Analysis of sea clutter radar data

A novel method to predict the sea clutter time series and detect target embedded in sea clutter is presented. The method is actually a recurrent neural network called an echo state network (ESN). A recursive least squares (RLS) algorithm is used for updating the output weights of ESN. A set of time series from IPIX radar data is tested. Numerical experiments reveal that the proposed network shows higher prediction precision in pure sea clutter data. Moreover, the mean squared error (MSE) between real-life data and prediction value by ESN can be used to detect target effectively.

Download Full-text

Detection of small target in sea clutter via multiscale directional Lyapunov exponents

Sensor Review ◽

10.1108/sr-03-2019-0061 ◽

2019 ◽

Vol 39 (6) ◽

pp. 752-762

Author(s):

Rui Wang ◽

Xiangyang Li ◽

Hongguang Ma ◽

Hui Zhang

Keyword(s):

State Space ◽

Lyapunov Exponents ◽

Design Methodology ◽

Radar Data ◽

Nonstationary Time Series ◽

New Method ◽

Small Target ◽

Sea Clutter ◽

Eigenvalues And Eigenvectors ◽

Content Type

Purpose This study aims to provide a new method of multiscale directional Lyapunov exponents (MSDLE) calculated based on the state space reconstruction for the nonstationary time series, which can be applied to detect the small target covered by sea clutter. Design/methodology/approach Reconstructed state space is divided into non-overlapping submatrices whose columns are equal to a predetermined scale. The authors compute eigenvalues and eigenvectors of the covariance matrix of each submatrix and extract the principal components σip and their corresponding eigenvectors. Then, the angles ψip of eigenvectors between two successive submatrices were calculated. The curves of (σip, ψip) reflect the nonlinear dynamics both in kinetic and directional and form a spectrum with multiscale. The fluctuations of (σip, ψip), which are sensitive to the differences of backscatter between sea wave and target, are taken out as the features for the target detection. Findings The proposed method can reflect the local dynamics of sea clutter and the small target within sea clutter is easily detected. The test on the ice multiparameter imaging X-ban radar data and the comparison to K distribution based method illustrate the effectiveness of the proposed method. Originality/value The detection of a small target in sea clutter is a compelling issue, as the conventional statistical models cannot well describe the sea clutter on a larger timescale, and the methods based on statistics usually require the stationary sea clutter. It has been proven that sea clutter is nonlinear, nonstationary or cyclostationary and chaotic. The new method of MSDLE proposed in the paper can effectively and efficiently detect the small target covered by sea clutter, which can be also introduced and applied to military, aerospace and maritime fields.

Download Full-text

Entropy Metrics of Radar Signatures of Sea Surface Scattering for Distinguishing Targets

Remote Sensing ◽

10.3390/rs13193950 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3950

Author(s):

Rui Jiang ◽

Li-Na Li ◽

Qiang Sun ◽

Si-Zhang Hong ◽

Jian-Jie Gao ◽

...

Keyword(s):

Large Scale ◽

Radar Data ◽

Sample Entropy ◽

Data Sets ◽

Sea Clutter ◽

Primary Target ◽

Data Set ◽

Wind Speeds ◽

Scale Factors ◽

Complexity Index

This paper analyzes sea clutter by a random series without assuming the scattering being independent. We quantitated the complexity of sea clutter by applying multiscale sample entropy. We found that above certain wave heights or wind speeds, and for HH or VV polarization, the target can be distinguished from sea clutter by regarding (i) the sample entropy at large scale factors or (ii) the complexity index (CI) as entropy metrics. This is because the backscattering amplitudes of range bins with the primary target were found equipped with the lowest sample entropy at large scale factors or the lowest CI compared to that of range bins with sea clutter only. To further cover low-to-moderate sea states, we constructed a polarized complexity index (PCI) based on the polarization signatures of the multiscale sample entropy of sea clutter. We demonstrated that the PCI is yet another alternative entropy metric and can achieve a superb performance on distinguishing targets within 1993’s IPIX radar data sets. In each data set, the range bins with the primary target turned to have the lowest PCI compared to that of range bins with sea clutter alone. Moreover, in our experiment using 1993’s IPIX radar data sets, the PCIs of range bins with sea clutter only were almost the same and stable in each data set, further suggesting that the proposed PCI metric can be applied in the presence of no or multiple targets through proper fitting curves.

