Kalman.java

/**
 * {@link Kalman} is an one dimensional implementation of KALMAN filter used to
 * predict a noisy signal containing fluctuations due to measurement errors, noise etc.
 * 
 * @author AchuthaRanga.Chenna
 * 
 */
public class SimpleKalman {
	/** process noise COVARIANCE */
	private double q = 1.48d;
	/** measurement noise COVARIANCE */
	private double r = 442.0d;

	/** Measurement value */
	private double x = 0d;
	/** estimation error COVARIANCE */
	private double p = 50d;
	/** KALMAN gain */
	private double k = 1d;

	/**
         * Init the filter with initial value of signal.
	 * @param initial_measurement
	 */
	public Kalman(double initial_measurement) {
		this.x = initial_measurement;
	}

	/**
	 * Method to get the predicted value of the measured signal value.
	 * 
	 * @param measurement
	 *            value from real time data.
	 * @return predicted measurement.
	 */
	public double getPredicted_Value(double measurement) {
		p = p + q;
		k = p / (p + r);
		x = x + k * (measurement - x);
		p = (1 - k) * p;
		return x;
	}
}

what's p? where's 50 come from? and what's measurement? is it different with x?