Hybrid ISAC-LSTM Architecture for Enhanced Target Tracking in Integrated Sensing and Communication Systems: A Symmetric Dual-Function Framework

Target tracking in integrated sensing and communication (ISAC) systems faces critical challenges due to complex interference patterns and dynamic resource allocation between radar sensing and wireless communication functions. Classical tracking algorithms struggle with the non-Gaussian noise characteristics inherent in ISAC environments. This paper addresses these limitations through a novel hybrid ISAC-LSTM architecture that enhances Extended Kalman Filter performance using intelligent machine learning corrections. The approach processes comprehensive feature vectors including baseline EKF states, ISAC-specific interference indicators, and innovation-based statistical occlusion detection. ISAC systems exhibit fundamental symmetry through dual sensing–communication operations sharing identical spectral and hardware resources, requiring balanced resource allocation, where αsensing+αcomm=1. The proposed hybrid architecture preserves this functional symmetry while achieving balanced performance across symmetric dual evaluation scenarios (normal and extreme conditions). Comprehensive evaluation across three realistic deployment scenarios demonstrates substantial performance improvements, achieving 21–24% RMSE reductions over classical methods (3.5–3.6 m vs. 4.6 m) with statistical significance confirmed through paired t-tests and cross-validation. The hybrid system incorporates fail-safe mechanisms ensuring reliable operation when machine learning components encounter errors, addressing critical deployment concerns for practical ISAC applications.