Bearings-Only Filtering Using Uncorrelated Conversion Based Filters

Bearings-only filtering (BOF) is important in many practical applications. It is also a challenging nonlinear filtering problem due to limited information contained in highly nonlinear measurements. Researchers have proposed various nonlinear filters for BOF problems. We propose a new approach to nonlinear filtering using pseudomeasurement construction based uncorrelated conversion for BOF. This approach can more effectively utilize the measurement information than the original linear minimum mean square error estimator. The constructed pseudomeasurement is uncorrelated with the original measurement. Based on the recently proposed uncorrelated conversion based filter (UCF), we propose a UCF using pseudorange construction (UCF-PRC) for the BOF problem. An improved filter, the optimized UCF-PRC, is also proposed to minimize the mean square error. The effectiveness of the new filters is demonstrated by simulation results. Specifically, compared with the particle filter, the UCF-PRC has better estimation accuracy with nearly the same computational cost.