In wireless communications, a joint channel coefficient and time-delay tracking technique are a critical issue. Due to the highly nonlinear nature of time delay estimation, Particle filter (PF) and Sigma Point Particle filter (SPPF) can be employed. The SPPF algorithm consists of a particle filter that uses an Sigma Point Kalman filter (SPKF) to generate the importance proposal distribution. The SPKF allows the particle filter to incorporate the latest observations into a prior updating routine. In addition, the SPKF generates proposal distributions that match the true posterior more closely. We propose an SPPF based on algorithm for the estimation of closely-spaced path delays and related channel coefficients in CDMA environments. We show that the parameter estimation using this filter structure is very effective even in the non-orthogonality of spreading codes and under imperfect power control in the CDMA environments. The simulation results show that this filter outperforms the PF including conventional Kalman filters.