diff --git a/examples/IEKF_NavstateExample.cpp b/examples/IEKF_NavstateExample.cpp
index a32d367b2..ef3ef204b 100644
--- a/examples/IEKF_NavstateExample.cpp
+++ b/examples/IEKF_NavstateExample.cpp
@@ -10,15 +10,15 @@
  * -------------------------------------------------------------------------- */
 
 /**
- * @file LIEKF_NavstateExample.cpp
- * @brief LIEKF on NavState (SE_2(3)) with IMU (predict) and GPS (update)
+ * @file IEKF_NavstateExample.cpp
+ * @brief InvariantEKF on NavState (SE_2(3)) with IMU (predict) and GPS (update)
  * @date April 25, 2025
  * @authors Scott Baker, Matt Kielo, Frank Dellaert
  */
 
 #include <gtsam/base/Matrix.h>
 #include <gtsam/base/OptionalJacobian.h>
-#include <gtsam/navigation/LIEKF.h>
+#include <gtsam/navigation/InvariantEKF.h>
 #include <gtsam/navigation/NavState.h>
 
 #include <iostream>
@@ -53,7 +53,7 @@ int main() {
   // Initial state & covariances
   NavState X0;  // R=I, v=0, t=0
   Matrix9 P0 = Matrix9::Identity() * 0.1;
-  LIEKF<NavState> ekf(X0, P0);
+  InvariantEKF<NavState> ekf(X0, P0);
 
   // Noise & timestep
   double dt = 1.0;
diff --git a/examples/IEKF_Rot3Example.cpp b/examples/IEKF_Rot3Example.cpp
index 172af894d..88989c67e 100644
--- a/examples/IEKF_Rot3Example.cpp
+++ b/examples/IEKF_Rot3Example.cpp
@@ -10,7 +10,7 @@
  * -------------------------------------------------------------------------- */
 
 /**
- * @file LIEKF_Rot3Example.cpp
+ * @file IEKF_Rot3Example.cpp
  * @brief Left‐Invariant EKF on SO(3) with state‐dependent pitch/roll control
  * and a single magnetometer update.
  * @date April 25, 2025
@@ -20,11 +20,10 @@
 #include <gtsam/base/Matrix.h>
 #include <gtsam/base/OptionalJacobian.h>
 #include <gtsam/geometry/Rot3.h>
-#include <gtsam/navigation/LIEKF.h>
+#include <gtsam/navigation/InvariantEKF.h>
 
 #include <iostream>
 
-
 using namespace std;
 using namespace gtsam;
 
@@ -39,7 +38,7 @@ Vector3 dynamicsSO3(const Rot3& X, OptionalJacobian<3, 3> H = {}) {
   D_phi.row(2).setZero();
 
   if (H) *H = -k * D_phi;  // ∂(–kφ)/∂δR
-  return -k * phi;        // xi ∈ 𝔰𝔬(3)
+  return -k * phi;         // xi ∈ 𝔰𝔬(3)
 }
 
 // --- 2) Magnetometer model: z = R⁻¹ m, H = –[z]_× ---
@@ -54,7 +53,7 @@ int main() {
   // Initial estimate (identity) and covariance
   const Rot3 R0 = Rot3::RzRyRx(0.1, -0.2, 0.3);
   const Matrix3 P0 = Matrix3::Identity() * 0.1;
-  LIEKF<Rot3> ekf(R0, P0);
+  InvariantEKF<Rot3> ekf(R0, P0);
 
   // Timestep, process noise, measurement noise
   double dt = 0.1;
diff --git a/examples/IEKF_SE2Example.cpp b/examples/IEKF_SE2Example.cpp
index 0985d2372..1a7772c7c 100644
--- a/examples/IEKF_SE2Example.cpp
+++ b/examples/IEKF_SE2Example.cpp
@@ -10,17 +10,17 @@
  * -------------------------------------------------------------------------- */
 
