United Kingdom

Adaptive notch filters (ANFs) have shown good interference mitigation capabilities against chirp interferences. However, the chirp dynamics and jump discontinuities in frequency limit ideal ANF tuning for all chirp types. An ANF removes a spectrally sparse interference through a notch filter, and an adaptive algorithm determines the center frequency of the chirp as it changes over time. One adaptive algorithm approach uses a conventional frequency-locked loop (FLL) with fixed settings to follow the chirp frequency. A critical tuning parameter of the FLL is the loop-bandwidth, which is a trade-off between the agility and noise suppression of the FLL. On the one hand, large bandwidth adapts quickly and is suitable for addressing the chirp’s jump-discontinuities but results in noisy estimates. On the other hand, a small bandwidth responds slow but results in more precise tracking.

This paper applies adaptive loop techniques commonly deployed in adaptive scalar tracking loops (ASTLs) to alter the loop-bandwidth of the FLL. As a result, the adaptive FLL-ANF (AFLL-ANF) responds much better against jump discontinuities by temporarily increasing the loop-bandwidth before settling back to a smaller loop-bandwidth once it is on the chirp signal. In recent publications, the loop-bandwidth control algorithm (LBCA) has shown promising results for adapting tracking global navigation satellite system (GNSS) receiver tracking loops, and in this paper, it is applied to a new application for FLL-based notch filters. Initial results indicate improved tracking errors compared to fixed-tuned FLL-ANFs and superior alpha-mean values.

Navigation 2021

Resilient Interference Mitigation with Adaptive FLL-based Adaptive Notch Filtering

### Welcome to Navigation 2021!

Our aim was to bring diverse disciplines together to stimulate insights and knowledge sharing towards a more navigable world. We hope we achieved this!

Navigation and timing are critical to the world we all inhabit. We believe that the work of the wider navigation community enables societal benefit including support to national grand challenges and also the United Nations’ Sustainable Development Goals. To achieve this requires collaboration and understanding across science, technology and practice.

Navigation 2021 is about much more than technology innovation, it also showcases science and research from across the wider community, including animal navigation, cognition and human factors.


Our aim was to bring diverse disciplines together to stimulate insights and knowledge sharing towards a more navigable world. We hope we achieved this!

technical paper

what3words: a simpler way to talk about location

Session 1.4.

Maritime incidents rates, the relative likelihood that a vessel has an accident per unit, are a useful metric for navigation authorities in understanding under which conditions and which areas vessels are more at risk of an accident. Understanding where and why accident rates vary supports targeted and cost-effective application of risk controls.

Vessel traffic data from the Automatic Identification System (AIS), principally developed for collision avoidance, has increased in availability and provides significant wider opportunities for navigation safety applications. Unfortunately, calculating spatial accident rates are subject to key methodological challenges associated with the Modifiable Areal Unit Problem that can influence the statistical validity of derived results. This phenomenon that has not been well explored in the maritime domain and requires further investigation.

This paper compares and contrasts accident rates across the United States calculated using 2.5 billion ship positions from AIS for 2018 and 130,000 accidents from the US Coastguard. Given the size and scale of the analysis, a workflow utilising big data processing technologies is developed for national risk mapping. The impacts on accident rates are evaluated of utilising three spatial data structures at different resolutions: namely administrative regions, cartesian regular grids and the Discrete Global Grid System (DGGS).

The results show significant variation in results can be achieved, exhibiting many of the hallmarks of the Modifiable Areal Unit Problem. In particular, such a method has limited utility at performing high resolution analysis, due to the sparsity of accident data, and therefore alternative methods are proposed. Of the three structures investigated, the DGGS exhibits some advantageous properties for regional scale analysis and is recommended for future navigation safety studies. This paper provides greater awareness of issues of spatial modelling for maritime risk analysis and highlight several actions which can be taken by researchers to mitigate them.

Geospatial Mining of Massive AIS Data for Strategic Management of Maritime Navigation Safety: Methodological and Policy Considerations

Session 1.5.

