To improve the precision of probability hypothesis density(PHD) filter when dealing with the problem of multiple
target tracking, an implementation method of PHD filter based on quasi-Monte Carlo is proposed. This PHD filter algorithm
uses the property of more regularly distribution of low discrepancy points and makes the sampling particles away from
each other. Thus it can more fully describe the posterior probability distribution function and more accurately compute the
estimate value of the target number and the state of individual target according to the particles with corresponding weights.
Simulation results show that the modified algorithm is effective, and the estimation accuracy is superior to particle PHD filter
algorithm.