Continual Radar Intra-Pulse Modulation Recognition via Multiview Replay and Class-Balanced Classifier Fine-Tuning

Continually deployed radar recognizers must acquire emerging intra-pulse modulation classes without erasing learned signal types under small replay memories. We present MM-CILNet, a radar-specific multiview replay framework that combines complementary I/Q and short-time Fourier transform (STFT) representations, sample-wise fusion, class-balanced memory, and a one-epoch update of three classifier heads after each incremental session. On the 20-class CIL-20 protocol with 12 matched seeds, MM-CILNet improves final-session All and Old accuracy by 2.7 percentage points each over matched Replay (All: p=0.0034, Cohen's d=1.07; Old: p=0.0103, d=0.89). Under the shared configuration, it achieves the best All and Old accuracy among the listed radar-adapted CIL methods and the lowest average forgetting among the four methods with recoverable session histories. Equal-budget controls identify significant post-session effects on All and Old accuracy and show that trainable scope and sampling determine the retention-plasticity operating point. The Old-accuracy advantage persists at the tested 400- and 800-sample budgets and across five class orders. Under 2.5:1 recent-class-heavy replay, Old accuracy decreases by 1.63-2.01 pp and forgetting increases by 3.66-4.69 pp relative to class-balanced replay. These results establish class-balanced memory as the retention-oriented operating choice and provide an integrated protocol for memory-constrained continual radar recognition.