The integrated particle filter (IPF) is an algorithm for single-target tracking in clutter, which incorporates the probability of target existence (PTE) into the traditional particle filter as a track quality measure for false track discrimination (FTD). This study investigates two IPF-based fixed-interval smoothing algorithms: the IP smoothing (IPS) algorithm and the IP-Rauch–Tung–Striebel backward smoothing (IP-RTSBS) algorithm, both of which are capable of trajectory estimation and FTD. The IPS algorithm fuses the propagations for each pair of forward IPF and backward IPF particles to obtain the smoothing propagation that is used to update the track state by applying all available measurements in the current scan. The IP-RTSBS algorithm employs the forward filtering backward smoothing approach to smooth the trajectory state, which is then applied to the RTS smoothing methodology to obtain the smoothing propagation used to update the PTE. As a result, both FTD and trajectory estimation are improved. The smoothing benefits of the two algorithms are validated in the simulations, where a sliding batch mode with overlapping measurements is utilised to limit the smoothing lag.