I am a Senior Software Engineer with a Master's degree in Signal and Information Processing from UESTC. My educational background has provided me with strong foundations in statistical signal processing, adaptive signal processing, and optimization theory.

Since 2016, I have been working at MIGU Co. Ltd, where I started with server-side software development and Unity software development. Recently, my interests have shifted toward cutting-edge AI technologies, particularly in the areas of Large Language Models (LLM), AI Generated Content (AIGC), and Agent development.

My technical skillset includes Java, Microservices, Unity, as well as emerging AI technologies. I am continuously expanding my knowledge in these areas to stay at the forefront of software engineering innovations.

• Distributed adaptive direct position determination of emitters in sensor networks

  Wei Xia, Wei Liu

  Signal Processing, Vol. 123, pp. 100-111, June 2016

  In the conventional centralized adaptive direct position determination (C-ADPD) approach, the emitter position is estimated at the fusion center (usually one of the sensors) with all the available signal samples transmitted from different sensors. This centralized framework may be not suitable for large-scale sensor networks due to the computational capability and energy storage bottleneck of the single fusion center. Furthermore, transmitting all the received signals to the fusion center usually needs multi-hop transmission, which is a big challenge to the communication bandwidth of the sensor networks. In this paper, we propose a fully distributed adaptive direct position determination (D-ADPD) approach for emitter localization.

• Distributed adaptive direct position determination based on diffusion framework

  Wei Xia, Wei Liu, Lingfeng Zhu

  Journal of Systems Engineering and Electronics, Vol. 27, No. 1, pp. 28-38, February 2016

  The conventional direct position determination (DPD) algorithm processes all received signals on a single sensor. When sensors have limited computational capabilities or energy storage, it is desirable to distribute the computation among other sensors. A distributed adaptive DPD (DADPD) algorithm based on diffusion framework is proposed for emitter localization. Unlike the corresponding centralized adaptive DPD (CADPD) algorithm, all but one sensor in the proposed algorithm participate in processing the received signals and estimating the common emitter position, respectively. The computational load and energy consumption on a single sensor in the CADPD algorithm is distributed among other computing sensors in a balanced manner.

• An Enhanced Distributed Adaptive Direct Position Determination

  Wei Xia, Wei Liu, Xinglong Xia, Jinfeng Hu, Huiyong Li, Zishu He, Sen Zhong

  IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, Vol. 99, No. 5, pp. 1005-1010, May 2016

  The recently proposed distributed adaptive direct position determination (D-ADPD) algorithm provides an efficient way to locating a radio emitter using a sensor network. However, this algorithm may be suboptimal in the situation of colored emitted signals. We propose an enhanced distributed adaptive direct position determination (EDA-DPD) algorithm. Simulations validate that the proposed EDA-DPD outperforms the D-ADPD in colored emitted signals scenarios and has the similar performance with the D-ADPD in white emitted signal scenarios.

• A noise-constrained distributed adaptive direct position determination algorithm

  Wei Xia, Xinglong Xia, Hongbin Li, Wei Liu, Jinfeng Hu, Zishu He

  Signal Processing, Vol. 135, pp. 9-16, June 2017

  In this work, we consider distributed localization of an emitter using a wireless senor network, where each sensor can, respectively, receive the signal transmitted by the emitter, estimate the noise variance and share information with its neighbors. We propose herein a noise-constrained distributed adaptive direct position determination (NCD-ADPD) algorithm by exploiting the a priori knowledge of the noise variance.