{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":24,"n_cols":5,"rows_kept":24,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[4,2,4,3],[8,2,8,3],[9,2,9,3],[10,2,10,3],[11,2,11,3],[17,2,17,3],[18,2,18,3],[21,2,21,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9911020207403321"]
[null,null,"Autore",null,"Brookner Eli"]
[null,null,"Titolo",null,"Tracking and Kalman filtering made easy"]
[null,null,"Pubbl/distr/stampa",null,"[Place of publication not identified], : Wiley, 1998"]
[null,null,"ISBN",null,"1-280-55638-2"]
[{"$nulls":4},"9786610556380"]
[{"$nulls":4},"0-470-30528-2"]
[{"$nulls":4},"0-471-22419-7"]
[null,null,"Edizione",null,"[Reissue]"]
[null,null,"Descrizione fisica",null,"1 online resource (XXIII, 463 p.)"]
[null,null,"Disciplina",null,"621.3848"]
[null,null,"Soggetti",null,"Electric filters - Mathematics"]
[{"$nulls":4},"Tracking radar - Mathematics"]
[{"$nulls":4},"Kalman filtering"]
[{"$nulls":4},"Electrical & Computer Engineering"]
[{"$nulls":4},"Engineering & Applied Sciences"]
[{"$nulls":4},"Electrical Engineering"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Bibliographic Level Mode of Issuance: Monograph"]
[null,null,"Nota di contenuto",null,"G-h and g-h-k filters -- Kalman filter -- Practical issues for radar tracking -- Least-squares and minimum-variance estimates for linear time-invariant systems -- Fixed-memory polynomial filter -- Expanding-memory (growing-memory) polynomial filters -- Fading-memory (discounted least-squares) filter -- General form for linear time-invariant system -- General recursive minimum-variance growing-memory filter (Bayes and Kalman filters without target process noise) -- Voltage least-squares algorithms revisited -- Givens orthonormal transformation -- Householder orthonormal transformation -- Gram-Schmidt orthonormal transformation -- More on voltage-processing techniques -- Linear time-variant system -- Nonlinear observation scheme and dynamic model (extended Kalman filter) -- Bayes algorithm with iterative differential correction for nonlinear systems -- Kalman filter revisited."]
[null,null,null,"Sommario/riassunto","This book is about radar tracking and the use of filters, particularly Kalman Filters. Tracking of moving targets, such as satellites, is complicated by the introduction of errors into the measurements resulting from noise and non-uniform vehicle motion. Such errors are "]
[{"$nulls":4},"smoothed out by filters. The book covers these filters from very simple, physical and geometric approaches."]