In the last decades, the Synthetic Aperture Radar (SAR) systems have become among the most reliable remote sensing instruments for monitoring the Earth's surface, due to their ability of large-scale analysis with ease. Precise results can be achieved due to the continuous improvement of sensors' capabilities and the development of coherent processing techniques, like SAR Interferometry and Tomography. A popular approach from this topic is based on the detection of stable scatterering mechanisms. In this sense, two detection algorithms, CAESAR-D and SqueeSAR-D have been recently developed by adapting efficient interferometric phase filtering techniques. The relative performances of those detectors have been evaluated and compared under multiple hypothesis. This work represents a continuation of those studies, by analyzing the capabilities of the two algorithms in correspondence with the sensors' characteristics, this representing the main contribution of the paper. In this sense, two datasets of the same test area, acquired by different SAR sensors, Cosmo-SkyMed and Sentinel-1, are exploited.
With unmanned aerial systems (UAS) becoming more commonplace, the threat they pose also increases. Therefore, Counter-UAS (C-UAS) systems become increasingly important to protect critical infrastructure and/or public events. Because UAS are difficult targets to detect, multi-sensor systems are a preferred means of detection. In order to achieve a successful fusion of the involved sensors' data, the system needs to be calibrated. For non-stationary applications the C-UAS system has to be set up each time it is deployed and needs to be extrinsically calibrated each time as well. The process of setting up and calibrating is typically conducted by the operator of the system, who often are not experts in the sensors themselves. Therefore, we propose an automated calibration process using a cooperative UAS to estimate each sensors' biases in azimuth and elevation. Additionally, different mathematical measures are used to automatically interpret the success of the calibration and provide additional information to the operator. The method is applied and evaluated on real-world data, and its accuracy and robustness are assessed.
Datasets are a crucial element in the development of perception algorithms. They relate sensor measurement data to annotated reference information and allow for the deduction of sensor and object characteristics. In autonomous driving, the reference data commonly consist of semantic image segmentation, point-wise associations, or bounding box annotations. The dataset proposed in this work, however, aims to dig deeper into the evaluation of measurement principles and provides scanned 3D models of all vehicles together with a pose and continuous kinematics reference obtained by RTK-GNSS. Combined, the state of the complete dynamic surrounding of the sensor vehicle is known for any point in time. Subsequent reference formats can be easily computed in user-defined granularity. This dataset involves single-object and multi-object recordings with seven target vehicles. In particular, measurement effects such as occlusion, as well as reflections, can be evaluated, as the normals of the shape of the target vehicles are known. We describe the dataset, discuss the technical background of its development, and briefly present exemplary evaluations.
We present a gate-based, digitized adiabatic (QAOA-style) realization of the Bayesian filtering update on discrete grids. Our method encodes the likelihood into a diagonal cost Hamiltonian and implements the anneal via a symmetric Trotter sequence of mixer and cost blocks, avoiding state-conditioned gate constructions and any variational parameter training. We show theoretically that the measurement statistics exhibit the same effective inverse temperature as in the continuous anneal up to O(T-3 /p(2)) Trotter error, and that a single energy-scale parameter alpha(applied only to the cost) restores the target posterior variance after a one-shot calibration. On Gaussian test cases, the calibrated digitized anneal closely matches the analytical posterior while maintaining shallow, structured circuits that scale naturally with the register size. These results position digitized adiabatic filtering as a practical alternative to both analog annealing and state-conditioned gate approaches in quantum data fusion.
In this work, we establish the consistency and asymptotic efficiency of an online formulation of the maximum likelihood estimator for independent, non-identically distributed random variables. The proposed online approach has a computational cost of O(1) per measurement, offering a significant improvement over the O(n) cost required by the traditional formulation after the n-th observation. We demonstrate the applicability of the developed theory to the Direct Position Determination geolocation problem. Simulation results confirm that, under practically relevant conditions, both the online and traditional formulations achieve near-optimal estimation performance, while the online algorithm shows clear advantages in terms of computation time.