 /**
- * @file LIEKF_SE2_SimpleGPSExample.cpp
+ * @file IEKF_SE2Example.cpp
  * @brief A left invariant Extended Kalman Filter example using a Lie group
  * odometry as the prediction stage on SE(2) and
  *
- * This example uses the templated LIEKF class to estimate the state of
- * an object using odometry / GPS measurements The prediction stage of the LIEKF
- * uses a Lie Group element to propagate the stage in a discrete LIEKF. For most
- * cases, U = exp(u^ * dt) if u is a control vector that is constant over the
- * interval dt. However, if u is not constant over dt, other approaches are
- * needed to find the value of U. This approach simply takes a Lie group element
- * U, which can be found in various different ways.
+ * This example uses the templated InvariantEKF class to estimate the state of
+ * an object using odometry / GPS measurements The prediction stage of the
+ * InvariantEKF uses a Lie Group element to propagate the stage in a discrete
+ * InvariantEKF. For most cases, U = exp(u^ * dt) if u is a control vector that
+ * is constant over the interval dt. However, if u is not constant over dt,
+ * other approaches are needed to find the value of U. This approach simply
+ * takes a Lie group element U, which can be found in various different ways.
  *
  * This data was compared to a left invariant EKF on SE(2) using identical
  * measurements and noise from the source of the InEKF plugin
@@ -32,7 +32,7 @@
  * @authors Scott Baker, Matt Kielo, Frank Dellaert
  */
 #include <gtsam/geometry/Pose2.h>
-#include <gtsam/navigation/LIEKF.h>
+#include <gtsam/navigation/InvariantEKF.h>
 
 #include <iostream>
 
@@ -45,8 +45,9 @@ using namespace gtsam;
 Vector2 h_gps(const Pose2& X, OptionalJacobian<2, 3> H = {}) {
   return X.translation(H);
 }
-// Define a LIEKF class that uses the Pose2 Lie group as the state and the
-// Vector2 measurement type.
+
+// Define a InvariantEKF class that uses the Pose2 Lie group as the state and
+// the Vector2 measurement type.
 int main() {
   // // Initialize the filter's state, covariance, and time interval values.
   Pose2 X0(0.0, 0.0, 0.0);
@@ -54,7 +55,7 @@ int main() {
 
   // Create the filter with the initial state, covariance, and measurement
   // function.
-  LIEKF<Pose2> ekf(X0, P0);
+  InvariantEKF<Pose2> ekf(X0, P0);
 
   // Define the process covariance and measurement covariance matrices Q and R.
   Matrix3 Q = (Vector3(0.05, 0.05, 0.001)).asDiagonal();
diff --git a/gtsam/navigation/InvariantEKF.h b/gtsam/navigation/InvariantEKF.h
index 9b4b6d2b5..443f730c4 100644
--- a/gtsam/navigation/InvariantEKF.h
+++ b/gtsam/navigation/InvariantEKF.h
@@ -10,10 +10,10 @@
  * -------------------------------------------------------------------------- */
 
 /**
- * @file    LIEKF.h
- * @brief   Left-Invariant Extended Kalman Filter (LIEKF) implementation
+ * @file    InvariantEKF.h
+ * @brief   Left-Invariant Extended Kalman Filter (InvariantEKF) implementation
  *
- * This file defines the LIEKF class template for performing prediction and
+ * This file defines the InvariantEKF class template for performing prediction and
  * update steps of an Extended Kalman Filter on states residing in a Lie group.
  * The class supports state evolution via group composition and dynamics
  * functions, along with measurement updates using tangent-space corrections.
@@ -34,8 +34,8 @@
 namespace gtsam {
 
 /**
- * @class LIEKF
- * @brief Left-Invariant Extended Kalman Filter (LIEKF) on a Lie group G
+ * @class InvariantEKF
+ * @brief Left-Invariant Extended Kalman Filter (InvariantEKF) on a Lie group G
  *
  * @tparam G  Lie group type providing:
  *           - static int dimension = tangent dimension
@@ -50,7 +50,7 @@ namespace gtsam {
  * using the left-invariant error in the tangent space.
  */
 template <typename G>
-class LIEKF {
+class InvariantEKF {
  public:
   /// Tangent-space dimension
   static constexpr int n = traits<G>::dimension;
@@ -59,7 +59,7 @@ class LIEKF {
   using MatrixN = Eigen::Matrix<double, n, n>;
 
