1 / 10

GPS-INS resampling & regularization

GPS-INS resampling & regularization. Midterm Presentation Annual project Winter semester תש"ע ( (2009. Students: Oren Hyatt, Alex Dutov Supervisor: Mony Orbach. Abstract. Problem: A GPS system isn’t fast enough, to meet updating requirements of high speed systems.

menora
Download Presentation

GPS-INS resampling & regularization

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. GPS-INSresampling & regularization Midterm PresentationAnnual projectWinter semesterתש"ע ((2009 Students: Oren Hyatt, Alex Dutov Supervisor: MonyOrbach

  2. Abstract • Problem: A GPS system isn’t fast enough, to meet updating requirements of high speed systems. • Solution: Implementation of a system that integrates GPS, INS, and a particles filter.

  3. Project’s goals • Implementation of the resampling & regularization parts. • Interface with the other parts of the system. • Meet hardware\software requirements (see specs.).

  4. Milestones for Project A Logical validation. Resampling & regularization integration Timing simulation On board debugging

  5. What has been done? • Learning algorithm. • Learning environment (except of Stratix III). • Initial Design (Block diagrams). • Implementation Beginning.

  6. Resampling diagram Xp[1..30K]x17 c_index CUMSUM memory (30Kx32=117KB) Resampling index U>C? ready W[1..30K] next_U RANDOM (rand_current,rand_next) Uj,Uj+1 Memory (17x32=68Byte) rand_next I/O W: weights vector; Xp:particles vector; Xp_new: resampledXp; Local variables U: Number to compare with C: C[i]=w1+…+wi; enable en_seed seed Control signals enable: calls rand(); en_seed: calls srand(seed); rand_next: protocol between RANDOM and U; ready: index ready for resampling; next_U: calculate next U; reg_init: regularization’s initialization; reg_init Xp_new[1..17]x32

  7. Regularization diagram cov matrix [30Kx30K] buffer Particles Regularization Angles’ normalization [-pi,+pi] Xp_new[1..17]x32 buffer Hopt RANDOM (~Gauss) Converting Euler’s angles to quaternians (LUT of cos/sin) reg_init I/O Xp_new: resampledXp; Cov matrix: covariance matrix; Xp_reg: regulated Xp_new; Control signals reg_init: initial random Regulated Xp_reg[1..30K]x17

  8. Software\Hardware requirements Using VHDL, the project would be implemented on FPGA board: Gidel’sStratix III PROCStar 110. Project will enable system’s output every 10[msec]. I/O: parallel Input/output, values transmitted one by one.

  9. Software\Hardware requirements • Input: particles (30K x 440bits) & weights vector (30K x28bits) every 50 [ns], covariance matrix (17x17 bits). • Output: revalued and normalized particles and weights vector (same sizes). • The board will communicate with the other parts of the system, using a predefined protocol.

  10. Gantt

More Related