[docs] Set Doxygen extract_all to true and fix Doxygen failures (#3695)

The template argument order for UnscentedTransform was reversed to match
all the other UKF classes. Since UnscentedTransform is intended as a
class for internal use only, this shouldn't cause much breakage.

wpimath/src/main/java/edu/wpi/first/math/estimator/ExtendedKalmanFilter.java

@@ -16,13 +16,23 @@ import edu.wpi.first.math.system.NumericalJacobian;
 import java.util.function.BiFunction;
 
 /**
- * Kalman filters combine predictions from a model and measurements to give an estimate of the true
- * system state. This is useful because many states cannot be measured directly as a result of
+ * A Kalman filter combines predictions from a model and measurements to give an estimate of the
+ * true system state. This is useful because many states cannot be measured directly as a result of
  * sensor noise, or because the state is "hidden".
  *
- * <p>The Extended Kalman filter is just like the {@link KalmanFilter Kalman filter}, but we make a
- * linear approximation of nonlinear dynamics and/or nonlinear measurement models. This means that
- * the EKF works with nonlinear systems.
+ * <p>Kalman filters use a K gain matrix to determine whether to trust the model or measurements
+ * more. Kalman filter theory uses statistics to compute an optimal K gain which minimizes the sum
+ * of squares error in the state estimate. This K gain is used to correct the state estimate by some
+ * amount of the difference between the actual measurements and the measurements predicted by the
+ * model.
+ *
+ * <p>An extended Kalman filter supports nonlinear state and measurement models. It propagates the
+ * error covariance by linearizing the models around the state estimate, then applying the linear
+ * Kalman filter equations.
+ *
+ * <p>For more on the underlying math, read
+ * https://file.tavsys.net/control/controls-engineering-in-frc.pdf chapter 9 "Stochastic control
+ * theory".
  */
 @SuppressWarnings("ClassTypeParameterName")
 public class ExtendedKalmanFilter<States extends Num, Inputs extends Num, Outputs extends Num>

wpimath/src/main/java/edu/wpi/first/math/estimator/UnscentedKalmanFilter.java

@@ -18,13 +18,21 @@ import org.ejml.simple.SimpleMatrix;
 
 /**
  * A Kalman filter combines predictions from a model and measurements to give an estimate of the
- * true ystem state. This is useful because many states cannot be measured directly as a result of
+ * true system state. This is useful because many states cannot be measured directly as a result of
  * sensor noise, or because the state is "hidden".
  *
- * <p>The Unscented Kalman filter is similar to the {@link KalmanFilter Kalman filter}, except that
- * it propagates carefully chosen points called sigma points through the non-linear model to obtain
- * an estimate of the true covariance (as opposed to a linearized version of it). This means that
- * the UKF works with nonlinear systems.
+ * <p>Kalman filters use a K gain matrix to determine whether to trust the model or measurements
+ * more. Kalman filter theory uses statistics to compute an optimal K gain which minimizes the sum
+ * of squares error in the state estimate. This K gain is used to correct the state estimate by some
+ * amount of the difference between the actual measurements and the measurements predicted by the
+ * model.
+ *
+ * <p>An unscented Kalman filter uses nonlinear state and measurement models. It propagates the
+ * error covariance using sigma points chosen to approximate the true probability distribution.
+ *
+ * <p>For more on the underlying math, read
+ * https://file.tavsys.net/control/controls-engineering-in-frc.pdf chapter 9 "Stochastic control
+ * theory".
  */
 @SuppressWarnings({"MemberName", "ClassTypeParameterName"})
 public class UnscentedKalmanFilter<States extends Num, Inputs extends Num, Outputs extends Num>