Application of Self-Adaptive Artificial Physics Optimized Particle Filter in INS/Gravity Gradient Aided Navigation

The artificial physics optimized particle filter (APO-PF) algorithm has some defectives, such as slowly converging, acquiring positioning inaccurately and so on. A new self-adaptive artificial physics optimized particle filter (SAPO-PF) algorithm is proposed to overcome these shortcomings through improving position update expression, adding new mechanical rules and keeping back elite particle by elitist selection. The algorithm adjusts the particle moving step with time and improves the posterior probability distribution. The proposed method is applied to the INS/gravity gradient aided navigation by combining the sea experiment data of an inertial navigation system. Compared with APO-PF and PSO-PF, the SAPO-PF has faster convergence speed and higher positioning accuracy.