Noise covariances estimation for systems with bias states
This paper presents a new approach to noise covariances estimation for a linear, time-invariant, stochastic system with constant but unknown bias states. The system is supposed to satisfy controllable/observable conditions without bias states. Based on a restructured data representation, the covaria...
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| Veröffentlicht in: | IEEE transactions on aerospace and electronic systems 2000-01, Vol.36 (1), p.226-233 |
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| container_title | IEEE transactions on aerospace and electronic systems |
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| creator | Um, Tae Yoon Lee, Jang Gyu Park, Seong-Taek Park, Chan Gook |
| description | This paper presents a new approach to noise covariances estimation for a linear, time-invariant, stochastic system with constant but unknown bias states. The system is supposed to satisfy controllable/observable conditions without bias states. Based on a restructured data representation, the covariance of a new variable that consists of measurement vectors is expressed as a linear combination of unknown parameters. Noise covariances are then estimated by employing a recursive least-squares algorithm. The proposed method requires no a priori estimates of noise covariances, provides consistent estimates, and can also be applied when the relationship between bias states and other states is unknown. The method has been applied to strapdown inertial navigation system initial alignment. Simulation results indicate a satisfactory performance of the proposed method. |
| doi_str_mv | 10.1109/7.826324 |
| format | Article |
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Simulation results indicate a satisfactory performance of the proposed method.</description><subject>Algorithms</subject><subject>Automatic control</subject><subject>Bias</subject><subject>Computer simulation</subject><subject>Control systems</subject><subject>Electric variables control</subject><subject>Estimates</subject><subject>Inertial navigation</subject><subject>Mathematical analysis</subject><subject>Noise</subject><subject>Nonlinear filters</subject><subject>Representations</subject><subject>State estimation</subject><subject>Statistics</subject><subject>Stochastic systems</subject><subject>Strapdown inertial navigation</subject><subject>Technological innovation</subject><subject>Yield estimation</subject><issn>0018-9251</issn><issn>1557-9603</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2000</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqN0UtLAzEQB_AgCtYHePa0eFAvW_N-HKX4gqKX3kM2O4sp7aZutkq_vSlbPHjQHkII-THMzB-hC4LHhGBzp8aaSkb5ARoRIVRpJGaHaIQx0aWhghyjk5Tm-ck1ZyNkXmNIUPj46brgWg-pgNSHpetDbIsmdkXapB6WqfgK_XtRBZeK1Lse0hk6atwiwfnuPkWzx4fZ5Lmcvj29TO6npedU9SWYxgFQpZVwVSU8BaeU4K42Lh8tiMeyBixqXuHayQZIzbiXwmMqJWHsFN0MZVdd_Fjn3uwyJA-LhWshrpM1hEshlZJZXv8pqTYKY2n2gJwLadT_UEnOuML7QMoY3U5z9QvO47pr8_6s1lxrJhTN6HZAvospddDYVZcT6TaWYLsN2So7hJzp5UADAPyw3ec3-F2f7g</recordid><startdate>200001</startdate><enddate>200001</enddate><creator>Um, Tae Yoon</creator><creator>Lee, Jang Gyu</creator><creator>Park, Seong-Taek</creator><creator>Park, Chan Gook</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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The system is supposed to satisfy controllable/observable conditions without bias states. Based on a restructured data representation, the covariance of a new variable that consists of measurement vectors is expressed as a linear combination of unknown parameters. Noise covariances are then estimated by employing a recursive least-squares algorithm. The proposed method requires no a priori estimates of noise covariances, provides consistent estimates, and can also be applied when the relationship between bias states and other states is unknown. The method has been applied to strapdown inertial navigation system initial alignment. Simulation results indicate a satisfactory performance of the proposed method.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/7.826324</doi><tpages>8</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Automatic control Bias Computer simulation Control systems Electric variables control Estimates Inertial navigation Mathematical analysis Noise Nonlinear filters Representations State estimation Statistics Stochastic systems Strapdown inertial navigation Technological innovation Yield estimation |
| title | Noise covariances estimation for systems with bias states |
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