The combination of Spatio-Temporal Adaptive Processing (STAP) and Controlled Reception Pattern Antennas (CRPAs) enhances the robustness of Global Navigation Satellite System (GNSS) anti-jamming receivers, particularly for autonomous launcher navigation where continuous Position, Velocity, and Time (PVT) availability is critical. In this architecture, STAP acts as a spatio-temporal filtering stage on array signals, while adaptive beamforming (ABF) algorithms compute and steer the filter weights to shape the antenna pattern and place spatial nulls under high-dynamics conditions involving strong carrier accelerations. Common techniques used to drive the STAP weights include Minimum Variance Distortionless Response (MVDR), Minimum Mean Square Error (MMSE), and Linearly Constrained Minimum Variance (LCMV).

This paper demonstrates that, despite their theoretical optimality, such ABF-driven STAP approaches may struggle in complex real-world conditions, particularly in the presence of significant launcher dynamics, including rapid attitude variations and high acceleration profiles. To illustrate these limitations, two complementary sets of results are presented. The first is based on simulated data generated using open-source tools such as gnss-sdr-sim, allowing controlled emulation of dynamic interference and motion scenarios. The second relies on testbed experiments conducted in a controlled, conducted environment at the European Commission’s Joint Research Centre (JRC).

The results show that variations in interference power, waveform type, and direction of arrival, combined with changes in GNSS signal geometry and carrier dynamics, introduce biases of varying magnitude. Significant nonlinear effects and distortions of the cross-correlation functions are observed, which degrade acquisition and tracking performance and may ultimately lead to PVT degradation or loss. These impairments are primarily induced by the conducted Radio Frequency (RF) chain, including front-end nonlinearities and gain increases introduced by the Low Noise Amplifier (LNA) and Automatic Gain Control (AGC), initially designed to improve GNSS signal detectability.

Under such conditions, while adaptive beamforming algorithms may still compute STAP filter weights, the resulting interference cancellation may be insufficient to guarantee robust PVT tracking across all scenario configurations. More generally, classical adaptive beamforming approaches are sensitive to steering vector mismatch and covariance estimation errors induced by RF nonlinearities, high jammer-to-signal ratios (JSR), AGC saturation, and quantization effects. Compensating for these effects often requires increasing the number or complexity of constraints or regularization mechanisms, which in turn raises computational complexity, particularly in high-dynamics scenarios requiring frequent weight updates and highlights the need for accurate distortion modeling to properly dimension ABF constraints.

This work highlights the need to revisit traditional ABF algorithms used to drive STAP filter weights to explicitly account for nonlinear and distortion effects, particularly in high-dynamics scenarios where robust PVT tracking is required. It also opens the way toward hybrid approaches combining classical digital signal processing with lightweight machine learning–based techniques, in which computationally frugal data-driven models assist in robust covariance estimation, distortion-aware constraint adaptation, and tracking of dynamic interference conditions, thereby improving robustness and maintaining reliable GNSS-based autonomy in RF contested environments.

Global Navigation Satellite Systems (GNSS) are indispensable for high-precision applications. One key element of high-precision positioning is to include carrier phase measurements into the position estimation. The carrier phase measurements allow for delivering much more accurate range measurements since the wavelength of a GNSS carrier, e.g. 19 centimeters for GPS L1, is much smaller than the equivalent length of a PRN code chip, e.g. 293 meters with C/A code of GPS L1.

GNSS signals suffer from intrinsic vulnerability to jamming due to their inherently low power at Earth’s surface (~−130 dBm). A sophisticated countermeasure employs antenna arrays with spatial-domain filtering to suppress jamming while preserving GNSS signal integrity. The mitigation is usually conducted by spatial filters like the Power Inversion filter or the Minimum Variance Distortionless Response (MVDR) filter. These filters can be classified to deterministic and blind approaches. A deterministic approach needs to know the array response vector of each incoming satellite signal. To estimate these vectors additional knowledge about the reception system is needed, including the Direction of Arrival (DoA) of the satellite’s signals and the antenna reception pattern. Furthermore, the reception system needs to be calibrated. To reduce complexity, in practice, it is often desired to use a blind approach. A blind approach works without the additional knowledge. Yet, integrating spatial filtering into high-precision systems presents challenges: common implementations of such filters induce directional dependent offsets into the carrier phase measurements.

