Distributed quantized detection fusion method independent of underwater acoustic sensor′s signal-to-noise ratio

To meet the needs of signal detection and fusion in underwater cross-platform collaborative systems, a new distributed quantized detection fusion method is proposed. This method is independent of prior information on the acoustic sensor′s signal-to-noise ratio. It not only enhances the performance of the detection fusion system but also avoids the need to know the signal-to-noise ratio of each sensor. First, using the Neyman-Pearson criterion, we derive the globally optimal algorithm for quantized detection fusion. This algorithm ensures that the detection fusion system meets the false alarm probability requirements. By jointly designing the detection fusion rules and individual sensor decision rules, we achieve the optimal decision for multiple quantized judgments. Subsequently, we introduce a suboptimal algorithm for quantized detection fusion. In this algorithm, the fusion center decision threshold and sensor decision thresholds are designed to depend only on the false alarm probability closely related to noise variance rather than the signal-to-noise ratio. This simplifies the process by using independent fusion rules and sensor decision rules. Finally, computer simulations show that the proposed algorithm outperforms the detection capabilities of individual sensors participating in fusion and globally optimal hard-decision fusion. The performance of the suboptimal quantized detection fusion algorithm closely matches that of the globally optimal quantized detection fusion algorithm. © 2024 Editorial Board of Journal of Harbin Engineering. All rights reserved.