diff --git a/src/main/java/org/apache/commons/math/filter/DefaultMeasurementModel.java b/src/main/java/org/apache/commons/math/filter/DefaultMeasurementModel.java
index f946a01cc..7ce703c6e 100644
--- a/src/main/java/org/apache/commons/math/filter/DefaultMeasurementModel.java
+++ b/src/main/java/org/apache/commons/math/filter/DefaultMeasurementModel.java
@@ -23,6 +23,7 @@ import org.apache.commons.math.linear.RealMatrix;
  * Default implementation of a {@link MeasurementModel} for the use with a
  * {@link KalmanFilter}.
  *
+ * @since 3.0
  * @version $Id$
  */
 public class DefaultMeasurementModel implements MeasurementModel {
@@ -42,10 +43,8 @@ public class DefaultMeasurementModel implements MeasurementModel {
      * Create a new {@link MeasurementModel}, taking double arrays as input
      * parameters for the respective measurement matrix and noise.
      *
-     * @param measMatrix
-     *            the measurement matrix
-     * @param measNoise
-     *            the measurement noise matrix
+     * @param measMatrix the measurement matrix
+     * @param measNoise the measurement noise matrix
      */
     public DefaultMeasurementModel(final double[][] measMatrix,
             final double[][] measNoise) {
@@ -57,10 +56,8 @@ public class DefaultMeasurementModel implements MeasurementModel {
      * Create a new {@link MeasurementModel}, taking {@link RealMatrix} objects
      * as input parameters for the respective measurement matrix and noise.
      *
-     * @param measMatrix
-     *            the measurement matrix
-     * @param measNoise
-     *            the measurement noise matrix
+     * @param measMatrix the measurement matrix
+     * @param measNoise the measurement noise matrix
      */
     public DefaultMeasurementModel(final RealMatrix measMatrix,
             final RealMatrix measNoise) {
@@ -68,16 +65,12 @@ public class DefaultMeasurementModel implements MeasurementModel {
         this.measurementNoise = measNoise;
     }
 
-    /**
-     * {@inheritDoc}
-     */
+    /** {@inheritDoc} */
     public RealMatrix getMeasurementMatrix() {
         return measurementMatrix;
     }
 
-    /**
-     * {@inheritDoc}
-     */
+    /** {@inheritDoc} */
     public RealMatrix getMeasurementNoise() {
         return measurementNoise;
     }
diff --git a/src/main/java/org/apache/commons/math/filter/DefaultProcessModel.java b/src/main/java/org/apache/commons/math/filter/DefaultProcessModel.java
index 619572d04..38473f485 100644
--- a/src/main/java/org/apache/commons/math/filter/DefaultProcessModel.java
+++ b/src/main/java/org/apache/commons/math/filter/DefaultProcessModel.java
@@ -25,6 +25,7 @@ import org.apache.commons.math.linear.RealVector;
  * Default implementation of a {@link ProcessModel} for the use with a
  * {@link KalmanFilter}.
  *
+ * @since 3.0
  * @version $Id$
  */
 public class DefaultProcessModel implements ProcessModel {
@@ -40,40 +41,30 @@ public class DefaultProcessModel implements ProcessModel {
      */
     private RealMatrix controlMatrix;
 
-    /**
-     * The process noise covariance matrix.
-     */
+    /** The process noise covariance matrix. */
     private RealMatrix processNoiseCovMatrix;
 
-    /**
-     * The initial state estimation of the observed process.
-     */
+    /** The initial state estimation of the observed process. */
     private RealVector initialStateEstimateVector;
 
-    /**
-     * The initial error covariance matrix of the observed process.
-     */
+    /** The initial error covariance matrix of the observed process. */
     private RealMatrix initialErrorCovMatrix;
 
