Selective Finite Memory Structure Filtering Using the Chi-Square Test Statistic for Temporarily Uncertain Systems
In this paper, a finite memory structure (FMS) filtering with two kinds of measurement windows is proposed using the chi-square test statistic to cover nominal systems as well as temporarily uncertain systems. First, the simple matrix form for the FMS filter is developed from the conditional density...
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MDPI AG
2019-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/9/20/4257 |
| _version_ | 1819057699062546432 |
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| author | Pyung Soo Kim |
| author_facet | Pyung Soo Kim |
| author_sort | Pyung Soo Kim |
| collection | DOAJ |
| description | In this paper, a finite memory structure (FMS) filtering with two kinds of measurement windows is proposed using the chi-square test statistic to cover nominal systems as well as temporarily uncertain systems. First, the simple matrix form for the FMS filter is developed from the conditional density of the current state given finite past measurements. Then, one of the two FMS filters, the primary FMS filter or the secondary FMS filter, with different measurement windows is operated selectively according to the presence or absence of uncertainty, to obtain a valid estimate. The primary FMS filter is selected for the nominal system and the secondary FMS filter is selected for the temporarily uncertain system, respectively. A declaration rule is defined to indicate the presence or absence of uncertainty, operate the suitable one from two filters, and then obtain the valid filtered estimate. A test variable for the declaration rule is developed using a chi-square test statistic from the estimation error and compared to a precomputed threshold. In order to verify the proposed selective FMS filtering and compare with the existing FMS filter and the infinite memory structure (IMS) filter, computer simulations are performed for a selection of dynamic systems including a F404 gas turbine aircraft engine and an electric motor. Simulation results confirm that the proposed selective FMS filtering works well for nominal systems as well as temporarily uncertain systems. In addition, the proposed selective FMS filtering is shown to be remarkably better than the IMS filtering for the temporarily uncertain system. |
| first_indexed | 2024-12-21T13:43:27Z |
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| id | doaj.art-6c8d3ff4633f4b3bb2572b2ed042f117 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-21T13:43:27Z |
| publishDate | 2019-10-01 |
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| spelling | doaj.art-6c8d3ff4633f4b3bb2572b2ed042f1172022-12-21T19:01:57ZengMDPI AGApplied Sciences2076-34172019-10-01920425710.3390/app9204257app9204257Selective Finite Memory Structure Filtering Using the Chi-Square Test Statistic for Temporarily Uncertain SystemsPyung Soo Kim0Department of Electronic Engineering, Korea Polytechnic University, Siheung-si, Gyeonggi-do 15073, KoreaIn this paper, a finite memory structure (FMS) filtering with two kinds of measurement windows is proposed using the chi-square test statistic to cover nominal systems as well as temporarily uncertain systems. First, the simple matrix form for the FMS filter is developed from the conditional density of the current state given finite past measurements. Then, one of the two FMS filters, the primary FMS filter or the secondary FMS filter, with different measurement windows is operated selectively according to the presence or absence of uncertainty, to obtain a valid estimate. The primary FMS filter is selected for the nominal system and the secondary FMS filter is selected for the temporarily uncertain system, respectively. A declaration rule is defined to indicate the presence or absence of uncertainty, operate the suitable one from two filters, and then obtain the valid filtered estimate. A test variable for the declaration rule is developed using a chi-square test statistic from the estimation error and compared to a precomputed threshold. In order to verify the proposed selective FMS filtering and compare with the existing FMS filter and the infinite memory structure (IMS) filter, computer simulations are performed for a selection of dynamic systems including a F404 gas turbine aircraft engine and an electric motor. Simulation results confirm that the proposed selective FMS filtering works well for nominal systems as well as temporarily uncertain systems. In addition, the proposed selective FMS filtering is shown to be remarkably better than the IMS filtering for the temporarily uncertain system.https://www.mdpi.com/2076-3417/9/20/4257chi-square test statisticdeclaration ruleestimation filteringtemporary uncertaintytest variable |
| spellingShingle | Pyung Soo Kim Selective Finite Memory Structure Filtering Using the Chi-Square Test Statistic for Temporarily Uncertain Systems Applied Sciences chi-square test statistic declaration rule estimation filtering temporary uncertainty test variable |
| title | Selective Finite Memory Structure Filtering Using the Chi-Square Test Statistic for Temporarily Uncertain Systems |
| title_full | Selective Finite Memory Structure Filtering Using the Chi-Square Test Statistic for Temporarily Uncertain Systems |
| title_fullStr | Selective Finite Memory Structure Filtering Using the Chi-Square Test Statistic for Temporarily Uncertain Systems |
| title_full_unstemmed | Selective Finite Memory Structure Filtering Using the Chi-Square Test Statistic for Temporarily Uncertain Systems |
| title_short | Selective Finite Memory Structure Filtering Using the Chi-Square Test Statistic for Temporarily Uncertain Systems |
| title_sort | selective finite memory structure filtering using the chi square test statistic for temporarily uncertain systems |
| topic | chi-square test statistic declaration rule estimation filtering temporary uncertainty test variable |
| url | https://www.mdpi.com/2076-3417/9/20/4257 |
| work_keys_str_mv | AT pyungsookim selectivefinitememorystructurefilteringusingthechisquareteststatisticfortemporarilyuncertainsystems |