on line training of the path-loss model in bayesian wlan indoor positioning
DESCRIPTION
Presentation on WiFi indoor positioning held in October '13, at IPIN 2014, Monbeliard, FranceTRANSCRIPT
On-Line Training of the Path-Loss Model in Bayesian WLAN Indoor Positioning
Luigi Bruno, Mohammed Khider and Patrick Robertson
Institute of Communications and Navigation, German Aerospace Center (DLR)Oberpfaffenhofen, Germany
30 m
Received Signal Strength – Why and How?
AP
Chart 2
Scatter plot
User-AP Distance [m]
Mea
sure
d R
SS
Chart 3
AP in the corridor
RS
S [dB
m]
Received Signal Strength - Example 1
Chart 4
Received Signal Strength - Example 2
AP in room
RS
S [dB
m]
Scatter plot
Mea
sure
d R
SS
User-AP Distance [m]
Chart 5
Received Signal Strength - Comparison
AP in the corridorAP in the room
Even in the same building at the same time and with same receiver,two APs can show different propagation models
• RSS based positioning - Fingerprinting
• Bahl and Padmanabhan (RADAR, 2000) • Haerleben et al. (2004), Yin et al. (2008), Fang et al. (2011)
• RSS based positioning – Adaptivity in path-loss techniques
• Li, (2006)• Bose et al. (2007)• Zhang et al. (2012)
• Other work relevant to us
• Particle filter based positioning: Arulampalam (2002)• Transmit power calibration: Addesso et al. (2010)• RSS model calibration: Nurminen et al. (IPIN 2012)
Related Work
Chart 6
• RSS Likelihood Gaussian in dBm
• Expected power: path-loss model
• We require:
• Transmit signal strength• Decay exponent
Indoor Radio Propagation Model
Chart 7
Ex. Least Squares Estimators
In corridor: h=-48 dBm, a=1.4 In room: h=-50 dBm, a=1.8
Observability of Parameters
Chart 8
k=1k=5k=20
Formal proof of observability can be given
L. Bruno and P. Robertson, “Observability of Path Loss Parameters in WLAN-Based Simultaneous Localization and Mapping”, IPIN 2013
• Simulative scenario:• 20 RSS i.i.d. at different
User-AP distances
• RSS Likelihood Function • Distances assumed known• Function of h and a• Depicted at k=1,5,20
Transmit power [dBm]
Exp
onen
t
Bayesian Filter
Chart 9
User’s state
Path-loss parameters
RSS measurements
• Bayesian algorithm: Compute recursively at each time step k and for each AP j:
• User’s state: Position and, eventually, velocity of the user• ‘Predicted’ by a theoretical user’s movement model (e.g. NCVM)• ‘Estimated’ by step measurements (accelerometers, compass,…)
• h and a independently sampled from suitable priors (here uniformartive)
• RSS measurements independent over j and k
• Implementation based on a Rao-Blackwellized Particle Filter
Rao-Blackwellized Particle Filter
Chart 10
User’s state
Path Loss parameters
• In our case:
• We discretize the propagation parameters on a finite grid
• A “hypothesis” is a pair of values
• Hypothesis probabilities are updated with any new RSS and each particle
Path-Loss Parameters Estimation
Chart 11
-40 -38 -36 -34 -32
1.52.02.53.03.5
Localization Algorithm
Chart 12
Initialize• Sample initial state for all particles
• Define grid for h and a
• Uniform prior for h and a
Iterations
particle 1 particle i
• Draw User’s State
• Weight on new RSS
• Update parameters pmf
• Draw User’s State
• Weight on new RSS
• Update parameters pmf
Marginalization on hypotheses
40 x 20 m testbed5 APs, 1000 particles
Movement model: NCVM
RSS noise
h and ~ a Gaussians
100 Monte Carlo trials
Simulations – RMSE
Chart 13
Our proposal
Best case: known parameters
Average parameters
Simulations – h Estimation Accuracy
Chart 14
Simulations – a Estimation Accuracy
Chart 15
• Two different office buildings
• Data collected by a pedestrian wearing a foot mounted IMU and holding either a laptop or a smartphone
• Normal WiFi network of the buildings – no ad-hoc additions
• Scenarios:• Building KN – DLR-OP (smartphone – OS Android)• Building TE01 – DLR-OP (laptop – OS Windows XP)
• Experiments:• Walks between 4 and 7 minutes long in corridors and offices
Experiments and Results
Chart 16
65 x 20 meters, 4 minutes walk, 4 APs
Equipment:• Foot-mounted IMU• Android Smartphone (Hand-held)
Experiment 1 - Trajectory
Chart 17
1000 particles RSS noise: s=5 dBm
Final best particle
Experiment 1 – Localization Error
Chart 18
Localization error [m] vs. time CDF of the error [m]
Only odometry
Fixed parameters
Our proposal
Experiment 2 - Trajectory
Chart 19
45 x 25 meters, 7 minutes walk, 4 APs
Equipment:• Foot-mounted IMU• Laptop - OS Windows XP
1000 particles RSS noise: s=5 dBm
Experiment 2 – Localization Error
Chart 20
Localization error [m] vs. time CDF of the error [m]
Chart 21
Opportunistic RSS: Need to Map?
If APs are unknown?
Can the building map help?
L. Bruno and P. Robertson, “Observability of Path Loss Parameters in WLAN-Based Simultaneous Localization and Mapping”, IPIN 2013
Session We1-IUT1: Tomorrow at around 10.45
Thank you!
Contacts:Luigi Bruno, PhDPhone: +49 - 8153 28 4116Email: [email protected], [email protected]
Department of Communication Systems Institute of Communications and NavigationGerman Aerospace Center Weßling, Germany