GNSS receivers process signals with very low received power levels (<−160 dBW) and, therefore GNSS signals are susceptible to interference. Interference mitigation algorithms have become common in GNSS receiver designs in both professional and mass-market applications to combat both unintentional and intentional (jamming) interference. Interference excision filters using fast Fourier transforms (FFTs) have been proposed in the past as a powerful method of interference mitigation. However, the hardware implementations of this algorithm mostly limited their use to military GNSS receivers where greater power and resources were available. Novel implementation of existing FPGA technology should make interference mitigation feasible with limited hardware resources. This paper details the practicalities of implementing excision filters on currently available FPGAs trading off the achievable performance against the required hardware resources. The hardware implementation of the FFT excision mitigation algorithm is validated with the GNSS software receiver. The results indicate that the desired performance of the developed algorithm has achieved the expectations and can provide significant improvement on mitigation techniques in current GNSS receiver hardware. Two hardware implementation designs (fixed-point and float-point data type format) are developed and compared to achieve the optimal design that can provide the best performance $\left(C/No\right)$ with the possible minimum hardware resources.

The JamSail mission is an educational CubeSat aiming to design, develop, and demonstrate two new technologies on a small satellite, tentatively scheduled for launch no earlier than 2026. When launched, JamSail will demonstrate the functionality of two new payloads in low Earth orbit. First, a flexible, low-cost GNSS interference detection payload capable of characterising and geolocating the sources of radio interference regarding the E1/L1 and E5a/L5 bands will be demonstrated on a global scale. The data produced by this payload can be used to target anti-interference actions in specific regions and aid in the design of future GNSS receivers to better mitigate specific types of interference. If successful, the flexibility of the payload will allow it to be remotely reconfigured in orbit to investigate additional uses of the technology, including a potential demonstration of GNSS reflectometry aboard a CubeSat. Second, a compact refractive solar sail will be deployed that is capable of adjusting the orbit of JamSail in the absence of an on-board propellant. This sail will be used to gradually raise the semi-major axis of JamSail over the span of the mission before being used to perform rapid passive deorbit near the end-of-life juncture. Additionally, self-stabilising optical elements within the sail will be used to demonstrate a novel method of performing attitude control. JamSail is currently in the testing phase, and the payloads will continue to be refined until the end of 2024. This paper discusses the key objectives of the JamSail mission, the design of the payloads, the expected outcomes of the mission, and future opportunities regarding the technologies as a whole.

Radio frequency interference (RFI) presents a growing threat to applications, ranging from surveying and construction to remote sensing and GNSS reflectometry (GNSS-R). RFI inevitably degrades the quality of GNSS-R observation data and data inversion. Due to ongoing geopolitical conflicts, the need to detect and geolocate these RFI sources worldwide has been increasing. This paper presents an analytical method based on the Doppler shift and Doppler rate measurements using a single Low Earth Orbit (LEO) satellite to detect and geolocate RFI transmitters. The methods are demonstrated using RFI-simulated data generated with the Orolia Skydel GNSS simulator and validated against historical Cyclone GNSS (CYGNSS) raw IF data. The geolocation method provides an unambiguous, converged, and stable solution through an optimisation process that estimates the location of the RFI transmitter. The RFI detection method is based on the short-time Fourier transform (STFT), which is robust in low signal-to-noise ratio (SNR) environments, operating reliably down to -35 dB. The geolocation method is computationally efficient, operating on short data windows (30-60 seconds) and accommodating different types of interference. Two CYGNSS raw IF datasets were selected for detection and geolocation. The geolocation algorithm produced a converged and consistent estimated RFI transmitters at the global minimum of the cost function, while also providing accurate vertical positioning of the RFI sources through the use of the Copernicus GLO-30 Digital Elevation Model (DEM). The geolocation accuracy for the simulated RFI scenarios was approximately within (20-50) meters, while for overhead scenarios it was 800 meters, reflecting the sensitivity of the analytical model that has been addressed in this paper. The end-to-end simulation allows assessments of the accuracy in a variety of scenarios, while the CYGNSS raw data demonstrate the method and techniques applied to current in-orbit data.

Image compression is a crucial field driven by advancements in communication and imaging technologies. Its primary goal is to achieve low bit rates while maintaining high-quality image reconstruction. Compression is essential in digital image processing, multimedia applications, and medical imaging. Various algorithms exist for image compression and reconstruction, each differing in efficiency. Compressive sensing (CS) algorithms, commonly used for radar data reconstruction, require iterative computations that demand significant processing power and time, limiting real-time applications. To overcome these challenges, this study proposes a parallel-pipelined processing approach to enhance compression and reconstruction efficiency. The method accelerates processing speeds, increases data throughput, and optimizes performance by reducing data size. The proposed approach divides image data into multiple parallel processing branches, significantly reducing computational cycles. This results in faster execution and improved real-time applicability. MATLAB simulations and field-programmable gate array (FPGA) hardware implementations have been conducted to validate the system’s effectiveness. The results demonstrate that the parallel-pipelined method significantly enhances efficiency compared to traditional approaches, making it suitable for applications requiring high-speed image processing, such as satellite imaging and medical diagnostics.