Tài liệu Tracking and Kalman filtering made easy P4 docx
Tài liệu Tracking and Kalman filtering made easy P4 docx
... is,Yn¼y0y1...yr266664377775nð4:1-1aÞXn¼x0ðtÞx1ðtÞ...xmÀ1ðtÞ266664377775ð4:1-1bÞ155 Tracking and Kalman Filtering Made Easy. Eli BrooknerCopyright # 1998 John Wiley & Sons, Inc.ISBNs: 0-471-18407-1 ... g–h growing-memory (expanding-memory) filter whose weights g and h are given by (1.2-38a and 1.2-38b). Theg–h filter itself is defined by (1.2-8a) and (1.2-8b). In Chapters 5 and 6 anindication ... general and on two other forms of thistransformation, the Givens and Householder, are given in respectively Chapters10 and 12.4.4 ADAPTIVE NULLING, THE ORTHONORMALTRANSFORMATION, AND THE...
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Tài liệu Tracking and Kalman filtering made easy P2 docx
... dynamics model with some randomness. This modelis called the constant-velocity trajectory model with a random-walk velocity.64 Tracking and Kalman Filtering Made Easy. Eli BrooknerCopyright ... Yn and XÃn;n and between XÃn;nÀ1 and XÃn;n. Again the weighting of theseerrors will be made according to their importance. The more important an erroris, the smaller it will be made. ... the g and h weight pairings for the critically damped filter[1.2-36)] and the expanding-memory polynomial filter described in Sections1.2.6 and 1.2.10, respectively. Recall that the expanding-memory...
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Tài liệu Tracking and Kalman filtering made easy P1 doc
... gnÞþyngnð1:2-12ÞFigure 1.2-3 Filtering problem. Esti-mate of xÃn;nbased on measurement yn and prediction xÃn;nÀ1.22g–h AND g–h–k FILTERS1g–h AND g–h–k FILTERS1.1 WHY TRACKING AND PREDICTION ARE ... properassociation of the echo from target 1 on the second scan with the echo from3 Tracking and Kalman Filtering Made Easy. Eli BrooknerCopyright # 1998 John Wiley & Sons, Inc.ISBNs: 0-471-18407-1 ... .00040g–h AND g–h–k FILTERSWe shall use the same assumptions as given in Example 1 of Section 1.2.7.There ¼ 0:75 and x¼ 50 ft. Substituting (1.2-41) and (1.2-42) into (1.2-40) and solving...
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Tài liệu Tracking and Kalman filtering made easy P3 pdf
... be the case in Figure 3.1-1 for spiky sea clutter, rain clutter, lognormal111 Tracking and Kalman Filtering Made Easy. Eli BrooknerCopyright # 1998 John Wiley & Sons, Inc.ISBNs: 0-471-18407-1 ... and match filtering and then discuss the effects of using such awaveform on tracking accuracy.3.5.1 Chirp Waveform, Pulse Compression, Match Filtering of ChirpWaveform, and Pulse CompressionFigure ... point detections and deleted from the detection file and used instead forupdating the clutter points. While this is going on a determination is made ifdetections in sectors 7, 8, and 9 (for which...
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Tài liệu Tracking and Kalman filtering made easy P5 ppt
... insight into the Legendre orthogonal polynomial fit approach will beobtained.205 Tracking and Kalman Filtering Made Easy. Eli BrooknerCopyright # 1998 John Wiley & Sons, Inc.ISBNs: 0-471-18407-1 ... matrix and their derivatives are small in the observation interval and increase monotonically outside; see Figures 5.8-2 and 5.8-3. Because thesystematic errors and their derivatives are made ... measured and not its derivatives, thenYn¼ MXnþ Nnð5:4-3Þwhere because range is the only measurementM ¼½100 0ð5:4-3aÞ and Yn and Nnare 1 1 matrices given byYn¼½yn and Nn¼½nð5:4-3bÞFor...
