基于样条插值的非线性自干扰对消技术

doi:10.12305/j.issn.1001-506X.2024.09.03

摘要/Abstract

摘要：

针对雷达干扰机收发天线之间存在的非线性自干扰耦合问题, 研究一种基于样条插值的非线性自干扰对消方法。该方法将样条插值和自适应滤波相结合, 分别建立样条哈默斯坦模型和样条维纳模型, 通过引入鲁棒性较强的反正切(arctangent, ARC)函数作为代价函数, 得到两种模型下样条控制点和滤波器系数的自适应学习规则, 并分析样条控制点数量对自干扰对消性能的影响。仿真实验表明, 对于带宽为60 MHz的信号, 所提基于ARC参数学习方法与传统最小均方参数学习方法相比, 对消比获得4 dB左右的提升, 收敛速度提高1倍。另外, 针对信道突变的场景, 所提方法跟踪性能好且稳态误差低。当背景噪声中存在非高斯脉冲干扰时, 所提方法能够有效应对脉冲噪声环境下的干扰。

关键词: 非线性自干扰对消, 样条插值, 反正切函数, 脉冲噪声

Abstract:

In view of the nonlinear self-interference coupling problem between the transmitting and receiving antennas of radar jammer, a nonlinear self-interference cancellation method based on spline interpolation is studied. The method combines spline interpolation and adaptive filtering, and establishes the spline-based Hammerstein model and the spline-based Wiener model respectively. By introducing the robust arctangent (ARC) function as the cost function, the adaptive learning rules of the spline control points and filter coefficients under the two models are obtained, and the influence of the number of spline control points on the performance of the self-interference cancellation is analyzed. Simulation experiments show that for signals with a bandwidth of 60 MHz, the proposed ARC-based parameter learning method achieves about 4 dB improvement in the interference cancellation ratio compared with the traditional least mean square parameter learning methods, and the convergence speed can be improved by a factor of one. In addition, the method has good tracking performance and low steady-state error for the scenario of sudden channel change. When non-Gaussian pulse interference exists in the background noise, the proposed method can effectively cope with the interference in the impulse noise environment.

Key words: nonlinear self-interference cancellation, spline interpolation, arctangent (ARC) function, impulsive noise

中图分类号:

TN974

赵忠凯, 关泽越, 李虎. 基于样条插值的非线性自干扰对消技术[J]. 系统工程与电子技术, 2024, 46(9): 2916-2925.

Zhongkai ZHAO, Zeyue GUAN, Hu LI. Nonlinear self-interference cancellation technique based on spline interpolation[J]. Systems Engineering and Electronics, 2024, 46(9): 2916-2925.

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数量	SPH-LMS	SPH-ARC	SPW-LMS	SPW-ARC
5	30.55	32.01	29.71	41.67
7	38.88	39.22	49.10	50.59
9	43.58	44.05	49.75	50.14
11	44.82	45.47	49.97	50.56
13	45.28	46.17	49.62	50.72
15	45.40	46.48	48.98	50.78
17	45.31	46.55	48.74	50.72
19	45.34	46.63	48.79	50.12
21	45.46	46.74	48.23	50.53

模型名称	滤波阶段乘法次数	系数更新阶段乘法次数
SPH	P²+4P+4M+8	4P+2PM+10M+8
SPW	P²+4P+4M+8	P²+5P+6M+23
MP	$4\left(\frac{P_{M P}+1}{2}\right)^2+(4 M+2)\left(\frac{P_{M P}+1}{2}\right) $	$ 4 M\left(\frac{P_{M P}+1}{2}\right)+2$

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