文件列表:

kalman (0, 2006-02-28)
kalman\kalman.c (18481, 1996-12-18)
kalman\kalman.h (5159, 1996-12-18)
kalman\kalman_p.h (650, 1996-12-18)
kalman\kalman.dat (7969, 1996-12-13)
kalman\paper.ps (238921, 1996-11-27)
kalman\Makefile (458, 1996-12-18)
kalman\paper.log (727, 2006-03-14)
kalman\Debug (0, 2006-04-05)
kalman\kalman_demo.ncb (33792, 2006-04-05)
kalman\kalman_demo.plg (1011, 2006-04-05)
kalman\kalman_demo.c (2812, 2006-04-05)
kalman\kalman_demo.opt (49664, 2006-04-05)
kalman\kalman_demo.dsp (3520, 2006-04-05)
kalman\kalman_demo.dsw (545, 2006-04-05)

KALMAN FILTER PACKAGE This package implements a Kalman filter as described in the paper "A Statistical Algorithm for Estimating Speed from Single Loop Volume and Occupancy Measurements" by D. J. Dailey. The filter uses volume (in vehicles/hour) and occupancy (as a percent, 0-100) to produce a speed (in miles/hour). FILES Makefile makefile kalman.c C source code kalman_p.h Prototypes for private functions kalman.h General header file: data structures, defines, and prototypes for public functions kalman_demo Simple demo program which uses the kalman filter to compute speed from canned data kalman.dat Canned data for demo program BUILDING THE FILTER "make" builds the Kalman filter and the demo program. "make demo" builds the Kalman filter, the demo, and runs the demo. IMPORTANT NOTE The Kalman filter requires two consecutive good data points to report a result. This means: (1) when it is started, it will not return a speed until the third iteration (2) if a sensor reports bad data or no data, there will be no speed reported for that iteration or the next (3) the filter will not start for a sensor until two good data points have been received on consecutive iterations. USING THE FILTER IN YOUR CODE Allocate a kalman_t for each sensor Initialize this kalman_t with kalman_initialize() Set kalman parameters to non-default values if needed Loop: Receive data Scale volume/occupancy Call kalman_filter() which returns TRUE/FALSE Examine return value, use if valid Example: kalman_t *k; /* pointer to kalman struct */ double occupancy /* occupancy, 0-100 */ int volume; /* volume, vehicles/hr */ int valid; /* validity flag for speed estimate */ double speed, length; /* speed and length from filter */ k = (kalman_t *) malloc(sizeof(kalman_t)); if (!k) { /* handle error */; break; } /* initialize, set Kalman parameters */ kalman_initialize(k); kalman_set_initial_speed(k, 55.0); while(1) { /* obtain volume and occupancy values * scale occupancy to 0-100 * scale volume to vehicles/hour */ valid = kalman_filter(k, occupancy, volume, &speed, &length); if (valid) { /* use valid value */ printf("got valid speed = %5.2f\n", speed); } else printf("no speed estimate\n"); } DATA STRUCTURES The struct kalman_t in kalman.h contains all the information used by the filter, as well as internal information about its state. VALID DATA The filter expects volume in vehicles per hour and occupancy as a percentage. Valid data is defined as: volume > 0 occupancy > 0 and occupancy < 100% The filter does not start until two consecutive valid data points are received. Once the filter starts, it handles invalid data but does not return a speed value. USABLE VS UNUSABLE kalman_filter returns TRUE if the speed estimate is deemed usable, FALSE otherwise. Under certain conditions, speed and length are returned even if kalman_filter() returns FALSE. These values should not be used, they are provided so that the filter may be monitored. SETTING COEFFECIENTS, PARAMETERS AND CONSTANTS The following constants are defined in kalman.h: DEFAULT_L Mean length of vehicles at sensor DEFAULT_SIGMA_S Variability of speed DEFAULT_SIGMA_NO Std dev of occupancy/volume ratio DEFAULT_A AR[2] coefficients, experimentally derived DEFAULT_B using least-squares forward/backward DEFAULT_INITIAL_SPEED Initial speed estimate The initial speed is used only when the filter is restarted, other parameters are used on each iteration. To change these for all sensors, change the #define. Alternatively, you can set coefficients on a per-sensor basis with kalman_set_{field}: kalman_set_initial_speed(k, 55.0); FILTER STATE AND OPERATION The filter requires two consecutive valid data points to properly initialize. Once it is initialized, it runs until the length goes negative. This gives us three states: initial: no data has been loaded transitions: moves to "first" if good data received stays on "initial" otherwise first: first data point loaded transitions: moves to "running" if good data received moves to "initial" otherwise running: filter is running transitions: moves to "initial" if length goes negative stays on "running" otherwise If the state is "initial", then bad data is ignored. If good data is received, it is loaded into the filter and the state changed to "first", indicating that another data point is needed. The filter always returns FALSE. If the state is "first", then another data point is needed. If good data is received, it is loaded into the filter and the filter is run. The state is changed to "running" The calculated speed is not yet usable, so the return value is FALSE. If good data is not received, then the filter moves back to "initial" state. If the state is "running", the filter has been initialized and can handle bad data. If good data is received, it is loaded and the filter is run. If the estimated length is negative, then the filter is restarted and FALSE returned. If the estimated length is outside contraints, the estimated values are still returned, but the return value is FALSE. If the estimated length is within constraints, the speed estimate is deemed rosbust and the return value is TRUE. If bad data is received, the filter is loaded with nulls and run. The resulting value may not be used so FALSE is returned. FALSE is also returned if the data from the previous iteration was bad. This can be expressed in pseudocode: initial: no data has been loaded actions: if good data received load into filter return FALSE transitions: moves to "first" if good data received stays on "initial" otherwise first: first data point loaded actions: if good data received load into filter run filter return FALSE transitions: moves to "running" if good data received moves to "initial" otherwise running: filter is running actions: if good data received load into filter else load nulls into filter run filter if length is negative restart filter return FALSE if data from this or previous run was null return FALSE if data is within length constraints return TRUE else return FALSE transitions: moves to "initial" if length goes negative stays on "running" otherwise The math is explained in the paper. $Id: kalman.README,v 1.4 1996/12/17 21:53:52 kint Exp $

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