Direction of arrival estimation for the uniform or non-uniform noise with adaptive expectation maximisation algorithm

• Published in: IET radar, sonar & navigation Vol. 14; no. 7; pp. 1029 - 1038
• Main Authors: Zhang, Fuqiang, Zhang, Zenghui, Yu, Wenxian
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.07.2020
• Subjects: adaptive expectation maximisation algorithm; AEM algorithm; direction‐of‐arrival estimation; DOA estimation performance; expectation‐maximisation algorithm; initial noise power ratio; iterative methods; nonuniform noise estimation; NUN estimation; passive radar; passive radar waveform estimation; Research Article; signal component; UN estimation; uniform noise estimation; iterative methods; UN estimation; NUN estimation; adaptive expectation maximisation algorithm; direction-of-arrival estimation; DOA estimation performance; passive radar; signal component; initial noise power ratio; AEM algorithm; expectation-maximisation algorithm; nonuniform noise estimation; passive radar waveform estimation; uniform noise estimation
• ISSN: 1751-8784; 1751-8792
• DOI: 10.1049/iet-rsn.2019.0584

The authors consider the problem of direction-of-arrival (DOA) estimation for the uniform noise (UN) or non-UN (NUN) in a passive radar. The radar waveform to be estimated is modelled as a deterministic and unknown process. Under this condition, the well-known expectation maximisation (EM) algorithm can be utilised for the estimation. In the conventional EM algorithm, the DOA is updated in every iteration, whereas the noise power ratio for each signal component is kept constant. Hence, they have to specify the noise power ratio before implementing the EM algorithm. Clearly, the DOA estimation performance of the conventional EM algorithm is sensitive to this initial noise power ratio. To deal with this problem, they propose the adaptive EM (AEM) algorithms for the UN and NUN, respectively. In the authors’ proposed AEM algorithm, the noise power and DOA are jointly estimated and updated in each iteration, which indicates that the noise power ratio for each signal component would be adaptively changed. Therefore, the DOA performance of their proposed AEM algorithm is less sensitive to the initial noise power ratio, thereby achieving better estimation results. Finally, extensive experiments are carried out to validate the effectiveness of their proposed AEM algorithm.