The CNN-SE-LSTM Model for Phase Interferometer Ambiguity Resolution

To address the phase ambiguity problem arising from baseline extension in phase interferometer for improving direction-finding accuracy, this paper proposes a CNN-SE-LSTM model-based phase ambiguity resolution method. By analyzing the mapping relationship between the phase differences and phase ambiguity values, a multidimensional phase differences dataset is constructed. The method employs a Convolutional Neural Network (CNN) to extract spatially localized features, a Squeeze-and-Excitation (SE) block for adaptive feature weight recalibration, and a Long Short-Term Memory (LSTM) network to capture temporal dependencies within features. This approach effectively overcomes the reliance of conventional methods on specific baseline configurations. Experimental results demonstrate a 72% improvement in the success rate of ambiguity resolution compared to existing methods at 0 dB signal-to-noise ratio (SNR) across varying array geometries. The proposed method provides an innovative solution for phase interferometer ambiguity resolution challenge.