diff --git a/matlab/EKF_replay/Filter/SetParameterDefaults.m b/matlab/EKF_replay/Filter/SetParameterDefaults.m
index 42553e3817..dc4e99e887 100644
--- a/matlab/EKF_replay/Filter/SetParameterDefaults.m
+++ b/matlab/EKF_replay/Filter/SetParameterDefaults.m
@@ -36,7 +36,7 @@ param.fusion.magDeclDeg = 10.5; % Magnetic declination in deg
 %% Optical flow measurement fusion
 param.fusion.rangeTimeDelay = 0.05; % range fidner sensor delay relative to IMU (sec)
 param.fusion.flowTimeDelay = 0.05; % Optical flow sensor time delay relative to IMU (sec)
-param.fusion.flowRateError = 0.5; % Observation noise 1SD for the flow sensor (rad/sec)
+param.fusion.flowRateError = 0.1; % Observation noise 1SD for the flow sensor (rad/sec)
 param.fusion.flowGate = 5.0; % Size of the optical flow rate innovation consistency check gate in SD
 param.fusion.rngValidMin = 0.05; % range measurements wil be constrained to be  no less than this (m)
 param.fusion.rngValidMax = 5.0; % ignore range measurements larger than this (m)
diff --git a/matlab/EKF_replay/readme.txt b/matlab/EKF_replay/readme.txt
index 947419e072..5628ee3020 100644
--- a/matlab/EKF_replay/readme.txt
+++ b/matlab/EKF_replay/readme.txt
@@ -30,6 +30,7 @@ Copy the generated .mat files into the /EKF_replay/TestData/APM directory.
 
 Collect data with EK2_REC_RPL = 1
 Convert the .ulg log file to .csv files using the PX4/pyulog python script https://github.com/PX4/pyulog/blob/master/pyulog/ulog2csv.py
+
 Import the .csv file containing the sensor_combined_0 data into the matlab workspace and process it using …/EKF_replay/Common/convert_px4_sensor_combined_csv_data.m. This will generate the following data files:
 
 imu_data.mat
@@ -38,13 +39,22 @@ mag_data.mat
 
 Import the .csv file containing the vehicle_gps_position_0 data into the matlab workspace and process it using …/EKF_replay/Common/convert_px4_vehicle_gps_position_csv. This will generate the gps_data.mat file.
 
+If you have an optical flow and range finder sensor fitted:
+
+Import the .csv file containing the optical_flow_0 data into the matlab workspace and process it using …/EKF_replay/Common/convert_px4_optical_flow_csv_data.m.
+Import the .csv file containing the distance_sensor_0 data into the matlab workspace and process it using …/EKF_replay/Common/convert_px4_distance_sensor_csv_data.m.
+This will generate the following data files:
+
+flow_data.mat
+rng_data.mat
+
 Copy the generated .mat files into the /EKF_replay/TestData/PX4 directory.
 
 4) Make ‘…/EKF_replay/Filter’ the working directory.
 
 5) Execute ‘SetParameterDefaults’ at the command prompt to load the default filter tuning parameter struct ‘param’ into the workspace. The defaults have been set to provide robust estimation across the entire data set, not optimised for accuracy.
 
-6) Replay the data by running either the replay_apm_data.m or replay_px4_data.m script files. 
+6) Replay the data by running either the replay_apm_data.m, replay_px4_data.m or if you have px4 optical flow data, the replay_px4_optflow_data.m script file. 
 
 Output plots are saved as .png files in the ‘…/EKF_replay/OutputPlots/‘ directory.