Space-Time Joint Clutter Suppression Technology for ISAC Sensing Echo Signals
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摘要: 针对通感一体化感知信号处理中强静态杂波易掩盖“低慢小”目标，以及传统动目标显示(MTI)方法在低速场景下易损伤目标回波和微多普勒特征的问题，该文提出了一种空-时联合杂波抑制方法。该方法利用杂波角度图(CLAM)构建空域投影算子，实现强静态杂波抑制；针对目标与杂波角度接近时空域抑制易将目标一并压制的问题， 进一步结合候选角度遍历与Taubin拟合，对慢时域残余直流偏置进行估计和消除。仿真结果表明，所提方法相比传统MTI方法，在低速目标场景下具有更低的角度估计误差，并能较好地保留目标微多普勒特征。Abstract:
Objective The rapid development of low-altitude logistics, urban air mobility, and security monitoring requires Integrated Sensing and Communication (ISAC) systems to detect weak uncooperative “low, slow, and small” (LSS) targets while reusing communication waveforms and hardware. In practical low-altitude environments, echoes from buildings, towers, and ground facilities may be much stronger than target echoes and mask targets during range, angle, and Doppler processing. Static clutter exhibits stable directions of arrival and near-zero-Doppler slow-time components. Conventional moving target indication (MTI) suppresses zero-frequency clutter but may attenuate low-speed targets and distort micro-Doppler modulation. Spatial nulling based on clutter angles preserves low-Doppler information, but it may remove a target together with clutter when their angles are close. Neither approach alone can simultaneously ensure strong clutter rejection, low-speed target preservation, and robustness to angular overlap. Therefore, this study proposes a joint spatial–slow-time clutter suppression method that exploits environmental angular prior information while preserving weak low-speed target signatures. Methods After reception of the MIMO-OFDM sensing echo, down-conversion, digitization, cyclic-prefix removal, and FFT processing are performed. Element-wise division by the transmitted communication symbols removes data modulation, and measurements over array elements, subcarriers, and slow-time symbols are stacked into a three-dimensional sensing data matrix. The proposed method comprises offline clutter-angle-map construction and online two-stage suppression. During the offline stage, an inverse FFT along the subcarrier dimension separates range cells in pure clutter measurements. Strong clutter cells are selected, their slow-time snapshots construct spatial covariance matrices, and two-dimensional MUSIC estimates the azimuth and elevation angles of dominant static scatterers. These angles are stored in a clutter angle map (CLAM) as reusable environmental prior information. During online sensing, steering vectors associated with the stored angles form a clutter spatial manifold matrix, from which an orthogonal projection operator is derived to remove spatially separable static clutter. To maintain model consistency, steering vectors for subsequent angle estimation are projected using the same operator. When a target lies close to a stored clutter direction, the CLAM angles are examined individually. For each candidate direction, the remaining CLAM directions are treated as interference and suppressed using a normalized local orthogonal projection, while the current direction is retained. This traversal avoids prematurely removing an angularly overlapped target and provides a candidate slow-time sequence for further discrimination. The residual sequence of each subcarrier is mapped onto the complex I/Q plane, where residual static clutter appears as an approximately fixed DC offset and a moving target forms a circular arc due to slow-time phase evolution. Because low target speeds and finite observation intervals may produce only short arcs, Taubin circle fitting estimates the circle center through a normalized generalized eigenvalue problem. The estimated center represents the residual clutter bias and is subtracted from the complex slow-time samples. Thus, CLAM-guided global projection removes spatially separable clutter, while local angular traversal and circle fitting handle angular overlap and residual slow-time clutter without modifying the target phase trajectory. Results and Discussions MIMO-OFDM simulations under the ISAC architecture verify the proposed method in several representative scenarios. When target and clutter angles are separated, the method preserves distinct target peaks in the two-dimensional angular spectrum, and the root mean square errors (RMSEs) of azimuth and elevation estimation are substantially lower than those of conventional MTI ( Fig. 4 ). When target and clutter angles are close, spatial filtering alone may suppress the target together with the clutter and cause missed detection (Fig. 5 ). The proposed local traversal and circle-fitting stage resolves this limitation and, at a low target speed of 2 m/s, reduces the angle-estimation RMSE to a level close to the ideal clutter-free result, outperforming both MTI and CLAM+FFT (Fig. 6 ). It also exhibits lower sensitivity to changes in subcarrier spacing than MTI, indicating stronger robustness to communication-system bandwidth configurations (Fig. 7 ). Time-frequency results further show that MTI causes nonlinear distortion and energy loss in the target echo, whereas the proposed method retains the principal translational component and the periodic rotor-induced micro-Doppler structures required for fine-grained target characterization (Fig. 8 ).Conclusions The proposed joint spatial–slow-time method suppresses strong static clutter while retaining weak LSS target information. It provides accurate angle estimation in both angularly separated and closely spaced target–clutter scenarios, with a particularly clear advantage for low-speed targets. By estimating and removing the residual DC bias through circle fitting instead of Doppler-domain high-pass filtering, it avoids unnecessary target-energy attenuation and preserves micro-Doppler structures needed for subsequent classification and recognition. The method therefore provides purified, high-fidelity echoes for downstream low-altitude target perception. Future work will investigate dynamic clutter broadening in measured environments and intelligent target classification using real-world echo data. -
表 1 仿真参数设置
参数名称 数值 载波中心频率$ {f}_{c} $ 28 GHz 系统总带宽$ B $ 30.72 MHz 子载波间隔$ \Delta f $ 60 kHz 子载波数$ K $ 512 符号数$ M $ 64 循环前缀周期$ {T}_{cp} $ 4.167 μs 发射天线数$ {N}_{t} $ 64 接收天线数$ {N}_{r} $ 64 
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表 2 杂波参数设置
杂波 距离(m) 信杂比(dB) 方位角(°) 俯仰角(°) 杂波1 50 –40 88.3 29.7 杂波2 100 –40 32.7 48.8 杂波3 150 –40 115.8 –58.2
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表 3 低空目标参数设置
低空目标 距离(m) 方位角(°) 俯仰角(°) 目标1 75 64.4 –31.3 目标2 125 82.8 –47.6 目标3 125 117.8 –56.2
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