Improved Particle Filter Algorithm Utilizing Sonar Information

In robotic self-localization applications, particle filter algorithms based on sonar information represent a widely adopted approach. This algorithm achieves localization by processing sonar readings and incomplete environmental data. Our improved implementation introduces enhancements to the conventional particle filter framework, particularly through optimized importance sampling and adaptive resampling strategies. Key code modifications include implementing a more sophisticated likelihood function that better models sonar measurement uncertainties and a dynamic particle redistribution mechanism that maintains diversity while reducing degeneracy. Experimental validation demonstrates significant improvements in both localization precision and algorithm robustness compared to baseline methods. Future research directions include extending this algorithm to autonomous vehicle navigation and smart home localization systems, with potential adaptations for multi-sensor fusion implementations.