Adaptive Mobile Sensor Positioning for Multi-Static Target Tracking

Abstract

Unmanned Air Vehicles (UAVs) are playing an increasingly prominent role in both military and civilian applications. In this paper, we focus on the use of multiple UAV agents in a target tracking application where performance is improved by exploiting each agent's maneuverability. Local time-delay and Doppler measurements made at each UAV are used as inputs to an Extended Kalman Filter (EKF) which tracks the target's position and velocity. Two simple metrics are defined to quantify the accuracy of the tracking algorithm, and heading feedback to the UAVs is used to minimize the metric and improve tracking performance. A simplified version of one of the algorithms that reduces computational complexity is also presented. Simulations demonstrate the significant improvement that results when the UAV sensors are allowed to be optimally positioned during tracking.

Radar network topology optimization for joint target position and velocity estimation

“…Previous studies mostly focus on the selection of the radar network configuration that ensures only accurate target localization. However, a number of radar applications require knowledge of the full target state vector, which includes not only the location, but also the velocity of the target at each time instant [16]. Additionally, the use of the Doppler shift provides higher detection probability in a strong clutter [17].…”

Sensor selection for estimation, filtering, and detection

“…The expression in (5) is valid as long as the observations across the sensors are independent. The expression in (5) indicates that every measurement provides some additional information, and thus the information provided by all the measurements reduces the uncertainty. We can now write the FIM in (5) using the variable w as F(w, θ) = M m=1 w m F m (θ).…”

“…Sensor deployment can also be interpreted as a sensor selection problem in which the best subset of the available sensor locations are selected subject to a performance constraint. Sensor selection has been applied to a wide variety of problems: dynamical systems [1]- [5], network monitoring [6], field estimation [7], array optimization [8], sourceinformative sensor identification [9], anchor placement [10], and outlier detection [11].…”

Coordinated Standoff Tracking Using Path Shaping for Multiple UAVs

“…Monitoring and subsequent tracking of a moving ground target of interest is one of the important capabilities of UAV(Unmanned Aerial Vehicle)s required to increase situational awareness and to take proactive measures against unknown intents of the threat [1], [2], [3], [4]. In performing such missions, UAVs are to keep a certain distance from the target known as a standoff distance while maintaining a prescribed inter-vehicle angular separation in order to track it without being noticed from the target and simultaneously to maximize target information acquisition.…”