The requirement for Terrain Referenced Navigation (TRN) also known as Terrain Aided Navigation (TAN) arises when Global Navigation Satellite System (GNSS) signals are unavailable, jammed or blocked. In recent years, research on the application of TRN to aerial and underwater vehicles has been increased rapidly with the developments in the accuracy of digital terrain elevation database (DTED). Since the land and sea floor profiles are inherently nonlinear, TRN becomes a nonlinear estimation problem. Because of the highly nonlinear and non-Gaussian problem, linear or linearized estimation techniques such as Kalman or Extended Kalman Filter (EKF) do not work properly for many terrain profiles. Hence, this paper focuses on the Sequential Monte Carlo (SMC) method namely the particle filter (PF) for dealing with nonlinearities and different types of probability distributions even multi modal. Two different PF implementations are studied, Sequential Importance Sampling with effective resampling (SIS-R) and Sampling Importance Resampling (SIR). The major difference between these implementations is that in SIS-R algorithm, resampling is only performed using effective sample size when actually required. Therefore, the SIS-R algorithm has less computational demands than the SIR algorithm for a given number of particles. Both algorithms are tested for an aircraft scenario over a commercial map. Simulations with different number of particles and inertial measurement units (IMUs) having various error measures are performed and investigated. Monte Carlo (MC) simulations show that both algorithms yield similar results in terms of horizontal position accuracy and convergence. Hereby with this paper, it is presented that besides requiring less computational load, the SIS-R implementation works accurately on TRN problem.