AP_Compass: CompassCalibrator comment update

libraries/AP_Compass/CompassCalibrator.cpp

@@ -15,43 +15,50 @@
 */
 
 /*
- *    AP_Compass_Callib.cpp 
+ * The intention of a magnetometer in a compass application is to measure
+ * Earth's magnetic field. Measurements other than those of Earth's magnetic
+ * field are considered errors. This algorithm computes a set of correction
+ * parameters that null out errors from various sources:
  *
- *     1.The following code uses an implementation of a Levenberg-Marquardt non-linear
+ * - Sensor bias error
- *       least square regression technique to fit the result over a sphere.
+ * - "Hard iron" error caused by materials fixed to the vehicle body that
+ *     produce static magnetic fields.
+ * - Sensor scale-factor error
+ * - Sensor cross-axis sensitivity
+ * - "Soft iron" error caused by materials fixed to the vehicle body that
+ *     distort magnetic fields.
+ *
+ * This is done by taking a set of samples that are assumed to be the product
+ * of rotation in earth's magnetic field and fitting an offset ellipsoid to
+ * them, determining the correction to be applied to adjust the samples into an
+ * origin-centered sphere.
+ *
+ * The state machine of this library is described entirely by the
+ * compass_cal_status_t enum, and all state transitions are managed by the
+ * set_status function. Normally, the library is in the NOT_STARTED state. When
+ * the start function is called, the state transitions to WAITING_TO_START,
+ * until two conditions are met: the delay as elapsed, and the memory for the
+ * sample buffer has been successfully allocated.
+ * Once these conditions are met, the state transitions to RUNNING_STEP_ONE, and
+ * samples are collected via calls to the new_sample function. These samples are
+ * accepted or rejected based on distance to the nearest sample. The samples are
+ * assumed to cover the surface of a sphere, and the radius of that sphere is
+ * initialized to a conservative value. Based on a circle-packing pattern, the
+ * minimum distance is set such that some percentage of the surface of that
+ * sphere must be covered by samples.
+ *
+ * Once the sample buffer is full, a sphere fitting algorithm is run, which
+ * computes a new sphere radius. The sample buffer is thinned of samples which
+ * no longer meet the acceptance criteria, and the state transitions to
+ * RUNNING_STEP_TWO. Samples continue to be collected until the buffer is full
+ * again, the full ellipsoid fit is run, and the state transitions to either
+ * SUCCESS or FAILED.
+ *
+ * The fitting algorithm used is Levenberg-Marquardt. See also:
  * http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm
  *
- *     2.Fitness Matrix is generated by placing the sample points into a general sphere equation.
+ * The sample acceptance distance is determined as follows:
- *  
+ * < EXPLANATION OF SAMPLE ACCEPTANCE TO BE FILLED IN BY SID >
- *     3.Jacobian matrix is calculated using partial derivative equation of each parameters
- *       wrt fitness function.
- *
- *
- *     Sampling-Rules
- *     ==============
- *
- *     1.Every point should be unique, no repeated samples
- *
- *     2.Every consecutive 4 samples should not be coplanar, as for every 4 non-coplanar point
- *      in space there exists a distinct sphere. Therefore using this method we will be getting
- *      set of atleast NUM_SAMPLES quadruples of coplanar point.
- *
- *     3.Every point should be atleast separated by D distance:
- *
- *              where:
- *                  D = distance between any two sample points
- *                  (Surface Area of Sphere)/(2 * (Area of equilateral triangle)) = NUM_SAMPLES
- *              =>  D >= 5.5 * Radius / 10
- *              but for the sake of leniency to the user let's halve this distance. This will ensure
- *              atleast 50% coverage of sphere. The rest will be taken care of by Gauss-Newton.
- *                  D >= 5.5 * Radius / 20
- *
- *      Explaination: If we are to consider a sphere and place discrete points which are uniformly
- *                    spread. The simplest possible polygon that can be created using distinct closest
- *                    points is an equilateral triangle. The number of such triangles will be NUM_SAMPLES
- *                    and will all be totally distinct. The side of such triangles also represent the 
- *                    minimum distance between any two samples for 100% coverage. But since this would 
- *                    be very-difficult/impossible for user to achieve, we reduce it to minimum 50% coverage.
  *
  */