Adaptive Integrated Navigation Algorithm Based on Interactive Filter
To address the diverse requirements of accuracy and robustness in integrated navigation for unmanned aerial vehicles, an interactive robust filter algorithm that integrates the interactive multiple model concept and leverages the complementary applicability of the strong tracking filter and the smoo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4562 |
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| author | Bin Zhao Chunlei Gao Hui Xia Jinxia Han Ying Zhu |
| author_facet | Bin Zhao Chunlei Gao Hui Xia Jinxia Han Ying Zhu |
| author_sort | Bin Zhao |
| collection | DOAJ |
| description | To address the diverse requirements of accuracy and robustness in integrated navigation for unmanned aerial vehicles, an interactive robust filter algorithm that integrates the interactive multiple model concept and leverages the complementary applicability of the strong tracking filter and the smooth variable structure filter is proposed. The algorithm operates as follows: the strong tracking filter, along with the smooth variable structure filter, operates side by side with distinct models. During the filter process, the likelihood function is utilized to update the filter probabilities and determine the weights for each one of the filters. Input interaction, coupled with output fusion, is then carried out. The results of the experiments validate that the presented interactive filter algorithm significantly reduces estimation errors. When confronted with complex, dynamic noise environments and system uncertainties, it retains high-precision state estimation while demonstrating markedly improved robustness. The proposed interactive robust filter algorithm is compared against the strong tracking filter, smooth variable structure filter, and strong tracking smooth filter. Taking the strong tracking smooth filter, which has the highest accuracy among the three, as the reference baseline, the presented interactive robust filter algorithm achieves over 16% improvement in velocity accuracy and over 40% improvement in position accuracy. |
| format | Article |
| id | doaj-art-e2daae91ddbd4c0cac768548873e1480 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-e2daae91ddbd4c0cac768548873e14802025-08-20T03:36:33ZengMDPI AGSensors1424-82202025-07-012515456210.3390/s25154562Adaptive Integrated Navigation Algorithm Based on Interactive FilterBin Zhao0Chunlei Gao1Hui Xia2Jinxia Han3Ying Zhu4School of Marine and Electrical Engineering, Jiangsu Maritime Institute, Nanjing 211100, ChinaJincheng College, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, ChinaSchool of Marine and Electrical Engineering, Jiangsu Maritime Institute, Nanjing 211100, ChinaSchool of Marine and Electrical Engineering, Jiangsu Maritime Institute, Nanjing 211100, ChinaSchool of Electrical and Power Engineering, Hohai University, Nanjing 211100, ChinaTo address the diverse requirements of accuracy and robustness in integrated navigation for unmanned aerial vehicles, an interactive robust filter algorithm that integrates the interactive multiple model concept and leverages the complementary applicability of the strong tracking filter and the smooth variable structure filter is proposed. The algorithm operates as follows: the strong tracking filter, along with the smooth variable structure filter, operates side by side with distinct models. During the filter process, the likelihood function is utilized to update the filter probabilities and determine the weights for each one of the filters. Input interaction, coupled with output fusion, is then carried out. The results of the experiments validate that the presented interactive filter algorithm significantly reduces estimation errors. When confronted with complex, dynamic noise environments and system uncertainties, it retains high-precision state estimation while demonstrating markedly improved robustness. The proposed interactive robust filter algorithm is compared against the strong tracking filter, smooth variable structure filter, and strong tracking smooth filter. Taking the strong tracking smooth filter, which has the highest accuracy among the three, as the reference baseline, the presented interactive robust filter algorithm achieves over 16% improvement in velocity accuracy and over 40% improvement in position accuracy.https://www.mdpi.com/1424-8220/25/15/4562estimation accuracyintegrated navigationinteractive robust filtersmooth variable structure filterstrong tracking filter |
| spellingShingle | Bin Zhao Chunlei Gao Hui Xia Jinxia Han Ying Zhu Adaptive Integrated Navigation Algorithm Based on Interactive Filter Sensors estimation accuracy integrated navigation interactive robust filter smooth variable structure filter strong tracking filter |
| title | Adaptive Integrated Navigation Algorithm Based on Interactive Filter |
| title_full | Adaptive Integrated Navigation Algorithm Based on Interactive Filter |
| title_fullStr | Adaptive Integrated Navigation Algorithm Based on Interactive Filter |
| title_full_unstemmed | Adaptive Integrated Navigation Algorithm Based on Interactive Filter |
| title_short | Adaptive Integrated Navigation Algorithm Based on Interactive Filter |
| title_sort | adaptive integrated navigation algorithm based on interactive filter |
| topic | estimation accuracy integrated navigation interactive robust filter smooth variable structure filter strong tracking filter |
| url | https://www.mdpi.com/1424-8220/25/15/4562 |
| work_keys_str_mv | AT binzhao adaptiveintegratednavigationalgorithmbasedoninteractivefilter AT chunleigao adaptiveintegratednavigationalgorithmbasedoninteractivefilter AT huixia adaptiveintegratednavigationalgorithmbasedoninteractivefilter AT jinxiahan adaptiveintegratednavigationalgorithmbasedoninteractivefilter AT yingzhu adaptiveintegratednavigationalgorithmbasedoninteractivefilter |