1. Detection, Classification and Tracking in a Distributed Wireless Sensor Network Presenter: Hui Cao

2. 2 Problem definition How can we classify the target (invader) in the sensor network.

3. 3 We can get information from one Node Onset time Offset time CPA time and value (Closed Point of Access) During time

4. 4 Summary Sensor networks requires decision fusion Majority voting is the most popular decision fusion method. It assumes all votes are equally accurate. Not all sensor decisions are equally accurate. Those closer to the target or with higher SNR will have better results.

5. 5 Summary (Cont’d) If source location can be estimated, such discrepancy can be exploited to improve the decision fusion accuracy. We formulate three different methods to combine sensor decisions based on their distance from the target and SNR, and we found encouraging results.

6. 6 Sensor Network Signal Processing Tasks Target Detection (CFAR + region fusion) Target Classification (ML+ region fusion ) Target Localization Target Tracking (Kalman Filter) D. Li, K.D. Wong, Y.H. Hu, A.M. Sayeed: Detection, Classification and Tracking of Targets. IEEE Signal Processing Magazine Vol. 19 Issue 2 pp. 17-29

7. 7 Some details…… CFAR: Constant false alarm rate The threshold is dynamically adjusted according to the noise variant to maintain the CFAR ML: Maximum likelihood

8. 8 Not all sensors are equal … Hypothesis: Classification accuracy is a function of target-sensor distance and SNR Each node’s classification rate depends on SNR. SNR is also roughly inversely proportional vehicle-node distance due to acoustic energy attenuation Experiment: Determine classification rate for different levels of distance, SNR

9. 9 Distance vs. Classification Rate

10. 10 SNR vs. Classification Rate

11. 11 Classification Rate as a Function of SNR and Distance

12. 12 Weighted Decision Fusion Optimal (linear) decision fusion † : perform weighted voting of individual results ( e i (x) for node i ) Weights ( w i for node i ) are proportional to classification rates † Z. Chair and P. Varshney “Optimal Data Fusion in Multiple Sensor Detection Systems”, IEEE Trans. AES, Vol. 22 No. 1, Jan. 1986, pp. 98-101

13. 13 Distance Based Decision Fusion (DBDF) Current system architecture allows detection and localization prior to classification, giving distance and SNR estimates. Accurate localization allows for estimation of probability of correct classification based on distance. Some events may be rejected by fusion algorithm as the distance or SNR figures fall outside the training data range. The majority voting can be used as a backup fusion method. Measurements: classification rate and acceptance rates.

14. 14 DBDF Approach 1: Maximum A Posteriori Decision Fusion Weighting factor as function of distance and SNR, determined using CFAR and EBL information. We formulate a Maximum A Posteriori (MAP) Probability Gating Network, using Bayesian estimation: Parameters: SNR, Distance grouping size P(x|d,s)P(d,s) estimated from experiment data.

15. 15 DBDF Approach 2: Distance Truncated Voting Simple majority voting performed among nodes close enough to the target. Decisions from other nodes are discarded. Parameter: max/threshold distance Reduces effect of localization error No decision will be made when vehicle is outside the distance threshold

16. 16 DBDF Approach 3: Nearest Neighbor Fusion Node closest to target will also have highest SNR, and hence the highest probability of correctness Region will assign same label as that assigned by the node closest to the target Lowest computational and communicational burden Accuracy of node to target distance critical in decision making

17. 17 Weighted Voting Schemes This amounts to assigning weights: 1. MAP Fusion 2. Distance Truncated Voting: 3. Nearest Neighbor: Baseline: simple Majority voting ( w i =1 for all i )

18. 18 Experiments All 4 methods are compared Data from SensIT SITEX02, November 2001 Distance groups by 20m, SNR groups by 5dB Distance truncated voting for 50m Nearest Neighbor Majority Voting Due to shortcomings in localization, experiments are run with different error levels in location estimate: σ = 0m, σ = 12.5m, σ = 25m, σ = 50m.

19. 19 Experiment Results: Classification Rate DTV

20. 20 Experiment Results Closest node gives highest acceptance, classification rates for accurate localization estimates MAP Fusion has smaller dependence on localization error than other methods All DBDF methods outperform simple majority voting due to the use of distance and SNR information.

21. 21 Further Work MAP Classifier allows for exclusion of those samples with low classification rates (i.e. only samples with w i > 0.5 are allowed). This will allow for reduction of communication bandwidth used for classification fusion. This method can be applied to other signal processing tasks. Website: http://www.ece.wisc.edu/~sensithttp://www.ece.wisc.edu/~sensit