In this thesis, we study ranging algorithms in an indoor environment using narrow-band industrial, scientific, and medical (ISM) radio bands at 2.4 GHz. Previously, a phase difference approach implemented for this problem. However, the distance estimation is rather inaccurate for indoor ranging, mainly due to multipath and noise. This thesis studies several direction of arrival (DOA) techniques such as matched filter (MF), minimum variance distortionless response (MVDR), and multiple signal classification (MUSIC) to reduce the impact of indoor multipath. Forward-backward smoothing and Akaike information criterion - minimum descriptive length (AIC-MDL) also proposed to diminish the multipath effect further and estimates the number of separable multipath in the channel. Besides, MUSIC-like is discussed to prevent incorrect estimation number of sources. We test the proposed algorithm under different channel parameter value, compensate the bias, and show the performance improvement as the absolute bias value reduced up an order of magnitude.