Qiang Le

On exploiting propagation delays for passive target localization using bearings-only measurements

For many cases, target localization accuracy can significantly improve by accounting for the propagation delay between the target source and the sensors. This paper considers when it is advantageous to compensate for the propagation delay for the case of a network of nodes where each node consists of an array of sensors. Each node calculates a direction of arrival (DOA) from the raw data, and these DOAs are combined to localize the target. First, we consider the case when no prior data are given and the localization occurs using DOA measurements from a single snapshot. Such localization methods are necessary to initiate a target track. Finally, we investigate the case where an extended Kalman filter is used to aggregate measurements over multiple snapshots.

Target localization using proximity binary sensors

This works presents the maximum likelihood localization (ML) algorithm for multi-target localization using proximity-based sensor networks. Proximity sensors simply report a single binary value indicating whether or not a target is near. The ML approach requires a hill climbing algorithm to find the peak, and its ability to find the global peak is determined by the initial estimates for the target locations. This paper investigates three methods to initialize the ML algorithm: 1) centroid of k-means clustering, 2) centroid of clique clustering, and 3) peak in the 1-target likelihood surface. To provide a performance bound for the initialization methods, the paper also considers the ground truth target positions as initial estimates. Simulations compare the ability of these methods to resolve and localize two targets. The simulations demonstrate that the clique clustering technique out-performs k-means clustering and is nearly as effective as the 1-target likelihood peak methods at a fraction of the computational cost.

Target tracking using proximity binary sensors

This paper investigates the feasibility of a mesh network of proximity sensors to track multiple targets. In such a network, the sensors report a detection when a target is within the proximity; otherwise, the sensors report no detection. Previous work has revealed the potential of target localization and tracking for a single target using these binary reports. This work introduces a particle-based probability hypothesis density (PHD) filter that is able to track multiple targets using the binary reports from a proximity sensor network. Furthermore, this work modifies another particle-based multitarget tracker for proximity sensors, namely the ClusterTrack, from 1-D tracking to 2-D. The simulations demonstrate that the PHD is able to outperform the Cluster- Track in terms of both accuracy of localization and estimating the number of targets.

Probability Hypothesis Density-Based Multitarget Tracking for Proximity Sensor Networks

An investigation of the feasibility of a mesh network of proximity sensors to track targets is presented. In such a network the sensors report binary detection/nondetection measurements for the targets within proximity. A new probability hypothesis density (PHD) filter and its particle implementation for multiple-target tracking in a proximity sensor network are proposed. The performance and robustness of the new method are evaluated over simulated matching and mismatching cases for the sensor models. The simulations demonstrate the utility of the PHD filter to both track the number of targets and their locations.

Effects of Operation Parameters on Multitarget Tracking in Proximity Sensor Networks

This paper investigates effects of operation parameters on multitarget tracking in proximity sensor networks. In such a network, the sensors report a detection when a target is within the proximity; otherwise, the sensors report no detection. Previous work has revealed the potential of multitarget tracking via the particle-based probability hypothesis density (PHD) filter when incorporating these binary reports. This work investigates how the sensor density, sensing range, and target separation affect the ability of the PHD filter to estimate the number of targets in the scene and to localize these targets (as measured by four different metrics). Two possible measurement models are considered. The disc model assumes target detection within a sensing radius, and the probabilistic model assumes 1/rα propagation decay of the source signal so that the probability of detection decreases with range r. The simulations demonstrate the simplistic disc model is inadequate for the PHD filter to estimate the number of targets, and the filter for the disc model exhibits difficulty to localize widely separated targets for low sensor densities. On the other hand, the more realistic probabilistic model leads to a PHD filter that can accurately estimate the number and locations of targets even for small target separations.

Joint tracking and power level estimation of multiple targets using a proximity sensor network

This work documents our investigation of multiple target tracking filters in proximity sensor networks when the target power levels are not known. The challenge is that when the targets are close, it is hard to determine if the sensor reports are the results of a loud target or multiple quiet targets. Given the binary measurements:1 for detection of targets and 0 for nondetection of targets, the works studies the feasibility of using the joint multitarget particle filter to estimate the number of targets, and at the same time estimate the 5D target states including the positions, velocities and power levels.

Segmentation and tracking of electrokinetic particles in microscopic video

The paper considers a fundamental particle separation problem in microfluidic devices,e.g.,microchannels. It is expected that particles with different electric characteristics flow into the different microchannels to achieve the separation purpose. The movement of the particles inside the microchannels is recorded in video as data. The objective of the research is to obtain the trajectories of the particles, and eventually establish the relationship between the particles dynamic characteristics and their electric characteristics. This paper proposes a framework that consists of raw image segmentation and multiple target trackers(multiple hypothesis tracker or Gaussian mixture probability hypothesis density tracker) to obtain the tracks of the particles.

Sensor selection for target localization in a network of proximity sensors and bearing sensors

The work considers sensor fusion in a heterogeneous network of proximity and bearings-only sensors for multiple target tracking. Specifically, various particle implementations of the probability hypothesis density filter are proposed that consider two different fusion strategies: 1) the traditional iterated-corrector approach, and 2) explicit fusion of the multitarget density. This work also investigates sensor type (proximity or bearings-only) selection via the Renyi entropy criteria. The simulation results demonstrate comparable localization performances for the two fusion methods, and they show that sensor type selection usually outperforms single sensor type performance.

Design of operation parameters to resolve two targets using proximity sensors

This work provides a design method to achieve a specified probability of resolution for two target localization via a wireless network of proximity sensors. Proximity sensors simply report a single binary value indicating whether or not a target is near. The design provides the density and threshold settings to achieve the given probability of resolution for targets separated by a specified distance. Simulations are included that demonstrate that at the designed sensor density and threshold values, the actual percentage of targets resolved achieves the desirable level of resolution for moderate to large target separations.

Energy-Aware Node Selection for Localization

This work presents node selection algorithms in a resource-constrained environment where the algorithms maintain a desirable geolocation accuracy while extending the tracking lifetime of the system. The sensor manager simply selects an active set of nodes for a given snapshot by maximizing a utility function under the geolocation constraint. The utility function serves as a surrogate for the effective network lifetime. Three utility functions are considered: energy consumption (EC), remaining energy (RE), and current lifetime (CL). A general h-horizon node selection algorithm is formulated for a generic utility function. Then, the node selection algorithms corresponding to the three utilities are tested for the myopic (h = 1) and h = 2 cases. The results indicate that the RE and CL utilities lead to longer effective network lifetimes than the EC utility.