mathematical operations | |
| virtual void | bayesB (const mat &Dt) |
| Batch Bayes rule (columns of Dt are observations). | |
| virtual double | logpred (const vec &dt) const |
| vec | logpred_m (const mat &dt) const |
| Matrix version of logpred. | |
| virtual epdf * | epredictor () const |
Constructs a predictive density  . | |
| virtual mpdf * | predictor () const |
| Constructs a conditional density 1-step ahead predictor. | |
Access to attributes | |
| const RV & | _drv () const |
| void | set_drv (const RV &rv) |
| void | set_rv (const RV &rv) |
| double | _ll () const |
| void | set_evalll (bool evl0) |
| virtual const epdf & | posterior () const =0 |
| virtual const epdf * | _e () const =0 |
Public Attributes | |
| int | Q [16] |
| int | R [4] |
| int | x_est [4] |
| int | x_pred [4] |
| int | P_pred [16] |
| int | P_est [16] |
| int | Y_mes [2] |
| int | ukalm [2] |
| int | Kalm [8] |
| int | PSI [16] |
| int | temp15a [16] |
| int | cA |
| int | cB |
| int | cC |
| int | cG |
| int | cH |
| long | temp30a [4] |
| enorm< fsqmat > | E |
| mat | Ry |
Protected Attributes | |
| RV | drv |
| Random variable of the data (optional). | |
| double | ll |
| Logarithm of marginalized data likelihood. | |
| bool | evalll |
If true, the filter will compute likelihood of the data record and store it in ll . Set to false if you want to save computational time. | |
Extension to conditional BM | |
| This extension is useful e.g. in Marginalized Particle Filter (bdm::MPF). Alternatively, it can be used for automated connection to DS when the condition is observed | |
| RV | rvc |
| Name of extension variable. | |
| const RV & | _rvc () const |
| access function | |
Logging of results | |
| ivec | LIDs |
| IDs of storages in loggers. | |
| bool | opt_L_bounds |
| Option for logging bounds. | |
| void | set_options (const string &opt) |
| Set boolean options from a string. | |
| void | log_add (logger *L, const string &name="") |
| Add all logged variables to a logger. | |
| void | logit (logger *L) |
Detailed Description
Extended Kalman Filter with full matrices in fixed point arithmetic.An approximation of the exact Bayesian filter with Gaussian noices and non-linear evolutions of their mean.
Member Function Documentation
| virtual BM* bdm::BM::_copy_ | ( | ) | const [inline, virtual, inherited] |
Copy function required in vectors, Arrays of BM etc. Have to be DELETED manually! Prototype:
BM* _copy_() const {return new BM(*this);}
Reimplemented in bdm::ARX, bdm::KalmanCh, bdm::EKF< sq_T >, bdm::EKFCh, and bdm::BMEF.
| virtual double bdm::BM::logpred | ( | const vec & | dt | ) | const [inline, virtual, inherited] |
Evaluates predictive log-likelihood of the given data record I.e. marginal likelihood of the data with the posterior integrated out.
Reimplemented in bdm::ARX, bdm::MixEF, and bdm::multiBM.
Referenced by bdm::BM::logpred_m().
The documentation for this class was generated from the following files:
- ekf_obj.h
- ekf_obj.cpp
