基于集员估计的混沌通信窄带干扰抑制技术

Narrowband interference suppression in chaos-based communications based on set-membership estimation

基于混沌载波的有界性和最优定界椭球(OBE)准则,推导出了已知干扰信号模型参数的状态估计和未知干扰信号模型参数的自适应状态估计的干扰对消算法.与基于最小相空间体积(MPSV)的Kalman滤波和传统的递归最小二乘(RLS)算法相比,本算法具有选择更新特性,能在仅有少量数据参与更新的情况下达到与前者接近的性能,降低了计算量.该方法的性能通过在混沌参数调制(CPM)和差分混沌相移键控(DCSK)两种通信方式下对自回归(AR)型和单音两种窄带干扰的有效抑制得到了验证.

This paper presents the set-membership estimation for narrowband interference （NBI） suppression in chaos-based communications. The proposed NBI suppression methods under known or unknown model parameters of interference signals are derived in terms of the optimal bounding ellipsoid criteria and the bounded property of chaotic carriers, respectively. Compared to the mean phase space volume （MPSV） based Kalman filtering and the recursive least square （RLS） algorithms, the proposed NBI suppression method estimates interference signals using only a few observations with the selective updating features, so that the computatiori is effectively reduced. Simulations results on AR and single tone interference suppression in chaotic parameter modulation （CPM） and differential chaotic shift keying （DCSK） communication systems show that the proposed NBI suppression methods can efficiently eliminate the narrowband interference signal in CPM and DCSK communication systems, and the computation is smaller than that of MPSV-based Kalman filtering and RLS algorithms.