[99] | 1 | \section{EKFfixed Class Reference} |
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| 2 | \label{classEKFfixed}\index{EKFfixed@{EKFfixed}} |
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| 3 | Extended \doxyref{Kalman}{p.}{classKalman} Filter in full matrices. |
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| 4 | |
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| 5 | |
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| 6 | {\tt \#include $<$ekf\_\-obj.h$>$} |
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| 7 | |
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| 8 | Inheritance diagram for EKFfixed:\nopagebreak |
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| 9 | \begin{figure}[H] |
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| 10 | \begin{center} |
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| 11 | \leavevmode |
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| 12 | \includegraphics[width=72pt]{classEKFfixed__inherit__graph} |
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| 13 | \end{center} |
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| 14 | \end{figure} |
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| 15 | Collaboration diagram for EKFfixed:\nopagebreak |
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| 16 | \begin{figure}[H] |
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| 17 | \begin{center} |
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| 18 | \leavevmode |
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| 19 | \includegraphics[height=400pt]{classEKFfixed__coll__graph} |
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| 20 | \end{center} |
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| 21 | \end{figure} |
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| 22 | \subsection*{Public Member Functions} |
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| 23 | \begin{CompactItemize} |
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| 24 | \item |
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| 25 | void \textbf{init\_\-ekf} (double Tv)\label{classEKFfixed_cece920bbf58fc72b25a6417b3ef0259} |
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| 26 | |
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| 27 | \item |
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| 28 | void \textbf{ekf} (double ux, double uy, double isxd, double isyd)\label{classEKFfixed_491e636b259dda3b876b7bd492df6b7c} |
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| 29 | |
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| 30 | \item |
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| 31 | void \textbf{prediction} (int $\ast$ux)\label{classEKFfixed_e77b35e1a11356dbfb1fdfa3017f60d3} |
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| 32 | |
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| 33 | \item |
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| 34 | void \textbf{correction} (void)\label{classEKFfixed_83ed56b86a056d7dbdd6ce44145fa5f3} |
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| 35 | |
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| 36 | \item |
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| 37 | void \textbf{update\_\-psi} (void)\label{classEKFfixed_dce43355681cfe8f1905db207b4dde8d} |
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| 38 | |
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| 39 | \item |
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| 40 | {\bf EKFfixed} ({\bf RV} rvx, {\bf RV} {\bf rvc})\label{classEKFfixed_64d7b1a39c27b1846bcd5628928748ef} |
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| 41 | |
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| 42 | \begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item |
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| 43 | void {\bf bayes} (const vec \&dt)\label{classEKFfixed_ddf5334bc1207658fd53698fffbac028} |
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| 44 | |
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| 45 | \begin{CompactList}\small\item\em Here dt = [yt;ut] of appropriate dimensions. \item\end{CompactList}\item |
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| 46 | {\bf epdf} \& {\bf \_\-epdf} ()\label{classEKFfixed_085cf16c573eda32d8d03619c6c4b518} |
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| 47 | |
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| 48 | \begin{CompactList}\small\item\em dummy! \item\end{CompactList}\item |
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| 49 | void {\bf condition} (const vec \&Q0)\label{classEKFfixed_c7fee79e75ad7f0c0e96c5a322cbf44e} |
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| 50 | |
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| 51 | \begin{CompactList}\small\item\em Substitute {\tt val} for {\tt rvc}. \item\end{CompactList}\item |
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| 52 | void {\bf bayes} (mat Dt)\label{classBM_87b07867fd4c133aa89a18543f68d9f9} |
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| 53 | |
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| 54 | \begin{CompactList}\small\item\em Batch Bayes rule (columns of Dt are observations). \item\end{CompactList}\item |
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| 55 | const {\bf RV} \& {\bf \_\-rv} () const \label{classBM_126bd2595c48e311fc2a7ab72876092a} |
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| 56 | |
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| 57 | \begin{CompactList}\small\item\em access function \item\end{CompactList}\item |
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| 58 | double {\bf \_\-ll} () const \label{classBM_87f4a547d2c29180be88175e5eab9c88} |
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| 59 | |
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| 60 | \begin{CompactList}\small\item\em access function \item\end{CompactList}\item |
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| 61 | const {\bf RV} \& {\bf \_\-rvc} () const \label{classBMcond_3fa60348b2da6b4208bb95b8d146900a} |
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| 62 | |
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| 63 | \begin{CompactList}\small\item\em access function \item\end{CompactList}\end{CompactItemize} |
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| 64 | \subsection*{Public Attributes} |
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| 65 | \begin{CompactItemize} |
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| 66 | \item |
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| 67 | int \textbf{Q} [16]\label{classEKFfixed_d04ddf049475a15e1ba93161aa5586ab} |
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| 68 | |
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| 69 | \item |
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| 70 | int \textbf{R} [4]\label{classEKFfixed_d914213d413b4d8f8d7bb728c5063d5e} |
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| 71 | |
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| 72 | \item |
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| 73 | int \textbf{x\_\-est} [4]\label{classEKFfixed_7fd20a80b00e9782da676e48eb5b54b3} |
