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The proposed pedestrian navigation framework consists of a lower filter and an upper filter. In the lower filter which is designed based on the Kalman filter, the adaptive zero velocity detection algorithm is used to detect the zero velocity interval at different motion speeds, and then, zero velocity update is applied to rectify the inertial navigation solutions\u2019 errors. In the upper filter which is designed based on the nonrecursive Bayesian filter, the map matching method with nonrecursive Bayesian filter is adopted to fuse the map prior information and the lower filter estimation results to correct the errors of navigation. The position estimation presented in this study achieves an average position error of 0.53\u2009m compared to the ZUPT\u2010aided inertial navigation system (INS) method under different motion states. The proposed pedestrian navigation algorithm achieves an average position error of 0.54\u2009m as compared to the ZUPT\u2010aided INS method among the different tested distances. The proposed framework simplifies the indoor positioning system under multiple motion speed conditions by ensuring the accuracy and stability property. The effectiveness and accuracy of the proposed framework are experimentally verified in various real\u2010world scenarios.<\/jats:p>","DOI":"10.1155\/2021\/7621393","type":"journal-article","created":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T20:20:36Z","timestamp":1640290836000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An Indoor Localization Method Based on the Combination of Indoor Map Information and Inertial Navigation with Cascade Filter"],"prefix":"10.1155","volume":"2021","author":[{"given":"Yushuai","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0490-7649","authenticated-orcid":false,"given":"Jianxin","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0992-6531","authenticated-orcid":false,"given":"Feng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liping","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,12,23]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.3390\/s16050635"},{"key":"e_1_2_10_2_2","article-title":"The positioning principle of global positioning system and its application prospects","volume":"781","author":"Wang Y.","year":"2021","journal-title":"IOP Conference Series: Earth and Environmental Science"},{"key":"e_1_2_10_3_2","article-title":"Hybrid deep learning model based indoor positioning using Wi-Fi RSSI heat maps for autonomous applications","volume":"10","author":"Poulose A.","year":"2021","journal-title":"Electronics"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/app10186290"},{"key":"e_1_2_10_5_2","doi-asserted-by":"crossref","unstructured":"PouloseA.andHanD. 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