diff --git a/examples/LIEKF_SE2_SimpleGPSExample.cpp b/examples/LIEKF_SE2_SimpleGPSExample.cpp
new file mode 100644
index 000000000..ae6396be2
--- /dev/null
+++ b/examples/LIEKF_SE2_SimpleGPSExample.cpp
@@ -0,0 +1,74 @@
+//
+// Created by Scott on 4/18/2025.
+//
+#include <gtsam/nonlinear/LIEKF.h>
+#include <gtsam/geometry/Pose2.h>
+#include <iostream>
+
+using namespace std;
+using namespace gtsam;
+
+  // Measurement Processor
+  Vector2 h_gps(const Pose2& X,
+                        OptionalJacobian<2,3> H = {}) {
+    return X.translation(H);
+    }
+  int main() {
+    static const int dim = traits<Pose2>::dimension;
+
+    // Initialization
+    Pose2 X0(0.0, 0.0, 0.0);
+    Matrix3 P0 = Matrix3::Identity() * 0.1;
+    double dt = 1.0;
+
+    // Define GPS measurements
+    Matrix23 H;
+    h_gps(X0, H);
+    Vector2 z1, z2;
+    z1 << 1.0, 0.0;
+    z2 << 1.0, 1.0;
+
+	std::function<Vector2(const Pose2&, gtsam::OptionalJacobian<2, 3>)> measurement_function = h_gps;
+    LIEKF<Pose2, Vector2> ekf(X0, P0, measurement_function);
+
+    // Define Covariances
+    Matrix3 Q = (Vector3(0.05, 0.05, 0.001)).asDiagonal();
+    Matrix2 R = (Vector2(0.01, 0.01)).asDiagonal();
+
+    // Define odometry movements
+    Pose2 U1(1.0,1.0,0.5), U2(1.0,1.0,0.0);
+
+    // Define a transformation matrix to convert the covariance into (theta, x, y) form.
+    Matrix3 TransformP;
+    TransformP << 0, 0, 1,
+        1,0,0,
+        0,1,0;
+
+    // Predict / update stages
+    cout << "\nInitialization:\n";
+    cout << "X0: " << ekf.getState() << endl;
+    cout << "P0: " << TransformP * ekf.getCovariance() * TransformP.transpose() << endl;
+
+
+    ekf.predict(U1, Q);
+    cout << "\nFirst Prediction:\n";
+    cout << "X: " << ekf.getState() << endl;
+    cout << "P: " << TransformP * ekf.getCovariance() * TransformP.transpose() << endl;
+
+    ekf.update(z1, R);
+    cout << "\nFirst Update:\n";
+    cout << "X: " << ekf.getState() << endl;
+    cout << "P: " << TransformP * ekf.getCovariance() * TransformP.transpose() << endl;
+
+    ekf.predict(U2, Q);
+    cout << "\nSecond Prediction:\n";
+    cout << "X: " << ekf.getState() << endl;
+    cout << "P: " << TransformP * ekf.getCovariance() * TransformP.transpose() << endl;
+
+    ekf.update(z2, R);
+    cout << "\nSecond Update:\n";
+    cout << "X: " << ekf.getState() << endl;
+    cout << "P: " << TransformP * ekf.getCovariance() * TransformP.transpose() << endl;
+
+    return 0;
+   }
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