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.

One of the challenges encountered when processing streaming data is a change of the data distribution, which is called concept drift. It has been shown that ensemble methods are effective in reacting to such a change. However, so far it has not been investigated how the architecture and configuration of the ensemble, as well as the properties of the scenario, influence the prediction accuracy if the ensemble members (experts) are Long Short-Term Memory networks with an internal state. This paper evaluates six ensemble architectures in several configurations with regards to their suitability for processing streaming data with sudden, recurring concept drift. The evaluation with a public dataset shows the impact of the architecture and configuration on the ensembles’ accuracies, as well as the influence of the concepts’ stability periods and the Long Short-Term Memory experts’ internal states under several conditions.

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.

Traditional formulations of the well-established Kalman filter build upon prediction models which are linear and Gaussian, moreover they usually adopt the Markov property which excludes any form of long-term temporal dependencies. However, targets might follow specific behavioural patterns based on, e.g., their origin or destination, therefore time dependencies become highly relevant. In this article, the recently developed Mnemonic Kalman Filter is analysed which predicts the full Gaussian density of a target based on its previous position using a recurrent neural network with Long Short-Term Memory. For comparison, a simpler Long Short-Term Memory Kalman Filter is introduced which only provides a prediction of the target state vector. The presented experiments suggest that the learning-based approaches are highly relevant for time-dependent scenarios with low detection rates or possible occlusions. Furthermore, uncertainty estimation plays an important role in the filtering process.

Analytic dynamic models for target estimation are often approximations of the potentially complex behaviour of the object of interest. Its true motion might depend on hundreds of parameters and can involve long-term temporal correlation. However, conventional models keep the degrees of freedom low and they usually assume the Markov property to reduce computational complexity. In particular, the Kalman Filter assumes prior and posterior Gaussian densities and is hence restricted to linear transition functions which are often insufficient to reflect the behaviour of a real object. In this letter, a Mnemonic Kalman Filter is introduced which overcomes the Markov property and the linearity restriction by learning to predict a full transition probability density with Long Short-Term Memory networks.

This paper adapts and extends the previously published hierarchical modelling approach of multifunction radars as systems that speak a language. We propose Long Short-Term Memory neural networks for learning the emitters' grammar and predicting their emissions. The approach is demonstrated using simulations of an airborne multifunction radar with three different resource management techniques of varying complexity. A comparison with simple prediction strategies shows that a huge improvement in accuracy can be achieved by using Long Short-Term Memory networks for predicting complex radar emitter behaviour.

In many applications, objects of interest navigate in the same environment with unimportant objects that show similar motion behaviours. One prominent example is maritime surveillance in the presence of sea clutter since the sea often looks like a strongly fluctuating population of real targets due to the temporal correlation found in radar measurements of the sea surface. Conventional clutter models usually do not account for temporal correlation but model clutter as spontaneous instances of false measurements. In contrast, it would be desirable to describe such “undesired targets” with their own mathematical model in order to distinguish them properly from the population of true targets. This paper presents a variation of the Panjer Probability Hypothesis Density (PHD) filter which propagates two populations at the same time, assuming their independence. The performance of the proposed method is analysed on simulated data using a Gaussian-Mixture implementation.