Identification and further analysis of radar emitters in a contested environment requires detection and separation of incoming signals. If they arrive from the same direction and at similar frequencies, deinterleaving them remains challenging. A solution to overcome this limitation becomes increasingly important with the advancement of emitter capabilities. We propose treating the problem as blind source separation in time domain and apply supervisedly trained neural networks to extract the underlying signals from the received mixture. This allows us to handle highly overlapping and also continuous wave (CW) signals from both radar and communication emitters. We make use of advancements in the field of audio source separation and extend a current state-of-the-art model with the objective of deinterleaving arbitrary radio frequency (RF) signals. Results show, that our approach is capable of separating two unknown waveforms in a given frequency band with a single channel receiver.

In recent years, diffusion models (DMs) have become a popular method for generating synthetic data. By achieving samples of higher quality, they quickly became superior to generative adversarial networks (GANs) and the current state-of-the-art method in generative modeling. However, their potential has not yet been exploited in radar, where the lack of available training data is a long-standing problem. In this work, a specific type of DMs, namely denoising diffusion probabilistic model (DDPM) is adapted to the SAR domain. We investigate the network choice and specific diffusion parameters for conditional and unconditional SAR image generation. In our experiments, we show that DDPM qualitatively and quantitatively outperforms state-of-the-art GAN-based methods for SAR image generation. Finally, we show that DDPM profits from pretraining on largescale clutter data, generating SAR images of even higher quality.

Due to its description of a synchronization between oscillators, the Kuramoto model is an ideal choice for a synchronisation algorithm in networked systems. This requires to achieve not only a frequency synchronization but also a phase synchronization - something the standard Kuramoto model can not provide for a finite number of agents. In this case, a remaining phase difference is necessary to offset differences of the natural frequencies. Setting the Kuramoto model into the context of dynamic consensus and making use of the $n$th order discrete average consensus algorithm, this paper extends the standard Kuramoto model in such a way that frequency and phase synchronization are separated. This in turn leads to an algorithm achieve the required frequency and phase synchronization also for a finite number of agents. Simulations show the viability of this extended Kuramoto model.

We present a detailed analysis of the absorption properties of one of the tidal gas streams around the Whale galaxy NGC4631 in the direction of the quasar 2MASSJ12421031+3214268. Our study is based on ultraviolet spectral data obtained with the Cosmic Origins Spectrograph (COS) onboard the Hubble Space Telescope (HST) and 21cm-data from the HALOGAS project and the Green Bank Telescope (GBT). We detect strong HI Ly alpha absorption in the velocity range +550 to +800 km s^-1 related to gas from a NGC4631 tidal stream known as Spur 2. We measure a column density of log N(HI)=18.68pm0.15, indicating that the quasar sightline traces the outer boundary of Spur 2 as seen in the 21cm data. Metal absorption in Spur 2 is detected in the lines of OI, CII, SiII, and SiIII in a complex absorption pattern that reflects the multi-phase nature of the gas. We find that the average neutral gas fraction in Spur 2 towards 2MASSJ12421031+3214268 is only 14 percent. This implies that ionized gas dominates the total mass of Spur 2, which then may comprise more than 10^9 M_sun. No significant depletion of Si is observed, showing that Spur 2 does not contain significant amounts of dust. From the measured OI/HI column-density ratio we determine an alpha abundance in Spur 2 of 0.13pm0.07 solar ([alpha/H]=-0.90pm 0.16), which is substantially lower than what is observed in the NGC4631 disk. The low metallicity and low dust content suggest that Spur 2 represents metal-deficient gas stripped off a gas-rich satellite galaxy during a recent encounter with NGC4631.

In hostile environments, GNSS is a potentially unreliable solution for self-localization and navigation. Many systems only use an IMU as a backup system, resulting in integration errors which can dramatically increase during mission execution. We suggest using a fighter radar to illuminate satellites with known trajectories to enhance the self-localization information. This technique is time-consuming and resource-demanding but necessary as other tasks depend on the self-localization accuracy. Therefore an adaption of classical resource management frameworks is required. We propose a quality of service based resource manager with capabilities to account for inter-task dependencies to optimize the self-localization update strategy. Our results show that this leads to adaptive navigation update strategies, mastering the trade-off between self-localization and the requirements of other tasks.

Automotive self-localization is an essential task for any automated driving function. This means that the vehicle has to reliably know its position and orientation with an accuracy of a few centimeters and degrees, respectively. This paper presents a radar-based approach to self-localization, which exploits fully polarimetric scattering information for robust landmark detection. The proposed method requires no input from sensors other than radar during localization for a given map. By association of landmark observations with map landmarks, the vehicle's position is inferred. Abstract point- and line-shaped landmarks allow for compact map sizes and, in combination with the factor graph formulation used, for an efficient implementation. Evaluation of extensive real-world experiments in diverse environments shows a promising overall localization performance of $0.12 \text{m}$ RMS absolute trajectory and $0.43 {}^\circ$ RMS heading error by leveraging the polarimetric information. A comparison of the performance of different levels of polarimetric information proves the advantage in challenging scenarios.

It is shown that quantum illumination with three photons non-Gaussian states, where the signal is described by a two photons state and the idler is described by a one photon state, can outperform in sensitivity standard Gaussian quantum illumination in a high noisy background. In particular, there is a reduction in the probability due to an increase in the probability of error exponent by a factor $1/{N_S}$, where $N_S$ is the average number of photons per mode of the signal state.