Trajectory Optimization for Target Localization With Bearing-Only Measurement

Abstract

This paper considers the problem of twodimensional (2D) constrained trajectory optimisation of a pointmass aerial robot for constant-manoeuvring target localisation using bearing-only measurement. A performance metric that can be utilised in trajectory optimisation to maximise target observability is proposed first based on geometric conditions. One-step optimal manoeuvre that maximises the observability criterion is then derived analytically for moving targets. The heading angle constraint is also incorporated in the proposed optimal manoeuvre derivation to support practical application. Numerical simulations with some comparisons are presented to validate the analytical findings.

Optimal Active Target Localisation Strategy with Range-only Measurements

“…This paper aims to to develop an analytically optimal active sensing strategy in consideration of physical constraints for non-manoeuvring target localisation with range-only measurements. This work is an extension of our previous results (He et al, 2019) to range-only scenarios. Similar to previous studies, the determinant of FIM is leveraged as the cost function in the optimisation problem.…”

“…Note that most approaches utilize numerical methods to find the optimal solutions and consequently require high computational burden. To alleviate the computational issue, an analytical solution was proposed in (He et al, 2019) using geometric analysis for bearing-only target localisation.…”

Optimal Cooperative Guidance Laws for Two UAVs Under Sensor Information Deficiency Constraints

“…Also they perform the key role in cooperative surveillance missions by enhancing the observability of the multi-measuring environment [ 7 ] and maximizing the mutual information obtained by multiple sensors [ 8 , 9 ]. Even though there have been lots of studies on this domain, complicated numerical trajectory optimization techniques are usually used to obtain the optimal time or energy path with the geometric constraints [ 10 , 11 , 12 ]. Considering the computational capacity of conventional guidance computers, numerical optimization techniques might not be suitable to swarm UAV systems as the computational complexity typically increases with the number of UAVs.…”

Expectation‐maximization‐based infrared target tracking with time‐varying extinction coefficient identification

“…Due to lightweight, low-cost, anti-electronic jamming, and anti-stealth, infrared (IR) sensors play an important role in reconnaissance and warning systems, target tracking systems, and so on. [1][2][3] In IR target tracking (IRTT), early researches mainly focus on designing nonlinear filters, including extended Kalman filter (EKF), modified polar coordinates EKF, pseudolinear tracking filter, unscented Kalman filter, particle filter, and so on. [4][5][6][7][8][9] Recently, multi-and dual-station schemes are becoming prevalent because of their ability to overcome the inherent weak observability of single station IRTT.…”