   /// Constructor: initialize with state and covariance
-  LIEKF(const G& X0, const Matrix& P0) : X_(X0), P_(P0) {}
+  InvariantEKF(const G& X0, const Matrix& P0) : X_(X0), P_(P0) {}
 
   /// @return current state estimate
   const G& state() const { return X_; }
@@ -229,6 +229,6 @@ class LIEKF {
 
 // Define static identity I_n
 template <typename G>
-const typename LIEKF<G>::MatrixN LIEKF<G>::I_n = LIEKF<G>::MatrixN::Identity();
+const typename InvariantEKF<G>::MatrixN InvariantEKF<G>::I_n = InvariantEKF<G>::MatrixN::Identity();
 
 }  // namespace gtsam
diff --git a/gtsam/navigation/tests/testInvariantEKF.cpp b/gtsam/navigation/tests/testInvariantEKF.cpp
index 5577a4357..c3f93d429 100644
--- a/gtsam/navigation/tests/testInvariantEKF.cpp
+++ b/gtsam/navigation/tests/testInvariantEKF.cpp
@@ -8,8 +8,8 @@
  * -------------------------------------------------------------------------- */
 
 /**
- * @file testLIEKFNavState.cpp
- * @brief Unit test for the NavState dynamics Jacobian in LIEKF example.
+ * @file testInvariantEKFNavState.cpp
+ * @brief Unit test for the NavState dynamics Jacobian in InvariantEKF example.
  * @date April 26, 2025
  * @authors Scott Baker, Matt Kielo, Frank Dellaert
  */
@@ -18,12 +18,12 @@
 #include <gtsam/base/Testable.h>
 #include <gtsam/base/numericalDerivative.h>
 #include <gtsam/geometry/Rot3.h>
-#include <gtsam/navigation/LIEKF.h>
+#include <gtsam/navigation/InvariantEKF.h>
 #include <gtsam/navigation/NavState.h>
 
 using namespace gtsam;
 
-// Duplicate the dynamics function in LIEKF_Rot3Example
+// Duplicate the dynamics function in InvariantEKF_Rot3Example
 namespace exampleSO3 {
 static constexpr double k = 0.5;
 Vector3 dynamics(const Rot3& X, OptionalJacobian<3, 3> H = {}) {
@@ -64,12 +64,12 @@ TEST(IEKF, PredictNumericState) {
 
   // Analytic Jacobian
   Matrix3 actualH;
-  LIEKF<Rot3> iekf0(R0, P0);
+  InvariantEKF<Rot3> iekf0(R0, P0);
   iekf0.predictMean(exampleSO3::dynamics, dt, Q, actualH);
 
   // wrap predict into a state->state functor (mapping on SO(3))
   auto g = [&](const Rot3& R) -> Rot3 {
-    LIEKF<Rot3> iekf(R, P0);
+    InvariantEKF<Rot3> iekf(R, P0);
     return iekf.predictMean(exampleSO3::dynamics, dt, Q);
   };
 
@@ -94,12 +94,12 @@ TEST(IEKF, StateAndControl) {
 
   // Analytic Jacobian
   Matrix3 actualH;
-  LIEKF<Rot3> iekf0(R0, P0);
+  InvariantEKF<Rot3> iekf0(R0, P0);
   iekf0.predictMean(f, dummy_u, dt, Q, actualH);
 
   // wrap predict into a state->state functor (mapping on SO(3))
   auto g = [&](const Rot3& R) -> Rot3 {
-    LIEKF<Rot3> iekf(R, P0);
+    InvariantEKF<Rot3> iekf(R, P0);
     return iekf.predictMean(f, dummy_u, dt, Q);
   };