diff --git a/src/modules/ekf2/EKF/ekf.h b/src/modules/ekf2/EKF/ekf.h
index 9c8f391d32..2899fca28a 100644
--- a/src/modules/ekf2/EKF/ekf.h
+++ b/src/modules/ekf2/EKF/ekf.h
@@ -63,9 +63,6 @@ public:
 	typedef matrix::Vector<float, State::size> VectorState;
 	typedef matrix::SquareMatrix<float, State::size> SquareMatrixState;
 	typedef matrix::SquareMatrix<float, 2> Matrix2f;
-	template<int ... Idxs>
-
-	using SparseVectorState = matrix::SparseVectorf<State::size, Idxs...>;
 
 	Ekf()
 	{
diff --git a/src/modules/ekf2/EKF/gps_yaw_fusion.cpp b/src/modules/ekf2/EKF/gps_yaw_fusion.cpp
index e8e6c2bfef..22bdfb4847 100644
--- a/src/modules/ekf2/EKF/gps_yaw_fusion.cpp
+++ b/src/modules/ekf2/EKF/gps_yaw_fusion.cpp
@@ -100,8 +100,6 @@ void Ekf::fuseGpsYaw()
 	sym::ComputeGnssYawPredInnovVarAndH(getStateAtFusionHorizonAsVector(), P, _gps_yaw_offset, gnss_yaw.observation_variance, FLT_EPSILON, &heading_pred, &heading_innov_var, &H);
 	}
 
-	const SparseVectorState<0,1,2,3> Hfusion(H);
-
 	// check if the innovation variance calculation is badly conditioned
 	if (gnss_yaw.innovation_variance < gnss_yaw.observation_variance) {
 		// the innovation variance contribution from the state covariances is negative which means the covariance matrix is badly conditioned
@@ -129,7 +127,7 @@ void Ekf::fuseGpsYaw()
 
 	// calculate the Kalman gains
 	// only calculate gains for states we are using
-	VectorState Kfusion = P * Hfusion / gnss_yaw.innovation_variance;
+	VectorState Kfusion = P * H / gnss_yaw.innovation_variance;
 
 	const bool is_fused = measurementUpdate(Kfusion, gnss_yaw.innovation_variance, gnss_yaw.innovation);
 	_fault_status.flags.bad_hdg = !is_fused;
diff --git a/src/modules/ekf2/EKF/mag_fusion.cpp b/src/modules/ekf2/EKF/mag_fusion.cpp
index 8d18e12f5b..f033750d11 100644
--- a/src/modules/ekf2/EKF/mag_fusion.cpp
+++ b/src/modules/ekf2/EKF/mag_fusion.cpp
@@ -62,11 +62,9 @@ bool Ekf::fuseMag(const Vector3f &mag, estimator_aid_source3d_s &aid_src_mag, bo
 	Vector3f innov_var;
 
 	// Observation jacobian and Kalman gain vectors
-	SparseVectorState<0,1,2,3,16,17,18,19,20,21> Hfusion;
 	VectorState H;
 	const VectorState state_vector = getStateAtFusionHorizonAsVector();
 	sym::ComputeMagInnovInnovVarAndHx(state_vector, P, mag, R_MAG, FLT_EPSILON, &mag_innov, &innov_var, &H);
-	Hfusion = H;
 
 	innov_var.copyTo(aid_src_mag.innovation_variance);
 
@@ -162,7 +160,6 @@ bool Ekf::fuseMag(const Vector3f &mag, estimator_aid_source3d_s &aid_src_mag, bo
 		} else if (index == 1) {
 			// recalculate innovation variance because state covariances have changed due to previous fusion (linearise using the same initial state for all axes)
 			sym::ComputeMagYInnovVarAndH(state_vector, P, R_MAG, FLT_EPSILON, &aid_src_mag.innovation_variance[index], &H);
-			Hfusion = H;
 
 			// recalculate innovation using the updated state
 			aid_src_mag.innovation[index] = _state.quat_nominal.rotateVectorInverse(_state.mag_I)(index) + _state.mag_B(index) - mag(index);
@@ -191,7 +188,6 @@ bool Ekf::fuseMag(const Vector3f &mag, estimator_aid_source3d_s &aid_src_mag, bo
 
 			// recalculate innovation variance because state covariances have changed due to previous fusion (linearise using the same initial state for all axes)
 			sym::ComputeMagZInnovVarAndH(state_vector, P, R_MAG, FLT_EPSILON, &aid_src_mag.innovation_variance[index], &H);
-			Hfusion = H;
 
 			// recalculate innovation using the updated state
 			aid_src_mag.innovation[index] = _state.quat_nominal.rotateVectorInverse(_state.mag_I)(index) + _state.mag_B(index) - mag(index);
@@ -212,7 +208,7 @@ bool Ekf::fuseMag(const Vector3f &mag, estimator_aid_source3d_s &aid_src_mag, bo
 			}
 		}
 
-		VectorState Kfusion = P * Hfusion / aid_src_mag.innovation_variance[index];
+		VectorState Kfusion = P * H / aid_src_mag.innovation_variance[index];
 
 		if (!update_all_states) {
 			for (unsigned row = 0; row <= 15; row++) {
@@ -275,10 +271,8 @@ bool Ekf::fuseDeclination(float decl_sigma)
 		return false;
 	}
 
-	SparseVectorState<16,17> Hfusion(H);
-
 	// Calculate the Kalman gains
-	VectorState Kfusion = P * Hfusion / innovation_variance;
+	VectorState Kfusion = P * H / innovation_variance;
 
 	const bool is_fused = measurementUpdate(Kfusion, innovation_variance, innovation);
 
diff --git a/src/modules/ekf2/EKF/optflow_fusion.cpp b/src/modules/ekf2/EKF/optflow_fusion.cpp
index e2f1379d06..88811ee9c2 100644
--- a/src/modules/ekf2/EKF/optflow_fusion.cpp
+++ b/src/modules/ekf2/EKF/optflow_fusion.cpp
@@ -146,8 +146,7 @@ void Ekf::fuseOptFlow()
 			}
 		}
 
-		SparseVectorState<0,1,2,3,4,5,6> Hfusion(H);
-		VectorState Kfusion = P * Hfusion / _aid_src_optical_flow.innovation_variance[index];
+		VectorState Kfusion = P * H / _aid_src_optical_flow.innovation_variance[index];
 
 		if (measurementUpdate(Kfusion, _aid_src_optical_flow.innovation_variance[index], _aid_src_optical_flow.innovation[index])) {
 			fused[index] = true;