Evaluation of three antenna pattern measurements for a 25 MHz seasonde

Evans, Colin; Roarty, Hugh; Handel, Ethan; Glenn, Scott

doi:10.1109/cwtm.2015.7098147

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…The pattern of the receive antenna should be re-measured once a year or if data quality degrades (Kohut & Glenn, 2003;Laws et al, 2010). An antenna pattern is measured through a variety of methods: walking, boat, drone or AIS (Evans et al, 2015;Whelan et al, 2018). MARACOOS has implemented a real-time metric that checks for significant changes in measured pattern radial distributions over time based on a method developed by CODAR Ocean Sensors.…”

Section: Levelmentioning

confidence: 99%

Real-time quality assurance and quality control for a high frequency radar network

Roarty,

Updyke,

Nazzaro

et al. 2024

Front. Mar. Sci.

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This paper recommends end to end quality assurance methods and quality control tests for High Frequency Radar Networks. We focus on the network that is operated by the Mid Atlantic Regional Association Coastal Ocean Observing System (MARACOOS). The network currently consists of 38 radars making real-time measurements of the surface currents over the continental shelf for a variety of applications including search and rescue planning, oil spill trajectory modelling and providing a transport context for marine biodiversity observing networks. MARACOOS has been delivering surface current measurements to the United States Coast Guard (USCG) since May 2009. Data quality is important for all applications; however, since the USCG uses this surface current information to plan life-saving missions, delivery of the best quality data is crucial. We have mapped the components of the HF radar data processing chain onto the data levels presented in the NASA Earth Science Reference Handbook and have applied quality assurance and quality control techniques at each data level to achieve the highest quality data. There are approximately 400 High Frequency radars (HFRs) deployed globally and the presented techniques can provide a foundation for data quality checks and standardization of the data collected by the large number of systems operating today.

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Section: Levelmentioning

confidence: 99%

Real-time quality assurance and quality control for a high frequency radar network

Roarty,

Updyke,

Nazzaro

et al. 2024

Front. Mar. Sci.

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“…The surrounding objects may introduce an error of up to 10 • -15 • to the estimation of the direction of arrival (DOA) [23]. Therefore, for the compact HF radar systems deployed to detect ship targets, some cooperative or non-cooperative signal sources are usually used to calibrate the measured CLM antenna pattern [24,25]. As we know, the ship detection threshold depends on the target radar cross section (RCS).…”

Section: Introductionmentioning

confidence: 99%

Improving Ship Detection in Clutter-Edge and Multi-Target Scenarios for High-Frequency Radar

et al. 2021

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As one of the main sensors for continuous maritime measurements of sea state parameters, high-frequency surface wave radar (HFSWR) also plays an important role in ship detection and tracking. Compact HFSWR often suffers from missing targets, especially when the target appears near the Doppler region with heavy sea clutter or near another target in a multi-target scenario. To address this problem, an automatic ship detection method based on time–frequency (TF) analysis is presented in this paper. The TF target ridge areas are extracted in the TF image via the eigenvalues of the Hessian matrix, image edge detection, and local maximum search. Then, whether ship signals exist in the TF ridges or not is decided by a decision threshold that is calculated by fitting the probability distribution function (PDF) of sea clutter in the TF domain. The proposed TF method can separate TF ridges of similar Doppler frequency and performs constant false alarm rate (CFAR) detection for TF targets, which facilitates detecting these targets that are masked by sea clutter and other large targets. Experimental results show that the number of detected ships that match with the automatic identification system (AIS) records is four times more than that obtained by the conventional constant false alarm rate (CFAR) detectors and 1.3 times more than that by the state-of-the-art TF method in consideration of approximately the same number of detected targets.

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“…Although Washburn et al [21] have proposed a more simple pattern-measuring method based on an aerial drone, this method is also limited by the weather conditions and surrounding obstacles (severe weather, high buildings, and trees may cause hazards in performing this method). Fortunately, an automatic method for measuring the antenna pattern with ships' echoes has been developed [22][23][24]. This automatic method needs almost no human intervention, but the angular coverage of the antenna pattern measured by this method may be limited depending on the orientation of the shipping lanes relative to the radar site [23].…”

Section: Introductionmentioning

confidence: 99%

Quantifying and Reducing the DOA Estimation Error Resulting from Antenna Pattern Deviation for Direction-Finding HF Radar

et al. 2017

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High-frequency (HF) radars are routinely used for remotely sensing ocean surface currents. However, the performance of the most widely used direction-finding HF radar is degraded due to the effect of the inevitable deviations of actual antenna pattern on the direction of arrival (DOA) estimation. In this paper, we quantify the DOA estimation error resulting from the deviation of the actual antenna pattern from the ideal one. Theoretical analysis and field experiment results suggest that the ratio of the deviations for the two loops dominates the DOA estimation error. Thus, eliminating the effect of the antenna pattern deviations on DOA estimation error is transformed into eliminating the effect of this ratio. From this, a calibration method based on the time-averaged local spatial coverage rate (TLSCR) is proposed to reduce the effect of the antenna pattern deviations on current extraction, which uses the ideal antenna pattern to estimate the DOA of the echoes. To validate this proposed calibration method, we assess the radar-derived radial velocities by comparing with in situ observations. The comparison results indicate that the proposed TLSCR calibration method can effectively reduce the DOA estimation error and improve the performance of the direction-finding HF radar in current observation.

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Evaluation of three antenna pattern measurements for a 25 MHz seasonde

Cited by 6 publications

References 4 publications

Real-time quality assurance and quality control for a high frequency radar network

Real-time quality assurance and quality control for a high frequency radar network

Improving Ship Detection in Clutter-Edge and Multi-Target Scenarios for High-Frequency Radar

Quantifying and Reducing the DOA Estimation Error Resulting from Antenna Pattern Deviation for Direction-Finding HF Radar

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