Application of particle filter algorithm based on chaotic sequences and improved t-distribution in UWB indoor positioning

Ultra-wideband (UWB) technology has significant application value in indoor autonomous navigation and robot path planning. However, noise interference in complex environments often causes anomalies in UWB positioning data, severely reducing positioning accuracy and stability. A particle filter algorithm (LTPF) based on chaotic sequences and an improved t-distribution is proposed to address this issue. This algorithm applies a linear transformation to the Logistic map to generate chaotic sequences, thereby enhancing particle diversity and distribution. Additionally, it uses standardized residuals to improve the t-distribution and construct an observation likelihood function, thereby reducing the impact of noise points and outlier data. Experimental results show that compared to the PF, EKPF, KGAPF, and SKF-DMM algorithms, the LTPF algorithm achieves average positioning accuracy improvements of \(47.80\%\), \(40.53\%\), \(43.73\%\), and \(50.10\%\), respectively, effectively enhancing positioning accuracy and stability. Additionally, its computational complexity is equivalent to the PF algorithm, making it a viable method for UWB positioning in complex indoor environments.