observability of path loss parameters in wlan based simultaneous
DESCRIPTION
Presented at IPIN 2013, Montbeliard, FranceTRANSCRIPT
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Observability of Path Loss Parameters in WLAN-Based Simultaneous Localization and Mapping
Luigi Bruno and Patrick Robertson
Institute of Communications and Navigation, German Aerospace Center (DLR)Oberpfaffenhofen, Germany
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30 m
Received Signal Strength – Why and How?
Approaching Leaving
Closest point
• Proximity
• Distance
AP
Chart 2
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Chart 3
AP in a room
RS
S [dB
m]
Received Signal Strength – Coverage 1/2
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Chart 4
AP in the corridor
RS
S [dB
m]
Received Signal Strength – Coverage 2/2
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• Simultaneous Localization and Mapping done in robotics since ~25 years
• Smith, Self, Cheeseman (1990) • Leonard, Durrant-Whyte (1991)
• SLAM for pedestrians
• FastSLAM (Montemerlo, 2002)• FootSLAM (Robertson, since 2009)• ActionSLAM (Hardegger, IPIN 2012/13)
• RSS-based SLAM
• Wifi-SLAM (Ferris, 2007)• WiSLAM (Bruno, 2011)• Walkie-Markie (Shen, 2013)
• RSS model calibration: Nurminen (IPIN 2012)
Related Work
Chart 5
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We have:
Dynamic States: user’s pose user’s step colored noise on odometry
Static Maps: Physical environment map M WiFi map W for each AP
Measurements: Odometry (e.g. INS/NavShoe) RSS scan
Dynamic Bayesian Network
Chart 6
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Wi(fi-based) SLAM
Chart 7
• Bayesian algorithm: FastSLAM factorization (Montemerlo et al. 2002)
• Implemented by a Rao-Blackwellized Particle Filter
• Proposal Function:
• State is propagated in accordance with odometry
• Likelihood Functions:
• FootSLAM weighting (hexagonal edge crossing counters)
• RSS-based weight per each AP
• Any other in accordance with available sensors (e.g. Magnetic, Gyroscope,
Altimeter)
• WiFi Map: update each particle map at each RSS
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• RSS Likelihood Gaussian in dBm
• Expected power: path-loss model
• We require:• AP’s position• Transmit signal strength• Decay exponent
When are known
Indoor Radio Propagation Model
Chart 8
User
AP
r
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1 RSS alone Combine 3 RSS …5 RSS
…more RSS
Sequentially multiplying the RSS likelihoods...
Bayesian Method – Known Parameters
Chart 9
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Uncertainty of path-loss parameters has a relevant impact
Path-Loss Parameters
Chart 10
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Toy case: AP’s and user’s position known, no noise ( = 0 )
Given two measurements, solve:
Circular walk
Constant r
Ambiguity remains
Linear walk
2 RSS sufficient
Exact solution for h and a
Observability of Parameters – Toy Example
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Extend to the case of noisy RSS: asymptotic observability
• Claim: Given AP’s and user’s positions, joint asymptotic observability of the path-loss parameters is guaranteed if relevant changes in the user-AP distance are provided
1. Compute CRLB for the vector parameter
a. 1-RSS likelihood
b. K-RSS likelihood
c. Fischer Information Matrix
Observability of Parameters – Known Positions
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d. By inverting the FIM, we obtain the CRLB for (unbiased) estimator:
2. ML estimator is efficient: 1/K
Observability of Parameters – Known Positions
Chart 13
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• We discretize the propagation parameters on a finite grid
• A “hypothesis” is a pair of values
• Hypothesis probabilities are updated with any new RSS
-40 -38 -36 -34 -32
1.52.02.53.03.5
Hyp 1:
Hyp 2:
Hyp s:
Observability of Parameters – SLAM
Chart 14
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• AP’s position, given one hypothesis: Gaussian Mixture Model (GMM)
where
Initialization • GMM learnt from samples of the exact map• Expectation-Maximization algorithm for GMM (developed for speech
processing)• Maximum-likelihood solution with few iterations
Update• At any new RSS update the GMM• Implemented in closed form
Map Model Of AP’s Position
Chart 15
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Experiments and Results
Chart 16
• Three different buildings, walks between 4 and 10 minutes
• Data collected by a pedestrian wearing a foot mounted IMU and holding a laptop/smartphone
• Normal WiFi networks of the buildings – no ad-hoc additions
• Scenarios:• Building KN – DLR-OP (smartphone – OS Android)• Building MF - DLR-OP (smartphone – OS Android)• First floor of the building TE01 – DLR-OP (laptop – OS Windows XP)
• Up to 4 APs, no offline calibration
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4 minutes walk, 4 APs
Equipment:• Foot-mounted IMU• Android Smartphone (Hand-held)
Contours: AP’s position posterior pdf
1% of maximal value
Experiment 1
Chart 17
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Experiment 2 – Open space
Chart 18
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7 minutes walk
Equipment:• Foot-mounted IMU• Laptop (to collect RSS)
4 APs
Experiment 3 – Office With Loops
Chart 19
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Experiment 3 – Parameters pmf evolution
Chart 20
120 s (1/3 of the walk) 200 s (1/2 of the walk) 420 s (end walk)
Transmit signal strengthTransmit signal strengthTransmit signal strength
Exp
onen
t
Exp
onen
t
Exp
onen
t
• Solving ambiguity in parameter estimation
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Chart 21
How many representations does the physical environment have?
WiFi Maps Mag Maps Real Time Position
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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
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To implement the Bayesian filter we use a Rao-Blackwellized Particle Filter, in which we exploit DBN structure to reduce the complexity of state space sampling
In our case
• : Dynamic states
• : Static maps
RBPF Implementation
Chart 24
What about the WiFi map?