Simulation of Signal Processing for Ship Detection on Two Overlapping HF Radars with FMCW Waveforms

Iswandi, Iswandi; Hidayat, Risanuri; Wibowo, Sigit Basuki

doi:10.1109/icitee49829.2020.9271698

Cited by 3 publications

(6 citation statements)

References 12 publications

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“…Te signal received by all elements of the receiving array is processed by a series of processes to detect the target position sequence (z n ). Te details of signal processing and detection methods are not discussed here but can be found in our previous publication [16].…”

Section: Methodsmentioning

confidence: 99%

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An Evaluation Method of Ship-Tracking Algorithms for High-Frequency Surface Wave Radar considering High Maneuvers Generated by the MMG Model

Iswandi

Hidayat

Wibowo

2023

Journal of Engineering

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Thelong-distance coverage of high-frequency surface wave radar (HFSWR) has promoted it as an enormous means for ship monitoring on the country’s maritime territory. Since it is a primaryradar, noncooperative targets can also be detected. However, this radar also has a shortcoming of low spatial and temporal resolutions due to the narrow available bandwidth in the HF band. This limitation can reduce the performance of ship detection and tracking, especially for highly maneuvering ships. This paper proposes a new method to assess the tracking algorithm for a high-maneuvering ship. The absence of a high-maneuvering plot in the AIS data and existing analytical models are replaced by the MMG model run on MANSIM software. The linear, turning, and zigzag motions are generated and used to evaluate the tracking algorithms. The Monte Carlo simulation was conducted regarding the degradation of spatial resolution in the higher radial range. The tracking performance was analyzed by calculating the RMSE of four parameters, i.e., absolute position, radial range, bearing angle, and speed. For a trial case, four tracking algorithms were evaluated, i.e., Kalman filter (KF), extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF). The evaluation results showed that the EKF tracker had a minor error for the linear track with RMSE of absolute position, radial range, bearing angle, and speed being 1.368 km, 0.526 km, 1.550°, and 0.005 m/s, respectively. Otherwise, the UKF performed slightly better than EKF for the high maneuver targets. The RMSE of absolute position, radial range, bearing angle, and speed were 1.649 km, 0.639 km, 1.919°, and 0.165 m/s, respectively. The results also ensure the applicability of the MMG model to evaluate the tracking algorithm’s performance in HFSWR.

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Section: Methodsmentioning

confidence: 99%

“…Te validity of MANSIM in the empirical measurement of full-scale or miniaturized ships has been reported [15]. Next, the maneuver model is combined with the HFSWR signal model in our previous work [16]. Te detection results of combined two radar stations are computed and used for input of tracking systems.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation Method of Ship-Tracking Algorithms for High-Frequency Surface Wave Radar considering High Maneuvers Generated by the MMG Model

Iswandi

Hidayat

Wibowo

2023

Journal of Engineering

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“…This is demonstrated in Figure 3, which presents histograms of the simulated clutter at different Doppler frequencies, assuming a range of 24 km, a uniform distribution of the wind direction θ w and wind speed U w in the intervals of [0, 2 π ] and [2, 20] m/s respectively. The operating frequency f c is fixed at 10 MHz.…”

Section: High‐frequency Surface Wave Radar Modelmentioning

confidence: 97%

“…The solution proposed in this work uses a simulation model for synthetically generating the data necessary for a supervised learning algorithm based on the works presented in refs. [16][17][18][19][20]. This approach ensures the data is correctly labelled and allows the detector to be trained against a variety of adverse conditions, such as detecting targets in the presence of peaks or interfering targets in the reference cells during the estimation process.…”

Section: Related Workmentioning

confidence: 99%

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A CFAR‐like detector based on neural network for simulated high‐frequency surface wave radar data

Costa

Medeiros

Saotome

et al. 2023

IET Radar Sonar & Navi

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This article presents a deep neural network‐based constant false alarm rate (NNB‐CFAR) detector for simulated high‐frequency surface wave radar (HFSWR) data. A deep neural network is trained to identify fluctuation parameters of each cell of a range‐Doppler power spectrum based on the patterns present in the neighbouring cells. The estimated parameters are then used for calculating a detection threshold with a user‐specified probability of false alarm. To train the network, a realistic model of HFSWR echoes is used for generating a large labelled range‐Doppler image dataset, including many possible clutter scenarios and interfering target echoes. Several CFAR windows are extracted from the training range‐Doppler dataset and used as training data. The neural network is trained to replicate the output of a maximum likelihood estimator based on the reference cells of the CFAR window. The NNB‐CFAR algorithm was then compared to traditional CFAR algorithms by identifying targets in the second set of simulated range‐Doppler images. The probability of detection was also experimentally measured in the context of HFSWR for all algorithms. Results show that the technique can significantly improve detection rates amid strong clutter.

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Bragg Peak Detection Using Compatible Mother Wavelet on Oceanographic HF Radar Signals

Iswandi,

Hidayat,

Wibowo

2023

2023 15th International Conference on Information Technology and Electrical Engineering (ICITEE)

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Simulation of Signal Processing for Ship Detection on Two Overlapping HF Radars with FMCW Waveforms

Cited by 3 publications

References 12 publications

An Evaluation Method of Ship-Tracking Algorithms for High-Frequency Surface Wave Radar considering High Maneuvers Generated by the MMG Model

An Evaluation Method of Ship-Tracking Algorithms for High-Frequency Surface Wave Radar considering High Maneuvers Generated by the MMG Model

A CFAR‐like detector based on neural network for simulated high‐frequency surface wave radar data

Bragg Peak Detection Using Compatible Mother Wavelet on Oceanographic HF Radar Signals

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