Navigation systems are the first things to have for all types of unmanned vehicles whether it is land, water, or air vehicles. Navigation systems are divided into independent and leader-follower. Today, Global Positioning System (GPS) plays an important role for vehicle positioning. Nevertheless, GPS has a weakness that the signals will be distorted when the GPS receivers are placed in indoor environments. In this research, leader-follower navigation systems based on pedestrian dead reckoning are designed with wheeled mobile robot as the follower. Artificial Neural Network (ANN) is used as stride length model and the leader heading is obtained from magnetometer. Both data are sent to the follower through Wi-Fi. For follower system, linear model is used as robot distance model and follower heading is obtained from its magnetometer. The test shows that the best model for stride length prediction is ANN model with one hidden layer and four neuron units which has 4.65 cm training error and 5.04 cm testing error using 614 stride examples. As for robot distance model shows that the error is 2.0956 cm using 25 data samples. Finally, the heading error is 39.262° which is tested from two magnetometers with 28 testing points.