Radio Tomographic Imaging and Tracking of Stationary and Moving People via Kernel Distance

Transcription

1 Radio Tomographic Imaging and Tracking of Stationary and Moving People via Kernel Distance Yang Zhao, Neal Patwari, Jeff M. Phillips, Suresh Venkatasubramanian April 11, 2013

2 Outline 1 Introduction Device-Free Localization Limits of Previous Methods 2 Methods Histogram Difference Imaging and Tracking via Kernel Distance 3 Experiments and Results Experiments Results 4 Conclusion

3 Outline 1 Introduction Device-Free Localization Limits of Previous Methods 2 Methods Histogram Difference Imaging and Tracking via Kernel Distance 3 Experiments and Results Experiments Results 4 Conclusion

4 Device-Free Localization Device and Device Free Localization of People T Radio device localization: RFID tag, Mobile Phone Device free localization (DFL): Idea: directly use human motion/presence as signal Applications: emergency response, smart homes, context-aware computing, etc.

5 Device-Free Localization Received Signal Strength (RSS)-Based DFL RSS: available in standard wireless devices Fingerprint-based DFL: training is needed for each environment, not applicable for emergency situations Model-based DFL: [Patwari and Agrawal, 2008] models shadowing effects of people/objects on RSS, and proposes the idea of radio tomographic imaging (RTI) Patwari and Agrawal. Effects of Correlated Shadowing: Connectivity, Localization, and RF Tomography. In Proc. of the 7th ACM/IEEE IPSN, April 2008.

6 Limits of Previous Methods Previous DFL Methods Shadowing-based RTI: first RTI method to image shadowing loss due to human presence, not robust to non-line-of-sight (NLOS) environments; Variance-based RTI (VRTI) [Wilson and Patwari, 2011]: works in NLOS environments, but cannot locate stationary people; Sequential Monte Carlo (SMC) [Wilson and Patwari, 2012]: can locate stationary and moving people, but requires empty-area calibration, and is not a real-time method. Wilson and Patwari. See-Through Walls: Motion Tracking Using Variance-Based Radio Tomography Networks. IEEE Trans. Mobile Computing, May 2011, pp Wilson and Patwari. A Fade Level Skew-Laplace Signal Strength Model for Device-Free Localization with Wireless Networks. IEEE Trans. Mobile Computing, June 2012, pp

7 Limits of Previous Methods Goal: Break These Limits Features RTI VRTI SMC Through-wall? No Yes Yes Stationary people? Yes No Yes Real-time? Yes Yes No Empty-area calibration? Yes No Yes Table: Features of previous DFL methods. Goal: a real-time method capable of imaging both stationary and moving people in both LOS and NLOS environments without training or empty-area calibration.

8 Outline 1 Introduction Device-Free Localization Limits of Previous Methods 2 Methods Histogram Difference Imaging and Tracking via Kernel Distance 3 Experiments and Results Experiments Results 4 Conclusion

9 Histogram Difference Idea: Use RSS Histogram Instead of RSS Mean or Variance Notation of RSS histogram at time n: h n = i w n,i I y i (1) where y i : RSS at time i (discrete value, quantization 1 dbm) N = y max y min + 1: range of RSS histogram I: N-length indicator vector w n,i : weight for I y i

10 Histogram Difference Two Weighting Schemes Uniform weight (FIR filter) w n,i = { 1 T 0 i T 1 0 otherwise (2) Exponentially weighted moving average (EWMA) w n,i = { β(1 β) n i i n 0 otherwise (3) where 0 < β < 1 is the forgetting factor

11 Histogram Difference Two Types of RSS Histograms EWMA: an IIR filter with lower computational and memory complexity h n = (1 β)h n 1 + βi y n (4) only requires current RSS y n and previous histogram h n 1. Long-term histogram (LTH) q and short-term histogram (STH) p FIR scheme: LTH q with a high T value IIR scheme: LTH q with a low β value (β = 0.05 for LTH; β = 0.9 for STH)

12 Histogram Difference Observations of RSS Histograms Online LTH (IIR using online data) is similar to empty-area LTH (FIR using offline data) STH with people is different from LTH STH w/o people is similar to LTH Idea: quantify the difference between online LTH and STH Frequency Online LTH Empty room LTH STH with person STH w/o person RSS (dbm) Figure: RSS histograms (same link).

13 Histogram Difference Histogram Difference Metrics Many ways to measure the difference D(p, q) between p and q: Earth mover s distance (very computationally expensive) Kullback-Leibler divergence (D KL (p, q) = k p k log p k q k ) Kernel distance (symmetric distance metric) [Joshi, et al. 2011] Joshi, et al. Comparing Distributions and Shapes Using the Kernel Distance. In Proc. of the 27th ACM symposium on computational geometry, June 2011.

14 Histogram Difference Kernel Distance Definition: D K (p, q) = p T Kp + q T Kq 2p T Kq, (5) where K is an N by N kernel matrix from a 2-D kernel function. Gaussian kernel: K(y j, y k ) = exp ( y j y k 2 σ 2 G ), where y j and y k are the jth and kth elements, and σg 2 is the kernel width parameter. Epanechnikov kernel: ) K(y j, y k ) = 3 4 (1 y j y k 2 I σe 2 yj y k σe 2, where I a is the indicator function.

