Path planning for two unmanned aerial vehicles in passive localization of radio sources

Detection, localization, and tracking of unauthorized UAS and Jammers

“…The swarm of UAS aims to achieve the Cramer-Rao lower bound on achievable localization error through maximization of the so-called D-optimality criterion [97], [98]. Authors of [98] used a Fisher information matrix (FIM) predictive model and a receding horizon maximization technique to plan feasible paths for the UAS that lead to the best achievable localization error bound. In other words, the UAS take into account the estimated trajectory of the target while determining their optimum paths.…”

Multi-UAV Collaborative Trajectory Optimization for Asynchronous 3-D Passive Multitarget Tracking

“…To tackle this challenge, several TO models and corresponding solution algorithms are proposed for target localization/tracking in [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26]. Tzoreff and Weiss [7] discuss online and offline TO problems for a single UAV in the presence of time of arrival (TOA) measurements, subject to speed and no-fly zone constraints.…”

“…The proximal policy method and the policy rollout algorithm are applied to TO problems to speed up the localization of an emitter with angle of arrival (AOA) measurements in [8] and [9], respectively. These TO schemes for single-UAV [7], [8], [9] do not require consideration of collision avoidance and communication distance constraints, whereas these constraints should be considered in multi-UAV collaborative TO (CTO) problems [10], [11], [12], [13], [14]. A receiver TO problem subjected to the minimal distance allowed to the emitter is considered in [10] to improve the localization performance, which is solved by the projected gradient (PG) method.…”

“…Then, Dogancay [10] extends their work to a multitarget localization environment [11]. In [12], a steering control approach is proposed for a pair of UAVs in the time difference of arrival (TDOA)-based localization of a single emitter. In [13], a noncausal TO scheme is addressed for the received signal strength (RSS)-based localization to obtain This work is licensed under a Creative Commons Attribution 4.0 License.…”

Bearing-Only Obstacle Avoidance Based on Unknown Input Observer and Angle-Dependent Artificial Potential Field

“…Another important issue and precondition of OA is to estimate obstacles’ states, such as positions and velocities, precisely. Given the a priori movement model of obstacles, the Kalman filter or its green variants [10,11,12,13,14] yelloware utilized to estimate states of obstacles. According to the basic principles, these methods can be divided into three classes.…”