Multisensor systems are essential for autonomous navigation applications to achieve reliable accuracy. Integrating the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) is the most common integration scheme. However, this integration is unreliable in different scenarios since the GNSS signal may deteriorate in downtown areas or suffer from a blockage in underground and indoor areas. Therefore, other sensors are integrated with INS to compensate for GNSS outages. This paper proposes a novel algorithm for radar/INS tightly-coupled integration for autonomous navigation applications. This algorithm is applied in multiple steps. Radar data analysis is the first and most crucial step to remove the noisy data and the outliers and keep the useful objects. Then, data association is done to match the detected objects between radar frames. The tightly-coupled integration is performed at the measurement level through an Extended Kalman Filter (EKF), where the distance between the INS and the detected objects can be predicted from the INS and measured from the radar. Real data was collected from four Frequency Modulated Continuous Wave (FMCW) radar units in Calgary's suburban areas and Toronto's downtown area. The proposed algorithm was tested and assessed by introducing simulated GNSS single outages with different durations. The results show an enhancement in the vehicle's position by about 94% to 96%.