Study employs polynomial fitting to enhance localization accuracy in low-SNR conditions, indicating significant improvements.
In passive localization, the synthetic aperture positioning (SAP) method enables high-precision positioning under low signal-to-noise ratio (SNR) conditions. However, higher-order phase errors induced by platform self-localization errors degrade image focusing and reduce localization accuracy. In this paper, a polynomial fitting approach based on designing optimal pre-whitening filters using autoregressive models and employing iteratively reweighted least squares (IRLS) is applied to the unwrapped phase to eliminate higher-order error components. Additionally, a multiple sub-aperture phase stitching method is proposed to mitigate phase susceptibility to noise interference and error accumulation during phase unwrapping. The effectiveness of the proposed method is validated through both simulations and UAV experiments. Results demonstrate that meter-level localization accuracy can be achieved for the emitter target.
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Sun et al. (2025) studied this question.
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