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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