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Tài liệu Tracking and Kalman filtering made easy P6 ppt
... obtainedwhen using a short memory for the fading-memory filter discussed inChapter 7.233 Tracking and Kalman Filtering Made Easy. Eli BrooknerCopyright # 1998 John Wiley & Sons, Inc.ISBNs: 0-471-18407-1 ... disadvantages are theexpanding-memory filter and the fading memory filter. The expandingmemory filter is, as discussed in Section 1.2.10 and later in Section 7.6,suitable for track initiation and will be ... (5.6-4), (5.6-7), and (5.8-1)] and systematic error [such as (5.10-2) and (5.10-3)] apply with Lreplaced by n. The least-squares polynomial fit equations given by (5.3-11),(5.3-13), and (5.5-3) also...
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Tài liệu Tracking and Kalman filtering made easy P7 pdf
... achievedusing a discounted, least-squares weighting as done in (1.2-34); that is, we239 Tracking and Kalman Filtering Made Easy. Eli BrooknerCopyright # 1998 John Wiley & Sons, Inc.ISBNs: 0-471-18407-1 ... ynÀ2, r ¼ 2 and r¼ 2, and the errorfor this time sample can even be bigger, and so forth. Thus with this weightingthe errors relative to the fitting polynomial are discounted more and more ... by (7.2-1d).7.8 BALANCING THE SYSTEMATIC AND RANDOMPREDICTION ERRORAs indicated at the end of Section 7.3, the fading-memory filter systematic and random errors have to be balanced in a similar...
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Tài liệu Tracking and Kalman filtering made easy P8 ppt
... d and thetransition matrix is a generalized form of (5.4-7). Note that in this text drepresents the true degree of the target dynamics while m is the degree used by252 Tracking and Kalman Filtering ... degree of the target dynamics while m is the degree used by252 Tracking and Kalman Filtering Made Easy. Eli BrooknerCopyright # 1998 John Wiley & Sons, Inc.ISBNs: 0-471-18407-1 (Hardback); ... beobtained by using the straightforward method illustrated in Section 2.4; see(2.4-1), (2.4-1a), and (2.4-1b) and (1.3-1) in Section 1.3. However, as shall beseen later, for more complicated target...
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Tài liệu Tracking and Kalman filtering made easy P9 pdf
... minimum-variance estimate XÃnþ1;nþ1from the set of data consisting of YðnÞ260 Tracking and Kalman Filtering Made Easy. Eli BrooknerCopyright # 1998 John Wiley & Sons, Inc.ISBNs: 0-471-18407-1 ... Kalman filter can lead to computational problems for the Kalman filter [5, Section 12.4]. This form of the Kalman filter is not generally used forthis reason, and it is not a form proposed by Kalman. ... proposed by Kalman. The Kalman filter with thetarget process noise included is revisited in Chapter 18.9.4 COMPARISON OF BAYES AND KALMAN FILTERSAs discussed in Sections 2.3, 2.5, and 2.6, the recursive...
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Tài liệu Tracking and Kalman filtering made easy P10 pptx
... and also from (4.3-16) and (4.3-17) and reference 79(p. 57), thatFTF ¼ I ¼ FFTð10:2-1Þ and FÀ1¼ FTð10:2-2ÞAlsokFYk¼kYkð10:2-3Þwhere kk is the Euclidean norm defined by (4.2-40) and ... space of F. From (10.2-8) and (10.2-9), and 272VOLTAGE LEAST-SQUARES ALGORITHMS REVISITEDDefine F1 and F2as the matirces formed by, respectively, the first m0rows ofF and remaining s À m0bottom ... ThenF1¼f1...fm0264375ð10:2-35Þ and F2¼fm0þ1...fs264375ð10:2-36Þ and F ¼F1 F22435gm0gs À m0ð10:2-37Þ and from (10.2-8) and (10.2-31) to (10.2-34)FT ¼F1T...
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