Angle-Of-Arrival Target Tracking Using A Mobile Uav In External Signal-Denied Environment

UAV Path Optimization for Angle-Only Self-Localization and Target Tracking Based on the Bayesian Fisher Information Matrix

“…As our ultimate objective is to optimize the target tracking performance by means of appropriate UAV manoeuvres, the 4 × 4 submatrix of the BCRLB, Σ 11,k , will be the focus of attention. This is different from previous work, where the entire Σ k was used [16,21]. In (33), the contributions of the target AOA and beacon bearing measurements (I k and J k , respectively) to the recursive BFIM are influenced by several factors, the most obvious of which are the angle noise variances σ 2 θ and σ 2 θ i…”

“…where only the readily available EKF estimates are used, thereby avoiding computationally expensive Monte Carlo simulations to compute the expectation over x k on the right-hand side of (32). Referring to (21), we note that in (33), the contribution of beacon bearing measurements is a sparse block matrix…”

“…As the UAV becomes close to the target, the target tracking performance improves if I k is considered alone. However, if the close proximity between the UAV and target translates into a significant increase in the UAV distance from the beacons, the UAV self-localization performance will degrade as the contribution of J k to Φ k , which is captured in the submatrix of Φ k , D k , diminishes with an increased ∥b i − s k ∥ [see (21)…”

“…Despite its immense potential for practical application, the problem of UAV path optimization for joint self-localization and target tracking has not received much attention in the literature. In [21], a Kalman filtering approach for self-localization and target tracking was proposed under the strong assumption of perfect knowledge of UAV orientation, which is not justified in practice. The optimization criterion adopted in [21] minimizes the trace of the covariance of Kalman filter state estimates, akin to [17,18].…”

“…In [21], a Kalman filtering approach for self-localization and target tracking was proposed under the strong assumption of perfect knowledge of UAV orientation, which is not justified in practice. The optimization criterion adopted in [21] minimizes the trace of the covariance of Kalman filter state estimates, akin to [17,18]. However, the entire state vector (including the velocity estimates) of the Kalman filter, rather than only target location estimates, is used in the optimization, which does not prioritize target tracking and increases the computational complexity as a result of increased matrix dimensions.…”