Download Full-text

Spectral Kurtosis–Based Method for Weak Target Detection in Sea Clutter by Microwave Coherent Radar

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-13-00108.1 ◽

2015 ◽

Vol 32 (2) ◽

pp. 310-317 ◽

Cited By ~ 2

Author(s):

Yan Jin ◽

Zezong Chen ◽

Lingang Fan ◽

Chen Zhao

Keyword(s):

Target Detection ◽

Doppler Frequency ◽

Radar Data ◽

Short Time Fourier Transform ◽

Sea Clutter ◽

Spectral Kurtosis ◽

Small Targets ◽

Coherent Radar ◽

Short Time ◽

Weak Target

AbstractA new method is proposed to detect small targets embedded in sea clutter for land-based microwave coherent radar using spectral kurtosis as a signature from radar data. It is executed according to the following procedures. First, the echoes of radar from each range gate are processed by the technique of short-time Fourier transform. Then, the kurtosis of each Doppler channel is estimated from the time–Doppler spectra. Last, the spectral kurtosis is compared to a threshold to determine whether a target exists. The proposed method is applied to measured datasets of different sea conditions from slight to moderate. The signal from a small boat is detected successfully. Furthermore, the detection performance of the proposed method is analyzed by the way of Monte Carlo simulation. It demonstrates that the spectral kurtosis–based detector works well for weak target detection when the target’s Doppler frequency is beyond the strong clutter region.

Download Full-text

Quality Control of Sea Clutter and Constant Power Function Artifacts for Operational U.S. Navy Shipboard Radar Data Assimilation

10.5194/egusphere-egu2020-11989 ◽

2020 ◽

Author(s):

Paul Harasti

Keyword(s):

