Amplitude characteristics of littoral sea clutter data at K-band and W-band

Funding: UK Engineering and Physical Sciences Research Council under grant EP/S032851/1.Sea clutter data at millimeter wave frequencies are quite limited in the literature. Recent advancements in millimeter wave radar technology have created a potential for its use in maritime surveillance and autonomy. Hence, collecting data at this frequency range is of great interest to both academia and industry. This study reports on a field trial conducted at St Andrews in winter 2020 to collect littoral sea clutter data using K-band (24 GHz) and W-band (94 GHz) radar systems. Extensive data collection was done during the trial, where this work specifically concentrates on analysis of the amplitude characteristics of the sea clutter returns. Analysis of the dataset shows that the radar backscatter was heavily dominated by sea-spikes. The modal normalized radar cross section (NRCS) values for Bragg, burst and whitecap scattering are measured to be -47, -30 and -17 dB respectively at 24 GHz in horizontal polarization and -48, -26 and -12 dB respectively at 94 GHz in circular polarization, measured at grazing angles of 1-3°. The backscatter from the smooth surface is found to be below the noise floor equivalent NRCS (-65 dB). Also, the power spectrum analysis of range-time intensity plots is discussed, revealing information on the sea surface dynamics.Postprin

Doppler characteristics of sea clutter at K-band and W-band : results from the St Andrews and Coniston water trials

Funding: UK Engineering and Physical Sciences Research Council under grant EP/S032851/1.This study reports on the experimental results from two field trials conducted by the University of St Andrews, focusing exclusively here on Doppler data. The first trial was at the Bruce Embankment in St Andrews, UK (winter 2020) and the second one was at Coniston Water in the Lake District, UK (autumn 2022). A 24 GHz K-band radar and a 94 GHz W-band radar were used in both trials to collect sea clutter data for phenomenology studies. As very few sea clutter data and analysis of these are available in the literature at these high frequencies, the results are expected to be of general interest within this field of study. The data collection at both trials was done for low grazing angles in the littoral zone. The datasets are quite varied in terms of wave direction, polarization and wind speed. The Doppler signatures and corresponding statistical parameters for these various conditions are reported here. The spectral analysis of different wave types (burst, whitecap, rough surface scattering) along with the combined spectra are also discussed. It is anticipated that these empirical results will be the precursor for improving upon the frequency ranges of existing sea clutter Doppler models

G-band FMCW Doppler radar for sea clutter and target characterisation

Funding: UK Engineering and Physical Sciences Research Council under grant EP/S032851/1.Marine autonomy is a field receiving a high degree of interest for its many potential applications in terms of commerce, crew safety, and the military. A successful autonomous vessel depends on a sophisticated degree of situational awareness facilitated by sensors. We are investigating sub-THz radar sensors for this purpose, with the primary goal being the characterization of sea clutter and targets in terms of both amplitude and Doppler statistics at frequencies spanning 24 to 350 GHz, where presently there is a lack of data. Sub-THz frequencies are of particular interest due to improved range and Doppler resolutions, and reduced sensor size, factors expected to be critical in enabling anomaly detection in the dynamic marine environment. As part of this work, a new 207 GHz frequency modulated continuous wave (FMCW) radar is being developed for the collection of clutter and target phenomenology data. The architecture uses a direct digital synthesis (DDS) generated chirp which is upconverted onto a low phase noise microwave LO then frequency multiplied by 24 to the carrier frequency. Twin Gaussian optics lens antennas (GOLAs) are used for transmit and receive with beamwidths of 2° , with adjustable linear polarization. The radar head is gimbal mounted for raster scanning RCS maps or for use in staring mode Doppler measurements. A chirp bandwidth of 4 GHz enables range bins of a few centimeters and high speed chirps enable a maximum unambiguous velocity of ±5 m/s.Publisher PD

Scale invariant coherent change detection to locate micro-motion in single pass SAR images

