This paper presents an exact quality of service (QoS)-based radar resource management (RRM) method that manages multiple radar sensors, while also taking task dependencies into account. This contrasts with other approaches that mostly do not consider dependencies and assume independence among the tasks. However, this simplification is generally incorrect. Our method optimizes the tasks' assignment to the radar sensors as well as the selection of the radar control parameters jointly, while also exploiting dependencies between tasks. The problem is formulated and solved as mixed-integer linear programming (MILP) optimization problem. A simulated tracking scenario shows that by exploiting task dependencies in combination with coordinating the task assignments among the sensors, our RRM method can improve the operational performance significantly in comparison to the benchmark algorithms.

This research paper investigates the anticipated improvement in emitter localization time simulating a UAV (unmanned aerial vehicle) sensor platform that allows for variable flight altitudes, contrary to maintaining a fixed flight altitude. The study aims to quantify efficiency gain and evaluates whether these gains justify the additional hardware and software complexities involved with variable flight control. The considered UAV sensor platform carries a radio-frequency (RF) direction-finder system. The sensor platform is maneuvered by a controller maximizing the Fisher information to minimize the required mission time until emitter localization. Additionally, a benchmark control strategy further introduced as Loitering is considered, which steers the platform in a circular maneuver around the emitter at constant radius. Simulations are conducted involving various parameters to thoroughly compare the altitude control modes and quantifying the improvements of enabling variable altitude control. The comparison reveals only minor improvements in the scenarios, which initialize the sensor platform at altitudes lower than $50[\mathrm{~m}]$. The effort required to fulfil the requirements for variable height control is discussed, with the conclusion that the effort does not outweigh the effect for UAVs with initial altitudes below $50[\mathrm{~m}]$.

The performance of data fusion and tracking algorithms often depends on parameters that not only describe the sensor system, but can also be task-specific. While for the sensor system tuning these variables is time-consuming and mostly requires expert knowledge, intrinsic parameters of targets under track can even be completely unobservable until the system is deployed. With state-of-the-art sensor systems growing more and more complex, the number of parameters naturally increases, necessitating the automatic optimization of the model variables. In this paper, the parameters of an interacting multiple model (IMM) filter are optimized solely using measurements, thus without necessity for any ground-truth data. The resulting method is evaluated through an ablation study on simulated data, where the trained model manages to match the performance of a filter parametrized with ground-truth values.

This paper addresses the problem of localizing a stationary RF emitter with a mobile UAV, equipped with a single directional antenna. By rotating around its vertical axis, it determines a bearing towards the emitter. Our interest is in optimally selecting the measurement positions to achieve a fast localization. The majority of such systems described in the literature use greedy planning to select the next measurement position. This work experimentally tests an algorithm that performs a non-myopic planning until the final localization step. The algorithm is based on the policy rollout principle and showed good performance in previous simulative studies. It is adapted to match the needs of a real world setup and evaluated in flight trials. Adaptions include the avoidance of close range measurements to prevent inaccurate measurements at high elevation, and the filtering of poor measurements.

Reinforcement learning is the problem of autonomously learning a policy guided only by a reward function. We evaluate the performance of the Proximal Policy Optimization (PPO) reinforcement learning algorithm on a sensor management task and study the influence of several design choices about the network structure and reward function. The chosen sensor management task is optimizing the sensor path to speed up the localization of an emitter using only bearing measurements. Furthermore, we discuss generic advantages and challenges when using reinforcement learning for sensor management.