1 / 12

Multisensor Data Fusion

n 1. F 1 (s). y 1. n 2. x. z. F 2 (s). y 2. n k. F k (s). n 1. y k. x. x. z. S 1. x. y 1. S 2. Filter. y. n 2. 1. The Filtering Approach:. (1). Multisensor Data Fusion. (2). (3). 2. The Compensation Approach:. n. y. e. W(s). F(s). x. I(s)=1.

vina
Download Presentation

Multisensor Data Fusion

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. n1 F1(s) y1 n2 x z F2(s) y2 nk Fk(s) n1 yk x x z S1 x y1 S2 Filter y n2 1. The Filtering Approach: (1) Multisensor Data Fusion (2) (3) 2. The Compensation Approach:

  2. n y e W(s) F(s) x I(s)=1 Optimal Filtration in Scalar Case. (4) (5) Wiener-Hopf Equation: (6) (7)

  3. Wiener Factorization: (8) Wiener Separation: (9) Optimal Filter: (10) Example: (11) (12) (13)

  4. n W(s) F(s) x y n1 z F1(s) W1(s) y1 ε Optimal Fusion of 2 sensors. (1) i (2) (3) (4) (5) (6)

  5. Wiener-Hopf equation: (7) (8) (9) Example: fusion of Doppler and barometric speed sensors: Barometric: (10) (11) Doppler: (12) where:

  6. Discrete Kalman Filter Discrete observed plant: x[n+1] = Anx[n] + w[n]{State equation} y[n] = Cnx[n] + v[n] {Measurements} M{ww'} = Qn, M{vv'} = Rn, M{wv'} = Nn=0, (1) (1a) Performance index: State vector prediction: x[n+1/n] = Anx[n/n]; (2a) Covariance matrix prediction: (2b)

  7. Discrete Observer: Measurement update: X[n+1/n] = AnX[n/n-1] + Kn/n-1 (y[n] - CnX[n/n-1]); X[n/n] = X[n/n-1] + Kn (y[n] - CnX[n/n-1]) Y[n/n] = CnX[n/n]; Pn/n-1 = E{(x[n] - X[n|n-1])(x[n] - X[n|n-1])'} (Ric. solution) Pn/n= E{(x[n] - X[n|n])(x[n] - X[n|n])'} (Updated estimate) Time update: (3) (4) Covariance Matrices: (5) (6) (7)

  8. P(0),X(0),An, Cn,Qn,Rn. Initial data: z-1 z-1 Measurements y[n] X[n/n] = AnX[n/n-1] + Kn (y[n] - CnX[n/n-1]) Time-dependant Kalman Filter Algorithm 1 2 3 4 5 6 7

  9. v1 w1 FK RNS w2 DRS yn/n v2 Example: fusion of the dead reckon and radio-navigation systems

  10. Dec. Bin. Grey 0 0 0 0 0 0 0 y 1 0 0 1 0 0 1 2 0 1 0 0 1 1 x 0 1 3 0 1 1 0 1 0 0 0 1 4 1 0 0 1 1 0 1 1 0 5 1 0 1 1 1 1 6 1 1 0 1 0 1 7 1 1 1 1 0 0 Hd=3 1. Coding of Signals. Hamming’s distance: Grey Code. Example: 3-digit word Transmission of Information Encoding: Truth Table: Decoding:

  11. Angle-Code Converter

  12. Com. Ch. LPF CSG ym ym 2. Modulation of Signals

More Related