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.

中文翻译：

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使用基于不相关转换的过滤器进行仅方位过滤


仅轴承过滤 (BOF) 在许多实际应用中非常重要。由于高度非线性测量中包含的信息有限，这也是一个具有挑战性的非线性滤波问题。研究人员针对 BOF 问题提出了各种非线性滤波器。我们提出了一种使用基于 BOF 的伪测量构造的不相关转换的非线性滤波新方法。该方法比原始的线性最小均方误差估计器能够更有效地利用测量信息。构造的伪测量与原始测量不相关。基于最近提出的基于不相关转换的滤波器（UCF），我们针对 BOF 问题提出了使用伪距构造的 UCF（UCF-PRC）。还提出了一种改进的滤波器，即优化的 UCF-PRC，以最小化均方误差。模拟结果证明了新滤波器的有效性。具体来说，与粒子滤波器相比，UCF-PRC 在几乎相同的计算成本下具有更好的估计精度。