     /**
      * Create a new {@link ProcessModel}, taking double arrays as input
      * parameters.
      *
-     * @param stateTransition
-     *            the state transition matrix
-     * @param control
-     *            the control matrix
-     * @param processNoise
-     *            the process noise matrix
-     * @param initialStateEstimate
-     *            the initial state estimate vector
-     * @param initialErrorCovariance
-     *            the initial error covariance matrix
+     * @param stateTransition the state transition matrix
+     * @param control the control matrix
+     * @param processNoise the process noise matrix
+     * @param initialStateEstimate the initial state estimate vector
+     * @param initialErrorCovariance the initial error covariance matrix
      */
     public DefaultProcessModel(final double[][] stateTransition,
-            final double[][] control, final double[][] processNoise,
-            final double[] initialStateEstimate,
-            final double[][] initialErrorCovariance) {
+                               final double[][] control,
+                               final double[][] processNoise,
+                               final double[] initialStateEstimate,
+                               final double[][] initialErrorCovariance) {
         this(new Array2DRowRealMatrix(stateTransition),
                 new Array2DRowRealMatrix(control),
                 new Array2DRowRealMatrix(processNoise),
@@ -86,15 +77,13 @@ public class DefaultProcessModel implements ProcessModel {
      * parameters. The initial state estimate and error covariance are omitted
      * and will be initialized by the {@link KalmanFilter} to default values.
      *
-     * @param stateTransition
-     *            the state transition matrix
-     * @param control
-     *            the control matrix
-     * @param processNoise
-     *            the process noise matrix
+     * @param stateTransition the state transition matrix
+     * @param control the control matrix
+     * @param processNoise the process noise matrix
      */
     public DefaultProcessModel(final double[][] stateTransition,
-            final double[][] control, final double[][] processNoise) {
+                               final double[][] control,
+                               final double[][] processNoise) {
         this(new Array2DRowRealMatrix(stateTransition),
                 new Array2DRowRealMatrix(control),
                 new Array2DRowRealMatrix(processNoise), null, null);
@@ -104,21 +93,17 @@ public class DefaultProcessModel implements ProcessModel {
      * Create a new {@link ProcessModel}, taking double arrays as input
      * parameters.
      *
-     * @param stateTransition
-     *            the state transition matrix
-     * @param control
-     *            the control matrix
-     * @param processNoise
-     *            the process noise matrix
-     * @param initialStateEstimate
-     *            the initial state estimate vector
-     * @param initialErrorCovariance
-     *            the initial error covariance matrix
+     * @param stateTransition the state transition matrix
+     * @param control the control matrix
+     * @param processNoise the process noise matrix
+     * @param initialStateEstimate the initial state estimate vector
+     * @param initialErrorCovariance the initial error covariance matrix
      */
     public DefaultProcessModel(final RealMatrix stateTransition,
-            final RealMatrix control, final RealMatrix processNoise,
-            final RealVector initialStateEstimate,
-            final RealMatrix initialErrorCovariance) {
+                               final RealMatrix control,
+                               final RealMatrix processNoise,
+                               final RealVector initialStateEstimate,
+                               final RealMatrix initialErrorCovariance) {
         this.stateTransitionMatrix = stateTransition;
         this.controlMatrix = control;
         this.processNoiseCovMatrix = processNoise;
@@ -126,37 +111,27 @@ public class DefaultProcessModel implements ProcessModel {
         this.initialErrorCovMatrix = initialErrorCovariance;
     }
 
-    /**
-     * {@inheritDoc}
-     */
+    /** {@inheritDoc} */
     public RealMatrix getStateTransitionMatrix() {
         return stateTransitionMatrix;
     }
 
-    /**
-     * {@inheritDoc}
-     */
+    /** {@inheritDoc} */
     public RealMatrix getControlMatrix() {
         return controlMatrix;
     }
 
-    /**
-     * {@inheritDoc}
-     */
+    /** {@inheritDoc} */
     public RealMatrix getProcessNoise() {
         return processNoiseCovMatrix;
     }
 
