diff --git a/matlab_simulation/05-estimation/EKF.m b/matlab_simulation/05-estimation/EKF.m
deleted file mode 100644
index b1db2ab..0000000
--- a/matlab_simulation/05-estimation/EKF.m
+++ /dev/null
@@ -1,19 +0,0 @@
-function [muP,mu,sig,K] = EKF(A,B,C,D,dt,y,u,mu,sig,positionE,measureE,n)
-%This function runs the Extended Kalman Filter with the following inputs:
-%A: state matrix, B: input matrix, C: measurement matrix, D: input
-%measurement matrix, y: measurment model, u: state inputs, n: number of
-%states
-
-%Prediction Update, muP equation based on example
-muP = A*mu + B*u*dt;   %The addition of the input matrix requires the B matrix to be used
-sigP = A*sig*A' + positionE;
-
-%Jacobian matrix to linearize, change this equation for each example
-Ht = [(muP(1))/(sqrt(muP(1)^2 + muP(2)^2)) (muP(2))/(sqrt(muP(1)^2 + muP(2)^2))];
-
-% Measurement update
-K = sigP*Ht'*inv(Ht*sigP*Ht'+ measureE);
-mu = muP + K*(y - sqrt(muP(1)^2 + muP(2)^2)); %change h(mu) equations based on example
-sig = (eye(n)-K*Ht)*sigP;
-end
-