15 Histogram Difference Efficient Computation: O (N) Instead of O ( N 2) Rewrite (5) as: D(p, q) = (K 1 2 p) T K 1 2 p + (K 1 2 q) T K 1 2 q 2(K 1 2 p) T K 1 2 q Let u = K 1 2 p, v = K 1 2 q: Updating u n and v n : D(p, q) = u v l2 u n = (1 β p )u n 1 + β p K 1 2 Iy n v n = (1 β q )v n 1 + β q K 1 2 Iy n where the term K 1 2 I y n is simply the y n th column of matrix K 1 2.

16 Histogram Difference Example of Kernel Distance RSS (dbm) Time (n) Kernel distance Figure: RSS ( ) and kernel distance (+) time series for a link which a person crosses at n = 23 and n = 120.

17 Imaging and Tracking via Kernel Distance RTI Idea Divide the area into P pixels Estimate pixel values x = [x 1, x 2,, x P ] T, which represent human presence, from RSS link measurements and model W [Patwari and Agrawal, 2008] Figure: One example of RTI image estimate.

18 Imaging and Tracking via Kernel Distance Kernel Distance-Based Radio Tomographic Imaging (KRTI) Notation: Let d = [d 0,..., d L 1 ] T denote a vector with L kernel distances, d l = D(p l, q l ) Problem: Estimate image ˆx from d and model W (ill-posed inverse problem L < P) Regularized least squares solution: ˆx = Π K d where Π K = (W T W + αc 1 x ) 1 W T. where α is the regularization parameter, and C x is the covariance of the image used in regularization.

19 Imaging and Tracking via Kernel Distance Localization and Tracking The location of a person is estimated as: ẑ = r q where q = arg max ˆx p p where ˆx p is the pth element of vector ˆx. For a moving person: Use a Kalman filter on the localization estimates to track locations over time. Localization and tracking of multiple people can be achieved from KRTI images.

20 Outline 1 Introduction Device-Free Localization Limits of Previous Methods 2 Methods Histogram Difference Imaging and Tracking via Kernel Distance 3 Experiments and Results Experiments Results 4 Conclusion

21 Experiments Experiment Testbed DE C B A ZigBee radio node: 2.4 GHz, IEEE Spin: token passing protocol; when one transmits, others measure RSS DE C B A Packet data: node ID and measured RSS values Laptop-connected note listens all traffic to record all pairwise RSS

22 Experiments Experiments 34 radio nodes are deployed around a 9 m by 8 m (Exp. 1-4) or a 12 m by 5 m (Exp. 5) area to locate a stationary person (Exp. 1) or a moving person (Exp. 2-5). Name Task Description Exp.1 stationary person calm day through-wall Exp.2 moving person calm day through-wall Exp.3 moving person windy day with fans Exp.4 moving person windy day with fans Exp.5 moving person at a cluttered bookstore Table: Experimental datasets (new data Exp. 3 and 4 reported in this paper is available at

23 Experiments Experimental Layout (through-wall) 8 Kitchen Door 6 Door Y (m) 4 2 Living room Tree 0 2 Nodes on stands Camera Nodes not on stands X (m) Figure: Experiment layout of Exp. 1-4.

24 Experiments Experimental Layout (cluttered bookstore) Y (m) A D RF sensors Shelves Path X (m) B C Figure: Experiment layout and picture of Exp. 5.

25 Results Imaging of a Stationary Person (Exp. 1) m m m m Figure: Imaging results of a stationary person (true location shown as ) from KRTI (left) and VRTI (right).

26 Results Localization of a Stationary Person (Exp. 1) Y (m) 4 Y (m) Known locations Estimates RF sensors 2 0 Known locations Estimates RF sensors X (m) X (m) Figure: Location estimates of a person standing at twenty locations from KRTI (left, average error 0.71 m, 0.03 sec per estimate) and SMC (right, average error 0.83 m, 3 to 4 minutes per estimate).

27 Results Localization of a Moving Person (Exp. 2-5) Figure: RMSE comparison with variance-based methods VRTI and subspace variance-based radio tomography (Zhao and Patwari. Noise Reduction for Variance-Based Device-Free Localization and Tracking. In Proc. of the 8th IEEE SECON, June 2011.)

28 Results Tracking of a Moving Person Y (m) X (m) Figure: Kalman filter tracking result of Exp. 5 (true path shown as dash line).

29 Outline 1 Introduction Device-Free Localization Limits of Previous Methods 2 Methods Histogram Difference Imaging and Tracking via Kernel Distance 3 Experiments and Results Experiments Results 4 Conclusion

30 Conclusion Using kernel distance in radio tomography allows us to locate both stationary and moving people in an LOS or non-los environment in real-time; Instead of using empty-area calibration, KRTI uses online calibration, which enables applications in emergency situations; Real-world experiments show that KRTI outperforms previous DFL methods in localization accuracy and computational efficiency.