Linear optimizing particle filter for strapdown inertial navigation system initial alignment

Particle Filter (PF) is well known as a state estimation method for nonlinear and non-Gaussian system. However, PF has the inherent drawbacks of loss of diversity in particles due to sampling-importance resampling (SIR). The loss may lead to PF's worse performance, even making filtering diffuse. In this paper, a Linear Optimization Particle Filter (LOPF) based on linear optimization resample was proposed to overcome the drawbacks of the loss of diversity in particles. Based on the linear optimization resample, the new algorithm calculates the new particles in the next deviation according to the linear combination of the abandoned particles and copied particles. These particles were used to replace the copied particles by SIR to overcome the loss of diversity caused by resampling only from the particles with large weight. Then LOPF was applied to nonlinear initial alignment for the strapdown inertial navigation system with large azimuth. The simulation results demonstrate that LOPF is better than PF in the alignment time and alignment accuracy. Therefore LOPF is more suitable to the strapdown inertial navigation system's nonlinear initial alignment.