文件列表:

kalman.1.3\barray.c++ (5201, 1996-06-18)
kalman.1.3\barray.hpp (2010, 1996-06-18)
kalman.1.3\barray2d.c++ (4554, 1997-09-06)
kalman.1.3\barray2d.hpp (2111, 1996-06-18)
kalman.1.3\cputime.c++ (1558, 1996-06-18)
kalman.1.3\cputime.hpp (839, 1996-06-18)
kalman.1.3\dynamics.c++ (5758, 2002-01-28)
kalman.1.3\dynamics.hpp (4042, 2002-01-28)
kalman.1.3\error.c++ (1141, 1996-06-18)
kalman.1.3\error.hpp (1441, 1997-09-06)
kalman.1.3\getargs.c (4834, 1996-06-18)
kalman.1.3\getargs.h (830, 1996-06-18)
kalman.1.3\invm.c++ (1789, 1996-06-18)
kalman.1.3\kalman.c++ (1952, 2002-01-28)
kalman.1.3\kalman.hpp (1231, 2002-01-28)
kalman.1.3\kaltest.c++ (4457, 2002-01-28)
kalman.1.3\laxwen1.c++ (809, 2002-01-28)
kalman.1.3\laxwen1.hpp (602, 2002-01-28)
kalman.1.3\lumatrix.c++ (3621, 1996-06-18)
kalman.1.3\lumatrix.hpp (2285, 1996-06-18)
kalman.1.3\Makefile (7969, 2002-01-28)
kalman.1.3\matrix.c++ (5379, 1996-06-18)
kalman.1.3\matrix.hpp (2792, 1996-06-18)
kalman.1.3\scalar.hpp (853, 1996-06-18)
kalman.1.3\standio.c++ (4031, 1996-06-18)
kalman.1.3\standio.hpp (1666, 1996-06-18)
kalman.1.3\status.hpp (1174, 2002-01-28)
kalman.1.3\stoi.c (1481, 1996-06-18)
kalman.1.3\vector.c++ (4089, 1996-06-18)
kalman.1.3\vector.hpp (1795, 1996-06-18)
kalman.1.3 (0, 2011-05-05)

