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HyDNN-PDR: A Novel Hybrid Deep Neural Network Based Pedestrian Dead Reckoning Navigation System in Challenging Terrains

Award Information
Agency: Department of Defense
Branch: Army
Contract: W912CG-21-P-0005
Agency Tracking Number: A20B-T027-0070
Amount: $166,492.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: A20B-T027
Solicitation Number: 20.B
Solicitation Year: 2020
Award Year: 2021
Award Start Date (Proposal Award Date): 2020-12-23
Award End Date (Contract End Date): 2021-06-23
Small Business Information
20271 Goldenrod Lane Suite 2066
Germantown, MD 20876-1111
United States
DUNS: 967349668
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: Yes
Principal Investigator
 (301) 515-7261
Business Contact
 yingli Wu
Phone: (949) 596-0057
Research Institution
 the University of Nevada, Reno
 Tracy Wheeler
1664 N. Virginia Street
Reno, NV 89557-0002
United States

 (775) 784-7085
 Nonprofit College or University

The modernization of live training requires accurate dismounted soldier trainee position/tracking to enable the convergence of Live, Virtual, and Constructive (LVC) training environments. The position jitter or the random position changes make the training unreliable under the condition of GPS satellite signal attenuation or blockage. In this project, a novel artificial neural network based pedestrian dead reckoning navigation system is proposed to upgrade the real-time navigation information accurately even in a variety of challenging terrains. To provide accurate location information, the designed pedestrian dead reckoning navigation platform utilizes an innovative online and offline hybrid machine learning based sensing data processing technique to be applied to the portable, low stringent size, weight, and power constraints (SWaP-C) inertial sensing platform. The hardware platform is composed of multi-inertial sensors, pre-processing filters, a GPU processor and memory. The deep learning based pedestrian dead reckoning navigation algorithms extract and sensing data features and the pedestrian navigation pattern. Then the navigation error would be further reduced by incorporating the novel neural network structure (TS-LSTM). The self-organizing structure and Generative Adversarial Network (GAN) could also make the solution reliable in the scenarios of complex environments and challenging terrains. The preliminary results validate the proposed solution’s feasibility.

* Information listed above is at the time of submission. *

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