基于最小1-范数准则模糊函数的联合TDOA/FDOA估计算法

Joint Estimation Algorithm of TDOA/FDOA Based on Least 1-Norm Criterion Ambiguity Function

针对非高斯噪声条件下利用传统模糊函数的到达时差(time difference of arrival,TDOA)和到达频差(frequency difference of arrival,FDOA)联合估计方法性能退化问题,提出一种基于最小1-范数准则模糊函数的联合TDOA/FDOA估计算法。在给出1-范数模糊函数概念的基础上,提出一种存在重尾分布α稳定噪声条件下的联合TDOA/FDOA估计算法。仿真实验结果表明,与传统模糊函数以及分数低阶矩(fractional lower order moments,FLOM)方法相比,该算法能够更好地逼近克拉美罗界(Cramér-Rao lower bounds,CRLB),且在鲁棒性和估计精度等方面性能明显提升。

To address performance degradation in the joint estimation method of time difference of arrival （TDOA） and frequency difference of arrival （FDOA） based on traditional ambiguity function for non-Gaussian noise conditions, a joint estimation algorithm based on least 1-norm criterion ambiguity function is proposed. Based on the concept of 1-norm ambiguity function, a joint estimation method of TDOA/FDOA algorithm for alpha-stable noise with heavy tails is presented. Simulation results of the system model show that the performance of the proposed algorithm approaches the Cramér-Rao lower bounds （CRLB） at high noise level, and it is better than that of the traditional ambiguity function and fractional lower order moments （FLOM） method in terms of robustness and estimation accuracy.