In the literature, three different strategies to combine high-precision positioning with spatial filtering can be found: The first strategy aims at estimating the array response vector of each incoming satellite signal from the signals itself to allow the usage of deterministic filters. The algorithm is based on the idea to utilize information from the navigation message and the spatial signature of the signals to estimate the local DoAs of the satellite signals and calibrate the reception system. The second strategy used with blind filters aims at stabilizing the phase offset induced the interference mitigation on the time axis to keep the offset constant from one iteration of the filter to the next one. If the initial phase offset induced by the filter is small, this can prevent a phase offset over time. The third strategy utilizes the special symmetry properties of centro-symmetrical arrays geometries and constrains the blind spatial filter to avoid inducing phase offsets.

The described approaches are mainly verified on a theoretical basis using simulations or isolated jamming scenarios. The proposed paper aims at verifying and comparing three different approaches in different well-documented and reproducible real-world scenarios. The signals are recorded at the Jammertest 2025 in Andøya, Norway, including in-space satellite signals and jamming signals transmitted over the air. The approaches are implemented and tested using DLR’s real-time multi-antenna receiver GALANT as well as a software receiver using recorded signals from the event. This paper explores the trade-offs and design complexities of combining spatial filtering with high-precision GNSS requirements, discussing and verifying solutions to ensure robust interference mitigation without compromising performance.

The combination of an analog multi-channel front end, field programmable gate arrays (FPGAs), and multi-core processors integrated into a single chip provides a highly flexible and powerful platform for developing algorithms in various applications. Multi-antenna applications, satellite communications, satellite navigation, and telecommunications in particular can benefit from this technology. The complex programming of these platforms and, in particular, the partitioning of the various parts of an algorithm pose a problem.
This paper presents a possible design process for the Ettus X440 based on Ettus' RFNoC approach. A controlled radiation pattern antenna (CRPA) serves as an example. The example presented enables the suppression of 7 interfering signals that impair GNSS reception and are currently observed worldwide. In addition, it is possible to analyze the input signal to determine the interference strategy, the amplitude distribution function of the interference signal, the frequency spectrum, and, with appropriate calibration, the direction of the interference.
The X440 is a very powerful SDR (Software Defined Radio) based on Xilinx Zynq UltraScale+ device. Its advantage is the integration of an analog front end with eight phase-synchronous channels and the corresponding ADC. In addition, the device also offers 8 DACs. The X440 can be programmed and configured in various programming styles. In addition to the programming and configuration provided by Xilinx, Ettus enables flexible, automated implementation with the RFNoC. The strategy of partitioning the algorithm and mapping it to the various hardware platforms is described in detail in the paper.
The signal path comprises analog input signals, which are converted into 14-bit numbers using the ADCs. In the next step the data is converted into complex data using a 7-channel FIR Hilbert filter. Afterwards, the complex covariance matrix is calculated and the beamforming weights extracted and applied. As a final step the digital output signal is converted to an analog signal at a center frequency in the L-band. In general, the highspeed tasks (covariance calculation, filtering) are mapped to the FPGA-fabric, while matrix inversion and other control-oriented tasks and floating-point calculations (e.g. matrix-inversion) are realized on the ARM.
In contrast to prior implementations, no further components are required. Depending on the application, the presented realization can be used as a preprocessing unit for existing receivers. In this case, up to seven interference signals can be suppressed. Tested interference characteristics can be continuous wave, broadband, and sweep form. Including a commercial standard receiver data format (e.g., NMEA 0183) can be obtained.
This paper describes the design of the RFNoC architecture, the signal processing blocks and the connection of these blocks using the proposed RFNoC design flow. Special focus lies on the application-specific partitioning of the interferer suppression algorithms to achieve minimal hardware costs and optimal Interferer to Noise (INR). The costs of the individual blocks (memory and logical units) are presented. Another crucial point discussed in the paper concerns the additional costs incurred by the RFNoC-based approach The RFNoC implementation is compared with an optimized implementation in VHDL. This approach allows the costs of flexibility to be estimated.

In response to the growing number of global navigation satellite system (GNSS) radio-frequency interference (RFI) attacks, there is increasing interest in developing resilient navigation capabilities to support safety-of-life applications. These efforts include hardening GNSS receivers against RFI and integrating alternative or complementary positioning, navigation, and timing (PNT) systems.

In the aviation context, as highlighted by the impact of recent GNSS RFI events [OPSGROUP 2024], the availability of a GNSS-independent timing source is of particular interest. When available, an additional precise timing signal can be used by the onboard avionics to monitor GNSS time, detect jamming and spoofing attacks, and aid the GNSS solution when satellite visibility or availability is limited [Hwang 2005].

