基于积分抽取的时/频差参数估计方法

利用互模糊函数对大数据量信号进行时/频差参数估计的运算量大，现有处理算法难以满足系统实时性要求。针对该问题，提出了基于积分抽取的时/频差参数估计方法。该方法根据对积分抽取滤波器的性能分析，选择最优的抽取倍数，对粗估计和精细估计中的混合积信号进行降采样，在信息基本不缺失的前提下，大幅降低了FFT和CZT细化处理的运算量。利用该方法，可以将时/频域混合积信号抽取到5个样本点，再通过CZT运算就可以实现对时/频差参数的高精度估计。仿真结果表明所提方法的有效性。

Fast Algorithm for Joint Estimation of DTO and DFO Based on Integrate-and-Dump Filters

The computational burden of cross ambiguity function is very large for estimating the DTO (Differential Time Offset) and DFO (Differential Frequency Offset) when processing huge data, and general algorithms are difficult to meet the real time requirements. A method for estimating DTO and DFO based on Integrate and Dump filters is proposed in this paper. According to performance analysis of the filter, appropriate decimation factor is determined. Then, the computational burdens for both coarse estimation and fine estimation are reduced significantly without affecting estimation accuracy. In the fine estimation, the mixed product signal can be decimated to five sampling points and operated by using CZT to achieve estimation of DTO and DFO with high accuracy. The simulation results validate the proposed algorithm.