1. Overview Objective Track an unknown number of targets using a wireless sensor network of binary sensors for real-time surveillance Issues Real-time operation Number of targets and initial states of targets are unknown Coarse measurements from binary sensors No classification information about the identities of targets Our Approach Multiple layers of data fusion for real-time operation Markov chain Monte Carlo data association (MCMCDA) for multi-target tracking Architecture Multi-Sensor Fusion To obtain finer position reports from binary measurements, we use spatial correlation among detections from neighboring sensors (Step 1) Compute pseudo-likelihoods (Step 2) Estimate target positions using clustering Experiment Successfully demonstrated at the DefenseAdvanced Research Projects Agency (DARPA) Network Embedded Systems Technology (NEST) final experiment on August 30, 2005 557 Trio motes (144 motes are used for the tracking demo) Passive infrared (PIR) motion sensors (range: 8m) Instrumenting Wireless Sensor Networks for Real-Time Surveillance Songhwai Oh, Phoebus Chen, Michael Manzo, and Shankar Sastry – UC Berkeley April 27, 2006 Multi-Target Tracking (MTT) Control System Base Station Local estimation of target positions from sensor network Regional estimates: number of targets, positions, velocities, and estimation error bounds Multi-Sensor Fusion (MSF) Tier-2 Tier-2 node 1 Multi-Target Tracking (MTT) Tier-2 node M Sensor Network Sensor measurements Tier-1 Multi-Track Fusion (MTF) Number of targets, positions, velocities, and estimation error bounds Why a Hierarchical Architecture? Measurements from a single sensor and its neighboring sensors are not sufficient to initiate, maintain, disambiguate, and terminate tracks; it requires measurements from distant sensors Distributed approach is not feasible for real-time surveillance due to the communication load and delay when exchanging measurements between distant sensors Centralized approach is not robust and scalable Requires real-time estimates (a) Detections of two targets by a 10 × 10 sensor grid (targets in ×, detections in, and sensor positions in small dots). (b) Pseudo- likelihood of detections. (c) Thresholded pseudo-likelihood. Estimated positions of targets are shown in (black) circles. Markov Chain Monte Carlo Data Association (MCMCDA) A Markov chain Monte Carlo approach (Metropolis-Hastings) for solving the data association problem in multi-target tracking. We can track an unknown number of targets with MCMCDA. Optimal Bayesian filter in the limit Exponential number of possible measurement-to-target associations Randomly sample over possible measurement-to-target associations (a) An example of measurements (each circle represents an observation and numbers represent observation times). (b) One possible measurement-to-target association Based on a set of efficient MCMC moves Trio mote Deployment Trio mote (sensor board) A Trio mote on a tripod Multi-target tracking Demo Pursuit-Evasion Game Demo Detection panel (upper left): Sensors are marked by small dots and detections are shown in large disks Fusion panel (lower left) shows the fused likelihood Estimated Tracks and Pursuer-to-evader Assignment panel (right) shows the tracks estimated by the MTT module, estimated evader positions (stars) and pursuer positions (squares).