This paper proposes complementing GNSS with the L-band Digital Aeronautical Communication System (LDACS). LDACS provides a secure ground-to-air (G2A) data link that, in addition to communication services, can support precise timing and ranging measurements. Furthermore, when sufficient signals are available (or when a limited number of LDACS signals are combined with other sensors such as DME, INS, and altimeters) LDACS can also provide a fully independent complementary positioning solution.

This work focuses on the timing and ranging capabilities, as these can be provided by a single ground station and are therefore compatible with early LDACS deployment scenarios aimed at communication coverage.

Early work introduced the concept of clock aiding to improve GNSS performance, both for standalone receivers [Misra 1996] and for differential techniques [Hwang 2005]. Previous publications by the authors proposed a two-way timing and ranging (TWTR) concept over the LDACS G2A link [McGraw 2023, Zampieri 2024]. These works also identified key challenges arising from the limited LDACS bandwidth (500 kHz), the characteristics of the propagation environment, and the need for tight synchronization within the ground station network. In addition, a multi-process stochastic bounding model was recently introduced to characterize multipath errors [Zampieri 2025].

Building on these contributions, this paper investigates the performance of the proposed TWTR approach under realistic G2A multipath channel conditions. An atomic clock is assumed at the ground station, while the aircraft LDACS receiver employs a temperature-controlled crystal oscillator (TCXO). The paper presents an error covariance analysis of the timing filter as a function of measurement update rate and propagation parameters, and incorporates the proposed multipath bounding model. Finally, an analysis of timing and ranging monitors, together with a simple example, illustrates the potential benefits of the proposed method for loosely complementing the GNSS solution.

References:
[Misra 1996] The Role of the Clock in a GPS Receiver, GPS World.
[Hwang 2005] Improving DGPS Accuracy With Clock Aiding Over Communication Links, ION GNSS 2005.
[McGraw 2023] LDACS APNT Architecture Development & Evolution, IEEE/ION PLANS 2023.
[Zampieri 2024] LDACS PNT Architecture Integrating Asymmetric Two-Way Timing Filters for Enhanced and Reliable Positioning, ENC 2024.
[OPSGROUP 2024] GPS spoofing: Final report of the GPS spoofing workgroup.
[Zampieri 2025] Multi-Process Stochastic Multipath Model for Terrestrial Radionavigation Systems in Aviation, IEEE/ION PLANS 2025.

Four new Galileo satellites were placed in orbit in 2024 through launches number 12 (L12) and 13 (L13). In this way, the Galileo constellation reached its largest size ever, with 27 operational satellites in total. This study assesses the impact that these four new satellites have on the time to first fix (TTFF) robustness of Galileo. Specifically, a typical scenario for mass-market applications is considered. This involves cold-start conditions, single-frequency signals, and a low-cost receiver (u-blox ZED-F9P), which does not implement the Galileo I/NAV improvements of August 2023. For this purpose, two extensive test campaigns were conducted: one prior to the launches, from 4-7 November 2023, and another from 3-7 August 2025, after all four new satellites were declared operational. This second date was selected so that the geometries of the Galileo, GPS, and GLONASS constellations were as similar as possible to those of the first campaign, based on their orbital repetition periods. The tests of both campaigns were performed under the same conditions, except for the four new Galileo satellites, so it is reasonable to assume that any differences between the results can be attributed to them.

The campaigns were performed using a static antenna, so, in order to cover as many different potential scenarios as possible and achieve representative results, two different measures were taken. Firstly, the recordings were collected for 10 minutes every hour over four complete days, so multiple different geometries of the constellations were evaluated. Secondly, 11 different levels of attenuation, from 0 dB to 20 dB, were applied to the original signals. In addition, to minimize non-deterministic biases from the receiver’s algorithms, the assessment of each pair of recording and attenuation was carried out 10 times to derive average results. This required a large effort in terms of both data storage (more than 6 TB) and time, so it was not feasible to use longer recordings or more receivers.

The parameters analysed, due to their influence on the TTFF, are the number of tracked satellites, the signal-to-noise ratio (SNR) and the TTFF itself. In addition, to assess any effects on positioning performance, the horizontal error is also studied. The results indicate that the addition of the four new Galileo satellites leads to clear improvements in the number of tracked satellites, SNR, TTFF, and horizontal error, for all attenuation levels. In contrast, BeiDou, GLONASS, and GPS show no significant changes in these metrics over the same period. In absolute terms, after the deployment of the new satellites, the performance of Galileo in comparison with the other global navigation satellite systems is as follows: lower number of tracked satellites, as the constellation is not complete yet; similar levels of SNR, even higher than the GPS ones; larger TTFF values, but narrowing the gap with BeiDou; and the second smallest horizontal error, behind only GPS.