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| 74 | |
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| 75 | \item |
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| 76 | int \textbf{x\_\-pred} [4]\label{classEKFfixed_9518fa723d7324f75df7822a589ee196} |
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| 77 | |
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| 78 | \item |
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| 79 | int \textbf{P\_\-pred} [16]\label{classEKFfixed_0b731c546a474433c1ea6f36f0125774} |
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| 80 | |
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| 81 | \item |
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| 82 | int \textbf{P\_\-est} [16]\label{classEKFfixed_b9ec9cb2d092ca3f4ad2a3b4420867ac} |
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| 83 | |
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| 84 | \item |
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| 85 | int \textbf{Y\_\-mes} [2]\label{classEKFfixed_5a8040cdb8bb5dca753485dc67db3287} |
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| 86 | |
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| 87 | \item |
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| 88 | int \textbf{ukalm} [2]\label{classEKFfixed_9292e43fb8e6fedfabb3a9b3c2118e33} |
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| 89 | |
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| 90 | \item |
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| 91 | int \textbf{Kalm} [8]\label{classEKFfixed_f754902bb769d3b58b89108c76d9a394} |
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| 92 | |
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| 93 | \item |
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| 94 | int \textbf{PSI} [16]\label{classEKFfixed_bf4b3d55c8d277673bf77f37f6590217} |
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| 95 | |
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| 96 | \item |
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| 97 | int \textbf{temp15a} [16]\label{classEKFfixed_8a677b253b54696701c1ca0cb6f7a622} |
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| 98 | |
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| 99 | \item |
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| 100 | int \textbf{cA}\label{classEKFfixed_6d4354dad09286a7a209983732853c5b} |
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| 101 | |
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| 102 | \item |
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| 103 | int \textbf{cB}\label{classEKFfixed_eac752adfb921c1c525f8c3b3fd15dad} |
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| 104 | |
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| 105 | \item |
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| 106 | int \textbf{cC}\label{classEKFfixed_2f35ef3dce13131ae9b4427309a1d005} |
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| 107 | |
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| 108 | \item |
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| 109 | int \textbf{cG}\label{classEKFfixed_50b31e70bb17cbdde2e28c83b9612c47} |
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| 110 | |
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| 111 | \item |
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| 112 | int \textbf{cH}\label{classEKFfixed_086e18ad28d139b0a2c0f77badc77a9a} |
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| 113 | |
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| 114 | \item |
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| 115 | long \textbf{temp30a} [4]\label{classEKFfixed_540046e3ab4d0bed4791f397062a626f} |
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| 116 | |
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| 117 | \item |
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| 118 | {\bf enorm}$<$ {\bf fsqmat} $>$ \textbf{E}\label{classEKFfixed_ea92b06e2b66c6771828e689bb727b76} |
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| 119 | |
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| 120 | \item |
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| 121 | mat \textbf{Ry}\label{classEKFfixed_6e5552506214757d24e59e508f91c8aa} |
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| 122 | |
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| 123 | \end{CompactItemize} |
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| 124 | \subsection*{Protected Attributes} |
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| 125 | \begin{CompactItemize} |
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| 126 | \item |
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| 127 | {\bf RV} {\bf rv}\label{classBM_af00f0612fabe66241dd507188cdbf88} |
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| 128 | |
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| 129 | \begin{CompactList}\small\item\em Random variable of the posterior. \item\end{CompactList}\item |
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| 130 | double {\bf ll}\label{classBM_5623fef6572a08c2b53b8c87b82dc979} |
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| 131 | |
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| 132 | \begin{CompactList}\small\item\em Logarithm of marginalized data likelihood. \item\end{CompactList}\item |
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| 133 | bool {\bf evalll}\label{classBM_bf6fb59b30141074f8ee1e2f43d03129} |
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| 134 | |
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| 135 | \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}\item |
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| 136 | {\bf RV} {\bf rvc}\label{classBMcond_9ba793c8ec453f04d372d17195ed8dec} |
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| 137 | |
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| 138 | \begin{CompactList}\small\item\em Identificator of the conditioning variable. \item\end{CompactList}\end{CompactItemize} |
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| 139 | |
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| 140 | |
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| 141 | \subsection{Detailed Description} |
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| 142 | Extended \doxyref{Kalman}{p.}{classKalman} Filter in full matrices. |
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| 143 | |
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| 144 | An approximation of the exact Bayesian filter with Gaussian noices and non-linear evolutions of their mean. |
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| 145 | |
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| 146 | The documentation for this class was generated from the following files:\begin{CompactItemize} |
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| 147 | \item |
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| 148 | work/mixpp/pmsm/simulator\_\-zdenek/ekf\_\-example/{\bf ekf\_\-obj.h}\item |
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| 149 | work/mixpp/pmsm/simulator\_\-zdenek/ekf\_\-example/ekf\_\-obj.cpp\end{CompactItemize} |
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