Grid based PHD filtering by Fast Fourier Transform

We propose an approach to calculate the Probability Hypothesis Density function on a numerical grid by using a method based on the convolution theorem and Fast Fourier transform. This approach provides a representation of the PHD over a discretized domain and, unlike other techniques, does not require Gaussian assumptions on the target and observation model. By using the Fast Fourier Transform it results reasonably competitive in comparison to existing implementations, especially in low dimensional state spaces.