Target Localization and Tracking by Fusing Doppler Differentials from Cellular Emanations with a Multi-Spectral Video Tracker

Abstract

We present an algorithm for fusing data from a constellation of RF sensors detecting cellular emanations with the output of a multi-spectral video tracker to localize and track a target with a specific cell phone. The RF sensors measure the Doppler shift caused by the moving cellular emanation and then Doppler differentials between all sensor pairs are calculated. The multi-spectral video tracker uses a Gaussian mixture model to detect foreground targets and SIFT features to track targets through the video sequence. The data is fused by associating the Doppler differential from the RF sensors with the theoretical Doppler differential computed from the multi-spectral tracker output. The absolute difference and the root-mean-square difference are computed to associate the Doppler differentials from the two sensor systems. Performance of the algorithm was evaluated using synthetically generated datasets of an urban scene with multiple moving vehicles. The presented fusion algorithm correctly associates the cellular emanation with the corresponding video target for low measurement uncertainty and in the presence of favorable motion patterns. For nearly all objects the fusion algorithm has high confidence in associating the emanation with the correct multi-spectral target from the most probable background target.

Doppler Position Location: Single Observation Point Feasibility Regions

“…In [6] and [7], authors propose source localization methods based on TDOA, FDOA, and differential Doppler rate measurements. In [6], a gradient is proposed.…”

“…In [6] and [7], authors propose source localization methods based on TDOA, FDOA, and differential Doppler rate measurements. In [6], a gradient is proposed. The iteration process of the second step could cause local convergence or even divergence at low signal-to-noise ratio (SNR) conditions.…”

“…The iteration process of the second step could cause local convergence or even divergence at low signal-to-noise ratio (SNR) conditions. In [7], authors propose a source localization algebraic method using weighted least square (WLS) estimator taking into consideration estimation error, which is neglected in [6].…”

An Experimental Multi-Target Tracking of AM Radio-Based Passive Bistatic Radar System via Multi-Static Doppler Shifts

“…It is shown in [ 19 ] that a multi-sensor Bernoulli filter with multi-static Doppler-only measurements contaminated by unknown probability of false alarms and missed detections is able to perform multiple target tracking via numerical simulation examples. An algorithm for fusing data from a constellation of RF sensors detecting cellular emanations with the output of a multi-spectral video tracker to localize and track a target with a specific cell phone is present in [ 20 ]; the results show that it is possible to track multiple targets using Doppler differential measurements. However, few field experiments have been performed to demonstrate the target tracking performance in similar scenarios.…”

Energy-Level Jumping Algorithm for Global Optimization in Compressive Sensing-Based Target Localization

“…Driven by the commercial and military applications, localization for people, vehicles and devices has attracted increasing attention in Internet of Things [1,2,3]. The traditional satellite-based target localization system can be exploited in favorable outdoor scenarios, but this system cannot be widely used in indoor scenarios because of the lack of the sight to satellite and the high cost of the equipped wireless devices.…”