Adaptive position‐domain carrier‐smoothed code filter based on innovation sequences. Issue 4 (1st April 2014)

• Record Type: Journal Article
• Authors: Lee, Je Young; Kim, Hee Sung; Choi, Kwang Ho; Cho, Jeongho; Lee, Hyung Keun
• Is Part Of: IET radar, sonar & navigation. Volume 8:Issue 4(2014)
• Page Start: 336
• Page End: 343
• Publication Date: 2014-04-01
• Subjects: adaptive codes -- innovation management -- Kalman filters -- radio receivers -- satellite navigation; adaptive position‐domain carrier‐smoothed code filter -- innovation sequences -- radio frequency signals -- Global Navigation Satellite System -- GNSS positioning -- CSC filtering -- Kalman filters -- dynamics model -- time propagation -- covariance parameters -- carrier‐phase measurements -- field‐collected measurements -- covariance estimator; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2013.0021
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : Owing to dependence on radio frequency signals, the accuracy of Global Navigation Satellite System (GNSS) is largely dependent on the receiver's surrounding environments. Accordingly, accurate and reliable GNSS positioning in various harsh environments has been one of the major problems of GNSS. With regard to improve positioning accuracy in GNSS, this study proposes an efficient covariance estimation method for position‐domain carrier‐smoothed code (CSC) filters which, differently from traditional Kalman filters, do not require a dynamics model for time propagation of system states. The exclusion of a dynamics model, however, may incur shortage of information to estimate covariance parameters. To handle this problem, the proposed method imposes minimal assumptions on covariance structures for pseudorange and carrier‐phase measurements. Performance of the proposed method is demonstrated by an experimental result with field‐collected measurements showing that the proposed covariance estimator improves the accuracy of CSC filtering considerably.