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Multi-emitters Direct Localization Method via Multi-dictionaries and Hierarchical Block Sparse Bayesian Framework
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摘要: 基于压缩感知的直接定位方法依赖准确的信号传播模型，当传播模型的参数部分未知时，其定位性能会显著下降。针对这个问题，该文提出了一种基于多字典联合与分层块稀疏贝叶斯框架的多辐射源直接定位方法。该文将辐射源定位问题转化为恢复对应不同字典但具有共享稀疏性的信号，通过多字典联合来解决存在信道衰减的辐射源定位问题。仿真结果表明：所提方法在低信噪比和少快拍条件下，相比稀疏贝叶斯方法和直接定位方法具有更优的定位性能。Abstract: The direct position determination method based on compressed sensing depends on the accurate signal propagation model. With partially unknown propagation model parameters, its location performance will decline significantly. Thus, this study proposed a localization method via multi-dictionaries and hierarchical block sparse Bayesian framework. Herein, the emitter location problem is transformed into recovering signals from different dictionaries but with shared sparsity, and the emitter location with channel attenuation is solved by a multi-dictionary combination. Simulation results revealed that the algorithm has better performance than the traditional Sparse Bayesian Learning (SBL) method and Direct Position Determination (DPD) method under the condition of low signal-to-noise ratio and a few snapshots.
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图 1 基于Laplace先验的分层块稀疏贝叶斯模型图
Figure 1. The figure of hierarchical block sparse Bayesian model based on Laplace prior
图 2 所提算法对辐射源的定位效果示意图
Figure 2. Localization result of emitters using the proposed method
图 3 单辐射源下不同定位方法定位结果RMSE随信噪比变化关系
Figure 3. Localization RMSE versus SNR using different methods with single emitter
图 4 不同调制形式的辐射源定位结果RMSE随信噪比变化关系
Figure 4. Localization RMSE versus SNR for the radiation source with different modulations
图 5 不同定位方法定位结果RMSE随信噪比变化关系
Figure 5. Localization RMSE versus SNR using different methods
图 6 不同定位方法定位结果RMSE随快拍数变化关系
Figure 6. Localization RMSE versus number of snapshots using different methods
图 7 不同定位方法定位结果RMSE随阵元数变化关系
Figure 7. Localization RMSE versus number of elements using different methods
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