AI 中文总结
研究针对多频段ISAC中现有双频段感知方法局限，提出DMSNet进行联合目标数量和参数估计，相比最佳基线提升了目标数量估计准确率及参数估计精度，还大幅减少运行时间。
AI 中文摘要
多频段综合感知与通信（ISAC）为多目标感知提供了互补的高频和低频回波信息。然而，现有的双频段ISAC感知方法在利用异构频段的深度互补信息方面能力有限，且计算成本高。为解决这些限制，我们提出了双频段多目标感知神经网络（DMSNet）用于联合目标数量和参数估计。在代表性模拟条件下，DMSNet在目标数量估计上优于最佳基线，计数准确率从89.01%提高到91.74%，宏F1从90.80%提高到93.07%。在参数估计方面，与最佳基线相比，DMSNet将距离、速度和角度的中位数绝对误差分别降低了82.2%、56.9%和73.2%。此外，DMSNet相对于现有最快的双频段ISAC感知方法运行时间减少了68.7%。
英文摘要
Multi-band integrated sensing and communication (ISAC) offers complementary high- and low-frequency echo information for multi-target sensing. However, existing dual-band ISAC sensing methods have a limited ability to exploit deep complementary information across heterogeneous bands and often incur high computational costs. To address these limitations, we propose a Dual-Band Multi-Target Sensing Neural Network (DMSNet) for joint target number and parameter estimation. Under representative simulation conditions, DMSNet outperforms the best baseline in target number estimation, increasing count accuracy from 89.01 % to 91.74 % and Macro-F1 from 90.80 % to 93.07 %. For parameter estimation, compared with the best baselines, DMSNet reduces the median absolute errors of range, velocity, and angle by 82.2%, 56.9%, and 73.2%, respectively. Moreover, DMSNet reduces runtime by 68.7 % relative to the fastest existing dual-band ISAC sensing method.
Comments5 pages, 4 figures, 3 tables, submitted to IEEE Wireless Communications Letters