A Kalman filter C++ class, V 1.3 This class defines a Kalman filter object that is designed to use any type of dynamical system, defined as a Dynamics object. This information is passed to the filter along with the size of the system vector and of the observation vector as part of the constructor: Kalman filter( n, m, model ); defines a filter, called filter, with n points in the system vector, and m points in the observation vector. "model" is an object derived from Dynamics, that defines the actual physical model or method to be used. The public methods are described below: void sys_noise(Matrix& qs); sets the filter system noise level void obs_noise(Matrix& ro); sets the filter observation noise level void obs_sys(Matrix& hin); sets the observation matrix (generally called H in the literature). This can be called whenever the observation matrix is changed. void initial(Vector& ic); sets the value of the state vector to whatever 'ic' is set too. This is generally for setting the initial condition. Matrix& k; points to the current gain matrix Matrix& p; points to the current covariance matrix Kalman& operator++(); do a single time step of the system vector and the covariance matrix, and recalculate the new filter gain. This is the normal way to increment the filter. Typically this will be done repeatedly until there is a new observation. friend Vector& operator<<(Vector&, Kalman&); This operator tells the filter to return the current state of the filter. This is used any time the user wants output from the filter. friend Kalman& operator<<(Kalman&, Vector&); This operator tells the filter that there is a new observation vector. The following 5 methods are not for normal use by the user, public access is provided only for testing purposes. void step_x(int steps = 1); do "steps" (default = 1) time increments of the system vector with the current data void step_p(int steps = 1); do "steps" (default = 1) time increments of the covariance matrix with the current data void set_gain(); sets the gain based upon the current value of the covariance void update_x(); update the state vector given the current data. void update_p(); update the covariance matrix given the current data Operators can be combined, but note that precedence rules may not give what you expect, so use paretheses to be sure, e.g.: To give the filter new data, and then output the state: filter << z; u << filter; or, u << (filter << z); but because of the precedence rules, the following: u << filter << z; will output the filter state FIRST, and THEN give the filter new observations. This is perfectly legal, but probably not what you wanted. The same caution applies to the time increment operator (++) as well. I have provided a test program, that runs a physical model that is the 1-D 1-direction wave (also called the linear convection) equation: du/dt + c du/dx = 0 The observations consist of data from the analytic solution at the grid points defined in the array: oindex One can experiment with the effectiveness of the filter by putting different values in oindex (any number of points -- up to the number of gridpoints in the model -- can be used, their order does not matter). The system and observation noise levels are fixed for demonstration purposes, they could have differing values. Also for demonstration purposes, the observed values are exactly the analytic solution, adding random noise would provide a more realistic simulation. The test program uses Unix style command line switches which can be mixed in any order and can be concatonated into a single command. So that, kaltest -v -h0.05 is the same as: kaltest -h0.05v Typing: kaltest -- will give a summary of the possible switch settings. The program output goes to either a file that is named after the command line switches, or to standard output: kaltest -v outfile sends output to outfile kaltest outfile does the same kaltest > outfile goes to outfile because of redirection of STDOUT kaltest -v goes to STDOUT kaltest also goes to STDOUT The test program, with the default settings, runs in about 23 seconds on my old MIPS 3230 workstation. The code was developed under Unix. I have used the AT&T V2.1 C++ compiler as well as more modern compilers like GNU C++ V2.4.2. NOTE FOR MSDOS/Windows-XX USERS: This code will run on systems running MSDOS/Windows-95/Windows-NT. I have tested the current version of the code using Borland C++ V5.1 on Windows-NT (4.0). The timing code in the files cputime.xxx are Unix specific. You will have to get my DOS version of the timer from ftp://ftp.taygeta.com/pub/DOS_Extras or you can add -DNO_TIMING to the CPFLAGS and just not use the timing. If your C++ compiler does not have bool.h, the following will suffice for this code: #ifndef _bool_h_ #define _bool_h_ 1 #define false 0 #define true 1 #endif The test problem provided estimates the state of a system that is controlled by the 1-D 1 direction wave equation: du/dt + c du/dx = 0 There are 3 observation in the domain. The output file contains three columns of numbers, the filter output the actual field the error covariance (the diagonal of P) the columns are broken with a blank line between each time step. With the default values, the final values are: 0.0166782 9.38749e-06 0.01***5 0.212781 0.1951 0.0207852 0.399705 0.382692 0.0217012 0.5700*** 0.555578 0.0220925 0.717358 0.707113 0.0188156 0.836255 0.831475 0.00972488 0.926261 0.923883 0.00913587 0.***1879 0.***0787 0.0107426 0.999377 1 0.0122494 0.978015 0.***0783 0.0136142 0.919057 0.923876 0.0148826 0.824603 0.8314*** 0.0160753 0.697627 0.7071 0.0171751 0.54289 0.555562 0.0182094 0.368106 0.382675 0.0189308 0.181383 0.195081 0.0168032 -0.0080***65 -9.47491e-06 0.0091819 -0.201093 -0.1951 0.00892809 -0.388802 -0.382692 0.0105573 -0.56184 -0.555578 0.0120252 -0.712843 -0.707114 0.0132718 -0.835733 -0.831475 0.0129451 -0.925932 -0.923883 0.00803602 -0.***1683 -0.***0787 0.00843097 -0.999943 -1 0.0101222 -0.979256 -0.***0783 0.0116369 -0.920258 -0.923876 0.0130145 -0.825418 -0.8314*** 0.0142952 -0.6******6 -0.7071 0.015504 -0.544881 -0.555562 0.0166567 -0.369901 -0.382675 0.0177629 -0.180229 -0.195081 0.0188314 I used my own Matrix/Vector classes for this code but it should be easy to substitute another set as long as they provide the usual access operators ([][] and []) and the usual linear algebra operations (including inverse). If another Matrix/Vector class is used you can eliminate the following in the link step: matrix.o vector.o barray.o barray2d.o lumatrix.o invm.o error.o and replace them with your own modules. This code is provided as is, for general experimentation and comment. Everett (Skip) Carter Phone: 408-***1-0***5 FAX: 408-***1-0***7 Taygeta Scientific Inc. INTERNET: skip@taygeta.com 1340 Munras Ave., Suite 314 UUCP: ...!uunet!taygeta!skip Monterey, CA. 93940 WWW: http://www.taygeta.com/skip.html

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