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The probability hypothesis density (PHD) filter is widely used to solve multi-target tracking (MTT) problems. Although the Sequential Monte Carlo (SMC) implementation provides a tractable solution for PHD filter to handle the highly nonlinear and non-Gaussian MTT scenario, the high computational cost caused by a large number of particles limits the applications that need to be performed in real-time...
Particle flow filters have been recently developed as an alternative approach for nonlinear filtering. The particles approximating the prior are migrated using differential equations to be distributed according to the posterior. Computationally tractable exact solutions only exist for linear Gaussian models. For other scenarios, approximations are required and it is not fully understood how these...
Particle probability hypothesis density filtering has become a tractable means for multi-target tracking due to its capability of handling an unknown and time-varying number of targets in non-linear or non-Gaussian system in the presence of clutter and missing measurements. However, it is time-consuming because hundreds of thousands of particles are required to reach a satisfactory tracking accuracy,...
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