The paper presents a new positioning method based on a Generalised Spatial Correlation of GNSS signals for vehicles moving on geo-referenced tracks. Such location approach allows to detect the closest position ranges to predetermined locations by predicting the phase of satellites’ signals at and in proximity to the referenced locations, by testing the match between the predicted signals and those received by the non standard GNSS receiver.

The gain in sensitivity obtained by cumulating the multi-satellite spatial correlation, associated with the knowledge of the track path, makes it possible to reinforce the reliability of the detection of passage at the referenced spots (concept of virtual balise) and to improve the accuracy of dating and position, reducing the protection radius associated with the single instant of passage of the mobile compared to classical RAIM like monitoring methods.

In addition to the SNR gain brought by cumulating satellite intercorrelation functions, the method of spatial correlation also makes it possible to benefit from a better robustness with respect to specular multipaths. In presence of multipaths delayed with respect to the direct signal, and although the time correlation function is distorted and not symmetrical, the position of the maximum correlation remains unchanged.

The Generalized Spatial Correlation can be used in static and dynamic applications for estimating in real time the "best" position likelihood, based on the sole estimation of the correlation maximum according to 3D space. It is more robust than a classical PVT estimation, based on individual time based correlation using dedicated discriminators sensitive to the distortion of the correlation function.

The duration of exposure of the function being limited to the duration of passage of the mobile near the virtual balise, the risks of non-integrity of the GNSS signal due to local environment are also reduced.

Generalised Spatial Correlation for a véhicule moving on a constrained trajectory

Alternate means of navigation are needed for when GNSS signals are unavailable. Satellite signals of opportunity have the advantages of global, 24/7 cover and are independent of any local outages that may disrupt terrestrial transmissions. This paper examines the use of Iridium, Globalstar and Orbcomm satellite signals for navigation on a moving platform. These signals are intended for use with small user terminals and so can be exploited with a relatively simple antenna array and receiver. The use of direction of arrival as well as Doppler and time of arrival measurements is examined. Data gathering trials are conducted using a vehicle platform. Measurements from all three constellations are combined with IMU data in an extended Kalman filter and the performance assessed compared with GNSS ground truth. It is found that the navigation accuracy achievable is predominantly dependent on the accuracy of the knowledge of the satellite positions and velocities. Using the SGP4 TLE information available from space-track.org, median radial errors around 400m are achieved when driving through a variety of land environments.

Using Satellite Signals of Opportunity for Alternate Navigation

Timing has become a critical resource for various public and defense infrastructure. GNSS systems have been the defacto standard for providing timing service for these infrastructure. Various studies have concluded that GNSS system can be spoofed; hence a need to have multiple systems providing timing information to maintain the resilience. NextNav has developed a TerraPoiNT system, a terrestrial-based positioning, navigation and timing system which can complement existing systems as well as serve as a backup to the GNSS. TerraPoiNT system (a.k.a Metropolitan Beacon System), a type of Terrestrial-Beacon system, is a Time-Of-Arrival multilateration system offering 3D positioning and timing through a dedicated network of synchronized transmitters using Code Division Multiple Access spread spectrum signals. TerraPoiNT beacons can synchronize between themselves without the need for an absolute time source like GNSS, thus proving to be an ideal alternative to the GPS system. Beacons have a timing estimation sub-system which has access to one or more stable time sources like a precise atomic clock (such as Cesium/Rb) allowing long holdover time to be maintained. Each beacon can listen to multiple beacons adding resiliency into the network. This self-synchronization capability is scalable to a large network with only a few beacons requiring absolute time source or atomic clocks due to multi-hop beacon time transfer capability. TerraPoiNT System capabilities were assessed by the U.S. Department of Transportation at NASA-LaRC as part of the alternate navigation technology assessment for positioning, navigation and timing. TerraPoiNT timing accuracy in static indoor test over 72-hours was within 25 ns (95%) and within 30 ns (95%) in a basement location indicating an accurate and robust performance in a challenging locations as well as long-term stability. TerraPoiNT system demonstrated accurate, reliable and robust performance and it was rated #1 among all the technologies evaluated in position and timing trials

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Resilient Interference Mitigation with Adaptive FLL-based Adaptive Notch Filtering

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