Adaptive Signal Processing Based on High-Order Statistics

本研究是从统计学文献中探索新的结果 其预期对性能具有显著优势 和信号模型的准确性。 以前只有最小相位（或 对称）模型可以从观测数据中识别， 隐式高斯假设通过传统的限制， 二阶统计 利用高阶累积量统计， 研究人员能够确定一个很好的近似真实 信号模型的相位。 这将是重要的应用程序， 自适应信道均衡、自适应回声消除和自适应 数组处理 在提交的16个SBIR提案中，该提案被评为第一 MIPS部门。 为6个项目编列预算49 499美元 强烈建议对该第一阶段提案进行为期三个月的审查。

This research is exploring new results from the statistics literature which are expected to have significant advantages for the performance and accuracy of signal models. Previously only minimum - phase (or symmetric) models could be identified from observed data due to an implicit Gaussian assumption through the conventional restriction to second-order statistics. By use of higher-order cumulant statistics, the researcher is able to identify a good approximation to the true phase of the signal model. This will be important in applications to adaptive channel equalization adaptive echo cancellation, and adaptive array processing. This proposal was rated first out the sixteen SBIR proposals submitted to the MIPS Division. Funding in the budgeted amount of $49,499 for 6 months is highly recommended for this Phase I proposal.