Effects of the receiver characteristics, variation of target intensity and nonlinearity on probability hypothesis density filter compared to data association techniques

Target tracking algorithms adopted in modern radars are designed such that they can track multitarget by considering target births and target deaths. These algorithms are derived by integrating the data association techniques into the single target filters. Recently, target tracking methods exploiting random finite sets have been emerged as an alternative to the data association techniques. Unlike data association methods, random finite set based techniques do not perform tracking based on the targets but instead propagate a target intensity function covering the entire state space in time and thereby decrease the dimension of the state space. In this thesis, firstly on a linear scenario we investigate the effects of receiver characteristics and variation of target intensity on the performance of PHD filter that is a random finite set based filter. The parameters we consider for receiver characteristics are detection probability and false alarm intensity; for variation of target intensity we investigate the effect of target birth and death probabilities. We also provide a linear regression model representing effects of these parameters. As a tracking performance metric we use OSPA distance. At each step, we compare our results with a data association method, global nearest neighbor technique, in order to identify the advantages and disadvantages of the both of the methods. Secondly we investigate the effect of the nonlinear models on both of the methods. By fixing the parameters to the values that results in equal average OSPA distances of both techniques in the linear case, we include nonlinear models in order to identify which technique is effected by nonlinearity more.