Particle filtering techniques have captured the attention of many researchers in various communities, including those in signal processing, communication and image processing. Particle filtering is particularly useful in dealing with nonlinear state space models and non-Gaussian probability density functions. The underlying principle of the methodology is the approximation of relevant distributions with random measures composed of particles (samples from the space of the unknowns) and their associated weights. This dissertation makes three main contributions in the field of particle filtering. The first problem deals with target tracking in radar signal processing. The second problem deals with object tracking in video. The third problem deals with estimating error bounds for particle filtering based symbol estimation in communication systems.