Micro-Doppler analysis of SAR signals has a broad range of applications including target vibrometry, structural health monitoring, and maritime surveillance. Location of sources of micro-Doppler signals in SAR images is however not straightforward, where typical artifacts such as ‘ghost’ targets may be unnoticeable if, for example, a vibration is of low amplitude and frequency. This issue is exacerbated by background noise and clutter. A method for locating micro-Doppler signals using scale invariant coherent change detection between subaperture images is developed and tested on SAR data of vibrating targets with a ground truth vibrational frequency of 2 Hz and amplitudes of 16.13 mm and 1.08 mm. The results indicate that this method is promising, where micro-motion from both targets was located and the relative insensitivity of the technique to bright stationary targets is also demonstrated

Bridge vibration measurements from very high-resolution spaceborne SAR

Structural health monitoring (SHM) using vibrometry is a key technique for the maintenance of bridges and other infrastructure. Conventional methods require in-situ placement of sensors which can be costly and inconvenient. This paper presents a remote monitoring method for extracting vibrational information from very high-resolution SAR data. This was achieved using a combination of a modified back-projection algorithm, spectrogram, and cadence-frequency analysis, which was applied to SAR data of a bridge. The extracted vibrations were validated against synchronous in-situ ground truth measurements

Amplitude characteristics of littoral sea clutter data at K-band and W-band

Sea clutter data at millimeter wave frequencies are quite limited in the literature. Recent advancements in millimeter wave radar technology have created a potential for its use in maritime surveillance and autonomy. Hence, collecting data at this frequency range is of great interest to both academia and industry. This study reports on a field trial conducted at St Andrews in winter 2020 to collect littoral sea clutter data using K-band (24 GHz) and W-band (94 GHz) radar systems. Extensive data collection was done during the trial, where this work specifically concentrates on analysis of the amplitude characteristics of the sea clutter returns. Analysis of the dataset shows that the radar backscatter was heavily dominated by sea-spikes. The modal normalized radar cross section (NRCS) values for Bragg, burst and whitecap scattering are measured to be -47, -30 and -17 dB respectively at 24 GHz in horizontal polarization and -48, -26 and -12 dB respectively at 94 GHz in circular polarization, measured at grazing angles of 1-3°. The backscatter from the smooth surface is found to be below the noise floor equivalent NRCS (-65 dB). Also, the power spectrum analysis of range-time intensity plots is discussed, revealing information on the sea surface dynamics

G-band FMCW Doppler radar for sea clutter and target characterisation

Marine autonomy is a field receiving a high degree of interest for its many potential applications in terms of commerce, crew safety, and the military. A successful autonomous vessel depends on a sophisticated degree of situational awareness facilitated by sensors. We are investigating sub-THz radar sensors for this purpose, with the primary goal being the characterization of sea clutter and targets in terms of both amplitude and Doppler statistics at frequencies spanning 24 to 350 GHz, where presently there is a lack of data. Sub-THz frequencies are of particular interest due to improved range and Doppler resolutions, and reduced sensor size, factors expected to be critical in enabling anomaly detection in the dynamic marine environment. As part of this work, a new 207 GHz frequency modulated continuous wave (FMCW) radar is being developed for the collection of clutter and target phenomenology data. The architecture uses a direct digital synthesis (DDS) generated chirp which is upconverted onto a low phase noise microwave LO then frequency multiplied by 24 to the carrier frequency. Twin Gaussian optics lens antennas (GOLAs) are used for transmit and receive with beamwidths of 2° , with adjustable linear polarization. The radar head is gimbal mounted for raster scanning RCS maps or for use in staring mode Doppler measurements. A chirp bandwidth of 4 GHz enables range bins of a few centimeters and high speed chirps enable a maximum unambiguous velocity of ±5 m/s

Machine learning-based approach for maritime target classification and anomaly detection using millimetre wave radar Doppler signatures

