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Timestamp:
03/03/08 13:00:32 (17 years ago)
Author:
smidl
Message:

test KF : estimation of R in KF is not possible! Likelihood of y_t is growing when R -> 0

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  • doc/latex/classKalman.tex

    r28 r32  
    1010\begin{center} 
    1111\leavevmode 
    12 \includegraphics[width=77pt]{classKalman__inherit__graph} 
     12\includegraphics[width=103pt]{classKalman__inherit__graph} 
    1313\end{center} 
    1414\end{figure} 
    … …  
    1717\begin{center} 
    1818\leavevmode 
    19 \includegraphics[width=70pt]{classKalman__coll__graph} 
     19\includegraphics[width=81pt]{classKalman__coll__graph} 
    2020\end{center} 
    2121\end{figure} 
    … …  
    2323\begin{CompactItemize} 
    2424\item  
    25 {\bf Kalman} (int dimx, int dimu, int dimy)\label{classKalman_96958a5ebfa966d892137987f265083a} 
     25{\bf Kalman} ({\bf RV} rvx0, {\bf RV} rvy0, {\bf RV} rvu0)\label{classKalman_3d56b0a97b8c1e25fdd3b10eef3c2ad3} 
    2626 
    2727\begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item  
    28 {\bf Kalman} (mat A0, mat B0, mat C0, mat D0, sq\_\-T R0, sq\_\-T Q0, sq\_\-T P0, vec mu0)\label{classKalman_83118f4bd2ecbc70b03cfd573088ed6f} 
     28{\bf Kalman} (const {\bf Kalman}$<$ sq\_\-T $>$ \&K0)\label{classKalman_ce38e31810aea4db45a83ad05eaba009} 
    2929 
    30 \begin{CompactList}\small\item\em Full constructor. \item\end{CompactList}\item  
    31 void {\bf bayes} (const vec \&dt, bool {\bf evalll}=true)\label{classKalman_e945d9205ca14acbd83ba80ea6f72b8e} 
     30\begin{CompactList}\small\item\em Copy constructor. \item\end{CompactList}\item  
     31void {\bf set\_\-parameters} (const mat \&A0, const mat \&B0, const mat \&C0, const mat \&D0, const sq\_\-T \&R0, const sq\_\-T \&Q0)\label{classKalman_239b28a0380946f5749b2f8d2807f93a} 
     32 
     33\begin{CompactList}\small\item\em Set parameters with check of relevance. \item\end{CompactList}\item  
     34void {\bf set\_\-est} (const vec \&mu0, const sq\_\-T \&P0)\label{classKalman_80bcf29466d9a9dd2b8f74699807d0c0} 
     35 
     36\begin{CompactList}\small\item\em Set estimate values, used e.g. in initialization. \item\end{CompactList}\item  
     37void {\bf bayes} (const vec \&dt)\label{classKalman_7750ffd73f261828a32c18aaeb65c75c} 
    3238 
    3339\begin{CompactList}\small\item\em Here dt = [yt;ut] of appropriate dimensions. \item\end{CompactList}\item  
    34 virtual void {\bf bayes} (const vec \&dt)=0 
    35 \begin{CompactList}\small\item\em Incremental Bayes rule. \item\end{CompactList}\item  
     40{\bf epdf} \& {\bf \_\-epdf} ()\label{classKalman_a213c57aef55b2645e550bed81cfc0d4} 
     41 
     42\begin{CompactList}\small\item\em Returns a pointer to the \doxyref{epdf}{p.}{classepdf} representing posterior density on parameters. Use with care! \item\end{CompactList}\item  
    3643void {\bf bayes} (mat Dt)\label{classBM_87b07867fd4c133aa89a18543f68d9f9} 
    3744 
    38 \begin{CompactList}\small\item\em Batch Bayes rule (columns of Dt are observations). \item\end{CompactList}\item  
    39 {\bf epdf} $\ast$ {\bf \_\-epdf} ()\label{classBM_a5b8f6c8a872738cfaa30ab010e8c077} 
    40  
    41 \begin{CompactList}\small\item\em Returns a pointer to the \doxyref{epdf}{p.}{classepdf} representing posterior density on parameters. Use with care! \item\end{CompactList}\end{CompactItemize} 
    42 \subsection*{Public Attributes} 
     45\begin{CompactList}\small\item\em Batch Bayes rule (columns of Dt are observations). \item\end{CompactList}\end{CompactItemize} 
     46\subsection*{Protected Attributes} 
    4347\begin{CompactItemize} 
    4448\item  
    45 vec {\bf mu}\label{classKalman_3063a3f58a74cea672ae889971012eed} 
     49{\bf RV} \textbf{rvy}\label{classKalman_7501230c2fafa3655887d2da23b3184c} 
    4650 
    47 \begin{CompactList}\small\item\em Mean value of the posterior density. \item\end{CompactList}\item  
    48 sq\_\-T {\bf P}\label{classKalman_188cd5ac1c9e496b1a371eb7c57c97d3} 
     51\item  
     52{\bf RV} \textbf{rvu}\label{classKalman_44a16ffd5ac1e6e39bae34fea9e1e498} 
