The Probability Hypothesis Density (PHD) Filter was developed as a method for tracking a time varying number of targets without data association. The first order statistical moment of the multiple target posterior distribution called the Probability Hypothesis Density which is represented by discrete samples or particles gives the expected locations of the targets. This property is used instead of the full multi-target posterior distribution as it is requires significantly less computation and particle filter implementations have demonstrated the potential of the algorithm to be used for real-time tracking applications. In this article, an application of the Particle PHD Filter is demonstrated to track a variable number of objects in three-dimensional sonar images estimating both the number of targets and their locations. The number of targets is estimated at each iteration by computing the mass of the particle weights. The locations of the targets are determined by extracting peaks of the PHD which is a distinct task from the computation of the particles. Previous approaches have used the Expectation Maximisation (EM) algorithm to fit a Gaussian mixture model whose time complexity is quadratic in the number of targets which is not ideal for a real-time tracking application and so alternative clustering techniques are considered here. A comparison is made between the methods for the accuracy of estimation, robustness and the time taken. © 2005 IEEE.
|Title of host publication||Oceans 2005 - Europe|
|Number of pages||6|
|Publication status||Published - 2005|
|Event||Oceans 2005 - Europe - Brest, France|
Duration: 20 Jun 2005 → 23 Jun 2005
|Conference||Oceans 2005 - Europe|
|Period||20/06/05 → 23/06/05|