In complex electromagnetic environments, an electronic support measures (ESM) receiver intercepts radar pulses from multiple emitters, which are subsequently represented as an interleaved sequence of pulse descriptor words (PDWs). For downstream tasks, such as tracking and threat evaluation, the radar intelligence system must recover individual emitters from this interleaved signal without prior knowledge of their number or parameters, making deinterleaving a critical challenge. Towards this, the letter presents a novel unsupervised clustering-based three-stage radar pulse deinterleaving pipeline and its real-time deployment on a low-cost embedded processor. Stage I forms the initial clusters using the proposed Separability-Adaptive Distance (SAD), whose weights are derived from the data through three fused histogram-separability statistics, with an adaptive minimum cluster size preserving sparse low-PRF emitters. Stage II recovers pulse repetition interval (PRI) structure from time of arrival (TOA) differences, and Stage III reunites the fragments of individual emitters through an optimal-transport merge on log-PRI distributions. Importantly, the proposed pipeline operates with $O(N\log N)$ computational complexity, enabling real-time operation, while attaining state-of-the-art performance with an adjusted Rand index of 0.908 on a physics-constrained synthetic benchmark.
Reliable GNSS positioning in dense urban environments depends on assessing satellite visibility and identifying measurements degraded by non-line-of-sight (NLOS) reception and multipath. This survey organizes reception-state inference into three complementary evidence families: receiver-side signals, 3D maps and environmental models, and auxiliary sensors. Receiver-side approaches span antenna/electromagnetic, tracking/baseband, and observable-domain evidence, interpreted through rule-based, statistical, machine-learning, and deep-learning methods. Map-based approaches are examined by reviewing environmental representations and methods such as geometric screening, shadow matching, 3D ray tracing, and neural implicit visibility prediction. IMU, camera, and LiDAR measurements provide complementary kinematic, visual, and geometric evidence for online screening. Across these families, we compare accuracy, interpretability, hardware and calibration requirements, map dependence, computational cost, and suitability for real-time deployment. We identify open challenges in dataset quality and labeling, domain generalization, map freshness, and resource-efficient implementation. The resulting synthesis connects signal processing, environmental modeling, and sensor fusion to guide the design of robust urban GNSS measurement-screening systems.
We are developing a commensal, passive Space Domain Awareness (SDA) satellite detection system which utilises the University of Tasmania’s radio telescopes. Observations were conducted of the actively transmitting Low Earth Orbit (LEO) Aeronomy of Ice in the Mesosphere (AIM) satellite in the S-band frequency range. The Doppler shift of the observed passes was compared to the expected frequency of arrival as predicted by publicly available Two-Line Elements (TLEs). This was used to compare the accuracy of TLEs taken at different epochs. The TLEs were used to remove Doppler shift from the signal, allowing the downlink frequency to be recovered. The TLE predicted Doppler S-curve was compared with the observed AIM satellite downlink by way of calculating the Root-Mean-Square Error (RMSE). This allowed improvements to be made to one of the terms in the TLE set to make it more closely match the data. The B$^*$ drag term was selected for improvement, as changing it led to the greatest improvement in alignment with the data among the terms quantified in the TLE set. Given an already highly accurate TLE was used, improvement was small, on the order of 1 percent. The impact of a time delay introduced between the signal as measured and the signal as predicted using a TLE on the RMSE was investigated, and measurements of the AIM satellite were found to agree with the TLE predictions to the second. Finally, the satellite signal was compared against the TLEs of neighbouring satellites at the time of observation. The AIM satellite TLE had the lowest RMSE when compared with the data. This demonstrated a method to select satellite TLEs that best fit the data automatically, as quantified by the RMSE.
Aiming at the unclear ballistic characteristics of a certain type of small-caliber projectile under high dynamic conditions, this paper proposes a full-range graded measurement framework to acquire actual flight data. Based on the measured data, the projectile's motion law and ballistic environmental force characteristics were systematically analyzed. Data analysis results show that the instantaneous overload peak during the launch phase reaches 76,147.24 g, and the maximum initial rotational speed is 1,160 r/s. Furthermore, the consistency between multi-channel geomagnetic spectrum analysis and rotational speed calculation results further verifies the reliability of the proposed measurement method. Following a technical approach of “innovative hardware acquisition + synergistic algorithm processing,” this study provides key measured data support for ballistic calculation and the design of anti-high overload navigation components for small-caliber projectiles. This study establishes a systematic measurement and analysis framework for high-dynamic small-caliber projectile environments, addressing the fundamental range–sensitivity trade-off and the confined space integration constraints that prevent direct adoption of existing sensor architectures. The measured data reveal, for the first time, the launch transient characteristics (a 7.6×10⁴ g peak of about 6 ms duration with 0–6,000 Hz excitation) and the spin-rate decay law (1,160 r/s to 462.30 r/s) of 35 mm projectiles under actual flight conditions.
Orbital data centers (ODCs) and space-based solar power (SBSP) are converging on the same orbital shell. Most ODC proposals target dawn–dusk sun-synchronous orbit because it almost never enters Earth's shadow, so a compute node can run without a large battery. That orbit is crowded, and it is poorly placed for latency-sensitive AI inference. This article asks whether SBSP can let compute leave it: sunlit relays would beam power to ODCs in other orbits, and orbital compute would become the first real in-space customer for SBSP. We frame the pairing as a fractionated architecture, the Sun-Side Cloud, and test it with an orbital, optical, and carbon model whose uncertain inputs are all set in the relay's favor. Three results bound the idea. First, relays in the dawn–dusk plane shrink an inclined client's eclipse but cannot erase it: for part of every precession cycle the middle of Earth's shadow is hidden from them, so clients keep a residual battery. Second, reaching that shadow core takes links of several thousand kilometers, where a sun-sized receiver captures only a small part of the beam. Third, once relay mass scales with the power it delivers, beamed power beats an onboard battery on carbon only above roughly 10–20% end-to-end efficiency and with tens of clients per fleet. Today's technology falls an order of magnitude short, so near-term value must come from the orbit itself. We turn these thresholds, together with the cooling and computing bottlenecks that no orbit removes, into a research agenda.
