Accurate target parameter estimation of range, velocity, and angle is essential for vehicle safety in advanced driver assistance systems (ADAS) and autonomous vehicles. To enable spectrum sharing, ADAS may employ integrated sensing and communications (ISAC). This paper examines a dual-deconvolution automotive ISAC scenario where the radar waveform is known but the propagation channel is not, while in the communications domain, the channel is known but the transmitted message is not. Conventional maximum likelihood (ML) estimation for automotive target parameters is computationally demanding. To address this, we propose a low-complexity approach using the controlled loosening-up (CLuP) algorithm, which employs iterative refinement for efficient separation and estimation of radar targets. We achieve this through a nuclear norm restriction that stabilizes the problem. Numerical experiments demonstrate the robustness of this approach under high-mobility and noisy automotive environments, highlighting CLuP's potential as a scalable, real-time solution for ISAC in future vehicular networks.

翻译：在高级驾驶辅助系统（ADAS）和自动驾驶车辆中，对距离、速度和角度等目标参数的精确估计对车辆安全至关重要。为实现频谱共享，ADAS可采用集成感知与通信（ISAC）技术。本文研究了一种双重解卷积的汽车ISAC场景：在雷达域中，波形已知但传播信道未知；而在通信域中，信道已知但发送消息未知。针对汽车目标参数的传统最大似然（ML）估计计算复杂度高。为解决此问题，我们提出了一种基于受控松弛（CLuP）算法的低复杂度方法，该方法通过迭代优化实现雷达目标的高效分离与估计。我们通过引入核范数约束来稳定问题。数值实验表明，该方法在高机动性和噪声干扰的汽车环境中具有鲁棒性，凸显了CLuP作为未来车载网络中可扩展、实时ISAC解决方案的潜力。