2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDSAM-12.1
Paper Title KLD MINIMIZATION-BASED CONSTRAINED MEASUREMENT FILTERING FOR TWO-STEP TDOA INDOOR TRACKING
Authors Rui Huang, Southeast University, China; Le Yang, University of Canterbury, New Zealand; Jun Tao, Southeast University, China; Yanbo Xue, Kanzhun Technology, China
SessionSAM-12: Tracking and Localization
LocationGather.Town
Session Time:Friday, 11 June, 14:00 - 14:45
Presentation Time:Friday, 11 June, 14:00 - 14:45
Presentation Poster
Topic Sensor Array and Multichannel Signal Processing: [RAS-LCLZ] Source localization
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract This paper presents an enhanced two-step method for tracking an indoor point target using the time difference of arrival (TDOA) measurements from an ultra wideband (UWB) positioning system. Again, the algorithm preprocesses the raw TDOAs and then feeds the results to a recursively bounded grid-based filter (RBGF) for position tracking. Different from the state-of-the-art, inequality constraints on the true TDOAs from the RBGF are exploited in the preprocessing step through constrained Kullback-Leibler divergence (KLD) minimization. In particular, a semidefinite programming (SDP) problem is formulated and solved to find a Gaussian TDOA posterior closest in terms of KLD to the unconstrained one while satisfying all inequality constraints. Simulations show that the newly developed algorithm outperforms the one we recently proposed to impose the inequality constraints via probability density function (PDF) truncation.