Sonar’s Third Paradox: Feature Similarity and Dimensional Inefficacy in Recognition

Sonar detection and recognition in adversarial underwater environments face a fundamental dilemma due to the target’s absolute disadvantages in both quantity and signal strength, compounded by environmental uncertainty and the intrinsic unmeasurability of the target itself. The conventional paradigm—based on “energy/feature detection followed by feature-based classification”—relies critically on the assumption that “increasing feature dimensionality improves performance.” However, this assumption completely breaks down when the features of targets, interferences, and the environment exhibit essential similarity, leading paradoxically to degraded overall detection and recognition performance. We formally identify this phenomenon as “Sonar’s Third Paradox.”This paper deeply unpacks the paradox through three interrelated sub-paradoxes across different levels:the “False-Detection Paradox” (manifestation level),the “Feature Dimensionality Inefficacy Paradox” (methodological level), andthe “Static Cognitive Confrontation Paradox” (system level).We further trace their origins to four foundational roots: philosophical presuppositions, physical mechanisms, adversarial environmental dynamics, and methodological limitations.To fundamentally resolve this impasse, we propose a systematic solution framework. First, at the meta-philosophical level, we advocate a paradigm shift—from an omniscient “God’s-eye view” to an embodied “agent-centric perspective”—redefining the system’s objective from seeking deterministic decisions to actively managing unmeasurability. Then, at the technical implementation level, we introduce three coordinated strategies: 1. Constructing a cognitive state vector coupled with a cautious decision-making mechanism to eliminate false detections; 2. Reversing dimensional inefficacy through physics-informed lean feature engineering combined with Bayesian learning; 3. Transcending static confrontation via online adaptive learning and an active cognitive closed-loop.This research establishes a novel theoretical framework and a practical technical blueprint for developing next-generation intelligent sonar systems endowed with dynamic cognition and strategic reasoning capabilities.