The authors present multiple machine learning-based methods for distinguishing maritime targets from sea clutter. The main goal for this classification framework is to aid future millimetre wave radar system design for marine autonomy. Availability of empirical data at this frequency range in the literature is scarce. The classification and anomaly detection techniques reported here use experimental data collected from three different field trials from three different millimetre wave radars. Two W-band radars operating at 77 and 94 GHz and a G-band radar operating at 207 GHz were used for the field trial data collection. The dataset encompasses eight classes including sea clutter returns. The other targets are boat, stand up paddleboard/kayak, swimmer, buoy, pallet, stationary solid object (i.e. rock) and sea lion. The Doppler signatures of the targets have been investigated to generate feature values. Five feature values have been extracted from Doppler spectra and four feature values from Doppler spectrograms. The features were trained on a supervised learning model for classification as well as an unsupervised model for anomaly detection. The supervised learning was performed for both multi-class and 2-class (sea clutter and target) classification. The classification based on spectrum features provided an 84.3% and 80.1% validation and test accuracy respectively for the multi-class classification. For the spectrogram feature-based learning, the validation and test accuracy for multi-class increased to 93.3% and 88.7% respectively. For the 2-class classification, the spectrum feature-based training accuracies are 88.1% and 86.8%, whereas with the spectrogram feature-based model, the values are 95% and 94.1% for validation and test accuracies respectively. A one class support vector machine was also applied to an unlabelled dataset for anomaly detection training, with 10% outlier data. The cross-validation accuracy has shown very good agreement with the expected anomaly detection rate

Machine learning-based approach for maritime target classification and anomaly detection using millimetre wave radar Doppler signatures

This work was supported by the UK Engineering and Physical Sciences Research Council under grant EP/S032851/1.The authors present multiple machine learning-based methods for distinguishing maritime targets from sea clutter. The main goal for this classification framework is to aid future millimetre wave radar system design for marine autonomy. Availability of empirical data at this frequency range in the literature is scarce. The classification and anomaly detection techniques reported here use experimental data collected from three different field trials from three different millimetre wave radars. Two W-band radars operating at 77 and 94 GHz and a G-band radar operating at 207 GHz were used for the field trial data collection. The dataset encompasses eight classes including sea clutter returns. The other targets are boat, stand up paddleboard/kayak, swimmer, buoy, pallet, stationary solid object (i.e. rock) and sea lion. The Doppler signatures of the targets have been investigated to generate feature values. Five feature values have been extracted from Doppler spectra and four feature values from Doppler spectrograms. The features were trained on a supervised learning model for classification as well as an unsupervised model for anomaly detection. The supervised learning was performed for both multi-class and 2-class (sea clutter and target) classification. The classification based on spectrum features provided an 84.3% and 80.1% validation and test accuracy respectively for the multi-class classification. For the spectrogram feature-based learning, the validation and test accuracy for multi-class increased to 93.3% and 88.7% respectively. For the 2-class classification, the spectrum feature-based training accuracies are 88.1% and 86.8%, whereas with the spectrogram feature-based model, the values are 95% and 94.1% for validation and test accuracies respectively. A one class support vector machine was also applied to an unlabelled dataset for anomaly detection training, with 10% outlier data. The cross-validation accuracy has shown very good agreement with the expected anomaly detection rate.Publisher PDFPeer reviewe

Radar signatures of sea lions at K-band and W-band

The millimetre wave radar signatures of sea lions collected from three animals in the outdoor seal pool available at the Sea Mammal Research Unit in St Andrews in the Autumn of 2021 is reported. The objective is to study the radar amplitude and Doppler signatures of the animals when their full body or part thereof is above water, which is important for the application of autonomous marine navigation. The data was collected using 24 GHz (K-band) and 77 GHz (W-band) Frequency Modulated Continuous Wave radars with linear polarisation. It has been demonstrated that the sea lions were very clearly detected by the radars with Signal to Noise Ratio greater than 30 dB at a range of 40 m. The calculated modal radar cross section (RCS) of the sea lions in HH polarisation at 24 and 77 GHz vary from −48 to −26 dBsm and −48 to −28 dBsm respectively, corresponding to the different body parts and the amount of exposure to the radar beam. In VV polarisation, the modal RCS value range is from −49 to −26 dBsm and −49 to −22 dBsm respectively. The corresponding maximum RCS and the Cumulative Distribution Function results are also reported