Quality Control ◽

Data Assimilation ◽

Research Laboratory ◽

Radar Data ◽

Naval Research ◽

Data Quality Control ◽

Sea Clutter ◽

Doppler Data ◽

Reflectivity Data ◽

The U.S

The Marine Meteorology Division of the U.S. Naval Research Laboratory (NRL) has developed and transitioned a 3DVAR reflectivity data assimilation (DA) system into operations at Fleet Numerical Meteorology and Oceanography Center (FNMOC), located in Monterey, California.&#160; The system assimilates hourly, volumetric, radar reflectivity data into the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS&#174;)1 high-resolution NWP model within the ship-following COAMPS &#8211; On demand System (COAMPS-OS&#174;)1.&#160; Both Next-Generation Radar (NEXRAD) land-based radar data and U.S. Navy shipboard SPS-48/Hazardous Weather Detection and Display Capability (HWDDC) radar data are assimilated depending on their data coverage provided to the COAMPS&#174; nested grids. The SPS-48/HWDDC units are installed on eighteen U.S. Navy aircraft carriers and amphibious assault ships, and when underway on a mission, the available units automatically transmit compressed, radar data files to FNMOC near the top of the hour.&#160; Through previously reported NRL and FNMOC demonstrations, and more recent operationally testing at FNMOC, the COAMPS-OS&#174; radar DA system&#8217;s nowcasting products have demonstrated their ability to provide improved predictions of precipitation events out to at least 6 hour forecasts compared to 3DVAR conventional DA into COAMPS&#174; alone.&#160; Shipboard SPS-48/HWDDC radar data and their assimilation into COAMPS-OS&#174; at FNMOC provide critical environmental awareness in the data sparse oceanic regions of the world that the Navy warfighter encounters.The SPS-48 radar is a S-band, phased-array, azimuthally scanning, air-search radar that scans electronically in elevation and completes a volume scan in four seconds. The HWDDC combines the volume scans into motion-compensated, one-minute composites with limited clutter filtering applied. The SPS-48 beams are combined to yield full PPI scans at 22 different elevation angles ranging from 0.1&#176; to 24&#176;. The azimuthal resolution of the data is 1&#176; and the range resolution is 1 km. The maximum range for reflectivity (radial velocity) data is 250 (81) km. &#160;The Doppler data are only produced for the lowest three elevation scans whereas reflectivity data are produced for all elevation scans; all these data are archived in Universal Format and compressed before dissemination to FNMOC.&#160; &#160;Owing to the limited HWDDC Doppler data both in range and elevation, and the single-polarization of the SPS-48 radar waveform, reflectivity data quality control is particularly challenging.&#160; New algorithms have been developed to handle sea clutter and constant power function artifacts, such as bullseyes and sun strobes.&#160; There are two algorithms for sea clutter; the first one deals with anomolus propagation sea clutter caused by sea-water evaporation into the atmospheric surface layer, and the second one deals with the more widespread and distant sea clutter due to surface-based and elevated electromagnetic ducts resulting from trapped moist air under temperature inversions often encountered off the coasts of California and the Arabian Gulf region.&#160; An overview of the ship-following COAMPS-OS&#174; radar data quality control and assimilation system will be presented along with examples of quality controlled SPS-48/HWDDC radar data and the impact on COAMPS&#174; forecast skill scores.&#160;1 COAMPS and COAMPS-OS are registered trademarks of the U.S. Naval Research Laboratory

Download Full-text

Accuracy analysis of individual waves retrieval from X-band nautical radar data by means of stochastic modeling of sea clutter images

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa ◽

10.21046/2070-7401-2016-13-5-68-78 ◽

2016 ◽

Vol 13 (5) ◽

pp. 68-78 ◽

Cited By ~ 1

Author(s):

P.V. Chernyshov ◽

◽

D.V. Ivonin ◽

S.A. Myslenkov ◽

Z.A. Khalikov ◽

...

Keyword(s):

Stochastic Modeling ◽

Radar Data ◽

Accuracy Analysis ◽

Sea Clutter ◽

X Band

Download Full-text

Modeling the Amplitude Distribution of Radar Sea Clutter

Remote Sensing ◽

10.3390/rs11030319 ◽

2019 ◽

Vol 11 (3) ◽

pp. 319 ◽

Cited By ~ 9

Author(s):

Sébastien Angelliaume ◽

Luke Rosenberg ◽

Matthew Ritchie

Keyword(s):

Goodness Of Fit ◽

Weather Condition ◽

Amplitude Distribution ◽

Radar Data ◽

Time Of Day ◽

Data Sets ◽

Sea Clutter ◽

Bhattacharyya Distance ◽

Ship Detection ◽

Wide Range

Ship detection in the maritime domain is best performed with radar due to its ability to surveil wide areas and operate in almost any weather condition or time of day. Many common detection schemes require an accurate model of the amplitude distribution of radar echoes backscattered by the ocean surface. This paper presents a review of select amplitude distributions from the literature and their ability to represent data from several different radar systems operating from 1 GHz to 10 GHz. These include the K distribution, arguably the most popular model from the literature as well as the Pareto, K+Rayleigh, and the trimodal discrete (3MD) distributions. The models are evaluated with radar data collected from a ground-based bistatic radar system and two experimental airborne radars. These data sets cover a wide range of frequencies (L-, S-, and X-band), and different collection geometries and sea conditions. To guide the selection of the most appropriate model, two goodness of fit metrics are used, the Bhattacharyya distance which measures the overall distribution error and the threshold error which quantifies mismatch in the distribution tail. Together, they allow a quantitative evaluation of each distribution to accurately model radar sea clutter for the purpose of radar ship detection.

Download Full-text