-    /**
-     * {@inheritDoc}
-     */
+    /** {@inheritDoc} */
     public RealVector getInitialStateEstimate() {
         return initialStateEstimateVector;
     }
 
-    /**
-     * {@inheritDoc}
-     */
+    /** {@inheritDoc} */
     public RealMatrix getInitialErrorCovariance() {
         return initialErrorCovMatrix;
     }
diff --git a/src/main/java/org/apache/commons/math/filter/KalmanFilter.java b/src/main/java/org/apache/commons/math/filter/KalmanFilter.java
index 8d322476c..4c70ddac7 100644
--- a/src/main/java/org/apache/commons/math/filter/KalmanFilter.java
+++ b/src/main/java/org/apache/commons/math/filter/KalmanFilter.java
@@ -78,6 +78,7 @@ import org.apache.commons.math.util.MathUtils;
  *      Kalman filter example by Dan Simon</a>
  * @see ProcessModel
  * @see MeasurementModel
+ * @since 3.0
  * @version $Id$
  */
 public class KalmanFilter {
@@ -286,10 +287,9 @@ public class KalmanFilter {
     /**
      * Predict the internal state estimation one time step ahead.
      *
-     * @param u
-     *            the control vector
-     * @throws DimensionMismatchException
-     *             if the dimension of the control vector does not fit
+     * @param u the control vector
+     * @throws DimensionMismatchException if the dimension of the control
+     * vector does not fit
      */
     public void predict(final RealVector u) {
         // sanity checks
@@ -318,12 +318,11 @@ public class KalmanFilter {
     /**
      * Correct the current state estimate with an actual measurement.
      *
-     * @param z
-     *            the measurement vector
+     * @param z the measurement vector
      * @throws DimensionMismatchException
-     *             if the dimension of the measurement vector does not fit
+     * if the dimension of the measurement vector does not fit
      * @throws org.apache.commons.math.linear.SingularMatrixException
-     *             if the covariance matrix could not be inverted
+     * if the covariance matrix could not be inverted
      */
     public void correct(final double[] z) {
         correct(new ArrayRealVector(z));
@@ -332,12 +331,11 @@ public class KalmanFilter {
     /**
      * Correct the current state estimate with an actual measurement.
      *
-     * @param z
-     *            the measurement vector
-     * @throws DimensionMismatchException
-     *             if the dimension of the measurement vector does not fit
+     * @param z the measurement vector
+     * @throws DimensionMismatchException if the dimension of the
+     * measurement vector does not fit
      * @throws org.apache.commons.math.linear.SingularMatrixException
-     *             if the covariance matrix could not be inverted
+     * if the covariance matrix could not be inverted
      */
     public void correct(final RealVector z) {
         // sanity checks
diff --git a/src/main/java/org/apache/commons/math/filter/MeasurementModel.java b/src/main/java/org/apache/commons/math/filter/MeasurementModel.java
index f8be78cd6..026b0da4a 100644
--- a/src/main/java/org/apache/commons/math/filter/MeasurementModel.java
+++ b/src/main/java/org/apache/commons/math/filter/MeasurementModel.java
@@ -21,6 +21,7 @@ import org.apache.commons.math.linear.RealMatrix;
 /**
  * Defines the measurement model for the use with a {@link KalmanFilter}.
  *
+ * @since 3.0
  * @version $Id$
  */
 public interface MeasurementModel {
diff --git a/src/main/java/org/apache/commons/math/filter/ProcessModel.java b/src/main/java/org/apache/commons/math/filter/ProcessModel.java
index 486bcb533..b097a080a 100644
--- a/src/main/java/org/apache/commons/math/filter/ProcessModel.java
+++ b/src/main/java/org/apache/commons/math/filter/ProcessModel.java
@@ -22,6 +22,7 @@ import org.apache.commons.math.linear.RealVector;
 /**
  * Defines the process dynamics model for the use with a {@link KalmanFilter}.
  *
+ * @since 3.0
  * @version $Id$
  */
 public interface ProcessModel {