Novel Enhanced UPF Particle Filter Algorithm

A novel enhanced Unscented Particle Filter (UPF) algorithm significantly improves computational efficiency through structural optimization. While traditional UPF delivers excellent accuracy in nonlinear system state estimation, its computational complexity limits applications in real-time scenarios. This improved solution balances performance and time consumption through the following mechanisms:

**Optimized Importance Sampling**: The proposal distribution generation process is restructured to reduce computational redundancy from ineffective particles while maintaining the ability to capture system nonlinear characteristics. In code implementation, this involves recalculating particle weights using an enhanced Gaussian mixture model and implementing selective particle propagation based on covariance thresholds. **Adaptive Resampling Strategy**: Resampling is triggered only when severe particle degeneration occurs, avoiding unnecessary computations from fixed-interval approaches. Algorithmically, this is implemented by continuously monitoring the effective sample size (ESS) and setting dynamic thresholds using the Neff/N ratio calculation. **Parallelized Preprocessing**: The Sigma point calculations in the Unscented Transform (UT) undergo task decomposition to leverage hardware acceleration for reduced time consumption. This can be coded using parallel computing frameworks that distribute the sigma point generation and nonlinear transformation across multiple processing units.

Practical testing demonstrates that the enhanced algorithm reduces runtime by 30%-50% while maintaining localization accuracy comparable to the original UPF in typical scenarios like target tracking. These characteristics make it particularly suitable for real-time-critical applications such as UAV navigation and industrial sensor fusion systems.