Refinement of TOA Localization with Sensor Position Uncertainty in Closed-Form

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 2; p. 390
• Main Authors: Gan, Yi, Cong, Xunchao, Sun, Yimao
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 10.01.2020; MDPI
• Subjects: Accuracy; Bias; closed-form; error refined; Localization; Methods; Neural networks; Noise; sensor position uncertainty; Sensors; Signal processing; small sensor network; source localization; time of arrival (toa); time of arrival (TOA); closed-form; source localization; small sensor network; error refined; sensor position uncertainty
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20020390

The subject of localization has received great deal attention in the past decades. Although it is perhaps a well-studied problem, there is still room for improvement. Traditional localization methods usually assume the number of sensors is sufficient for providing desired performance. However, this assumption is not always satisfied in practice. This paper studies the time of arrival (TOA)-based source positioning in the presence of sensor position errors. An error refined solution is developed for reducing the mean-squared-error (MSE) and bias in small sensor network (the number of sensors is fewer) when the noise or error level is relatively large. The MSE performance is analyzed theoretically and validated by simulations. Analytical and numerical results show the proposed method attains the Cramér-Rao lower bound (CRLB). It outperforms the existing closed-form methods with slightly raising computation complexity, especially in the larger noise/error case.