One is the improved semi-blind channel estimation algorithm inOFDM systems based on signal subspace. While under color noise, in order toeffectively realize semi-blind channel estimation, the autocorrelation matrix ofreceived signal is computed by using gradient-based variable forgetting factor RLSalgorithm (GVFF-RLS).
本文提出了一种基于信号子空间的改进OFDM系统半盲信道估计算法,利用基于梯度变化的变遗忘因子递归最小二乘算法(GVFF-RLS)计算接收信号的自相关矩阵。
参考来源 - OFDM系统中的信道估计算法研究·2,447,543篇论文数据，部分数据来源于NoteExpress
卡尔曼增益通过变遗忘因子进行改进，避免误差的累积。
Using improved Kalman filter to forecast and track. Kalman gain is improved through variable forgetting factor which will avoid the accumulation of errors.
新算法使用时变遗忘因子对误差进行指数加权平均来估计均方误差，并使用该因子改变自适应迭代过程中滤波器系数向量的更新方向。
In the new algorithm, a time-variant forgetting factor is introduced to estimate the Mean Square Er-ror (MSE) and change the updating direction of adaptive filter coefficient vector.
为了改善固定遗忘因子递推最小二乘(rls)算法在时变系统中的跟踪性能，提出一种改进的RLS算法。
In order to improve the tracking performance of the fixed forgetting factor RLS algorithm in the time-varying system, a modified RLS algorithm is proposed.
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