    4953 
    50 \begin{CompactList}\small\item\em Mean value of the posterior density. \item\end{CompactList}\item  
    51 double {\bf ll}\label{classBM_5623fef6572a08c2b53b8c87b82dc979} 
    52  
    53 \begin{CompactList}\small\item\em Logarithm of marginalized data likelihood. \item\end{CompactList}\item  
    54 bool {\bf evalll}\label{classBM_bf6fb59b30141074f8ee1e2f43d03129} 
    55  
    56 \begin{CompactList}\small\item\em If true, the filter will compute likelihood of the data record and store it in {\tt ll} . Set to false if you want to save time. \item\end{CompactList}\end{CompactItemize} 
    57 \subsection*{Protected Attributes} 
    58 \begin{CompactItemize} 
    5954\item  
    6055int \textbf{dimx}\label{classKalman_39c8c403b46fa3b8c7da77cb2e3729eb} 
    … …  
    8580 
    8681\item  
     82{\bf enorm}$<$ sq\_\-T $>$ {\bf est}\label{classKalman_5568c74bac67ae6d3b1061dba60c9424} 
     83 
     84\begin{CompactList}\small\item\em posterior density on \$x\_\-t\$ \item\end{CompactList}\item  
     85{\bf enorm}$<$ sq\_\-T $>$ {\bf fy}\label{classKalman_e580ab06483952bd03f2e651763e184f} 
     86 
     87\begin{CompactList}\small\item\em preditive density on \$y\_\-t\$ \item\end{CompactList}\item  
    8788mat \textbf{\_\-K}\label{classKalman_d422f51467c7a06174af2476d2826132} 
    8889 
    8990\item  
    90 vec \textbf{\_\-yp}\label{classKalman_30b7461989185d3d02cf42b8e2a37649} 
     91vec $\ast$ \textbf{\_\-yp}\label{classKalman_5188eb0329f8561f0b357af329769bf8} 
    9192 
    9293\item  
    93 sq\_\-T \textbf{\_\-Ry}\label{classKalman_477dca07d91ea1a1f41d51bb0229934f} 
     94sq\_\-T $\ast$ \textbf{\_\-Ry}\label{classKalman_e17dd745daa8a958035a334a56fa4674} 
    9495 
    9596\item  
    96 sq\_\-T \textbf{\_\-iRy}\label{classKalman_15f1a793210750a7e4642fcd948b24c5} 
     97sq\_\-T $\ast$ \textbf{\_\-iRy}\label{classKalman_fbbdf31365f5a5674099599200ea193b} 
    9798 
    98 \end{CompactItemize} 
    99 \subsection*{Friends} 
    100 \begin{CompactItemize} 
    10199\item  
    102 std::ostream \& \textbf{operator$<$$<$} (std::ostream \&os, const {\bf KalmanFull} \&kf)\label{classKalman_86ba216243ed95bb46d80d88775d16af} 
     100vec $\ast$ \textbf{\_\-mu}\label{classKalman_d1f669b5b3421a070cc75d77b55ba734} 
    103101 
    104 \end{CompactItemize} 
     102\item  
     103sq\_\-T $\ast$ \textbf{\_\-P}\label{classKalman_b3388218567128a797e69b109138271d} 
     104 
     105\item  
     106sq\_\-T $\ast$ \textbf{\_\-iP}\label{classKalman_b8bb7f870d69993493ba67ce40e7c3e9} 
     107 
     108\item  
     109{\bf RV} {\bf rv}\label{classBM_af00f0612fabe66241dd507188cdbf88} 
     110 
     111\begin{CompactList}\small\item\em Random variable of the posterior. \item\end{CompactList}\item  
     112double {\bf ll}\label{classBM_5623fef6572a08c2b53b8c87b82dc979} 
     113 
     114\begin{CompactList}\small\item\em Logarithm of marginalized data likelihood. \item\end{CompactList}\item  
     115bool {\bf evalll}\label{classBM_bf6fb59b30141074f8ee1e2f43d03129} 
     116 
     117\begin{CompactList}\small\item\em If true, the filter will compute likelihood of the data record and store it in {\tt ll} . Set to false if you want to save time. \item\end{CompactList}\end{CompactItemize} 
    105118 
    106119 
    … …  
    110123\doxyref{Kalman}{p.}{classKalman} filter with covariance matrices in square root form.  
    111124 
    112 \subsection{Member Function Documentation} 
    113 \index{Kalman@{Kalman}!bayes@{bayes}} 
    114 \index{bayes@{bayes}!Kalman@{Kalman}} 
    115 \subsubsection{\setlength{\rightskip}{0pt plus 5cm}virtual void BM::bayes (const vec \& {\em dt})\hspace{0.3cm}{\tt  [pure virtual, inherited]}}\label{classBM_a892eff438aab2dd1a9e2efcb7fb5bdf} 
    116  
    117  
    118 Incremental Bayes rule.  
    119  
    120 \begin{Desc} 
    121 \item[Parameters:] 
    122 \begin{description} 
    123 \item[{\em dt}]vector of input data \end{description} 
    124 \end{Desc} 
    125  
    126  
    127125The documentation for this class was generated from the following file:\begin{CompactItemize} 
    128126\item