中文摘要：

该文针对信号相关杂波环境下的运动目标检测问题,研究一种稳健的慢时间发射波形和接收滤波器设计方法。首先,基于杂波2阶统计特性不确定时的最坏SINR（the Worst-case SINR,W-SINR）,建立非凸恒模约束下高维的发射-接收联合优化模型;然后,提出一种基于序列迭代的优化算法（Iterative Sequential Optimization,ISO）。每步迭代中,该算法将一个高维优化问题转化为多个1维分式规划问题,并通过丁克尔巴赫（Dinkelbach）方法进行求解。最后,仿真实验证明,ISO具有对抗不确定杂波信息的能力,使系统具有更好的适应能力;此外,相比半正定松弛（Semi-Definite Relaxation,SDR）与随机化方法,提出的算法在W-SINR优化值与计算复杂度上均具有明显的优势。

英文摘要：

In this paper, we focus on the detection of a moving point-like target embedded in uncertain signaldependent clutter and develop robust transmit-code and receive-filter designs in slow-time. First, based on the Worst-case Signal-to-Interference-plus-Noise Ratio （W-SINR） when the second-order clutter statistics are uncertain, we establish a high-dimensional transmit-receive optimization model that considers the constant modulus constraint with non-convexity. Next, we propose an Iterative Sequential Optimization （ISO） algorithm. At each iteration, it converts a high-dimensional optimization into multiple one-dimensional fractional programming problems that can be efficiently solved using Dinkelbach＇s method. Finally, we use numerical examples to confirm that the ISO can resist the uncertain knowledge of signal-dependent clutter, which enables the radar system to adapt to complicated environments. Moreover, compared to Semi-Definite Relaxation （SDR）-related and randomization methods, the proposed algorithm is superior with respect to both optimized W-SINR and computational time.