This paper investigates the problem of high-precision target localization in integrated sensing and communication (ISAC) systems, where the target is sensed via both a direct path and a reconfigurable intelligent surface (RIS)-assisted reflection path. We first develop a sequential matched-filter estimator to acquire coarse angular parameters, followed by a range recovery process based on subcarrier phase differences. Subsequently, we formulate the target localization problem as a non-linear least squares optimization, using the coarse estimates to initialize the target's position coordinates. To solve this efficiently, we introduce a fast iterative refinement algorithm tailored for RIS-aided ISAC environments. Recognizing that the signal model involves both linear path gains and non-linear geometric dependencies, we exploit the separable least-squares structure to decouple these parameters. Furthermore, we propose a modified Levenberg algorithm with an approximation strategy, which enables low-cost parameter updates without necessitating repeated evaluations of the full non-linear model. Simulation results show that the proposed refinement method achieves accuracy comparable to conventional approaches, while significantly reducing algorithmic complexity.

翻译：本文研究了集成感知与通信（ISAC）系统中的高精度目标定位问题，其中目标通过直达路径和可重构智能表面（RIS）辅助的反射路径进行感知。我们首先开发了一种顺序匹配滤波器估计器来获取粗略的角参数，随后基于子载波相位差进行距离恢复处理。接着，我们将目标定位问题构建为非线性最小二乘优化，利用粗略估计值初始化目标的位置坐标。为高效求解该问题，我们提出了一种专为RIS辅助的ISAC环境设计的快速迭代优化算法。考虑到信号模型同时包含线性路径增益和非线性几何依赖关系，我们利用可分离最小二乘结构对这些参数进行解耦。此外，我们提出了一种采用近似策略的改进Levenberg算法，该算法能够实现低成本的参数更新，而无需重复计算完整的非线性模型。仿真结果表明，所提出的优化方法在达到与传统方法相当精度的同时，显著降低了算法复杂度。