Targets In Sea Clutter Research Articles

Detection of a Low Observable Sea-Surface Target With Micromotion via the Radon-Linear Canonical Transform

In this letter, a novel long-time coherent integration method, known as the Radon-linear canonical transform (RLCT), is proposed for detection of a low observable moving target in sea clutter. The micro-Doppler (m-D) of a sea-surface target is studied and modeled as multiple linear-frequency-modulated signals, which result from the accelerated and 3-D rotated movements. The RLCT-based algorithm employs m-D as a useful signature for target detection and can simultaneously compensate the range and Doppler migrations during long observation time, which simplifies the operational procedure. By searching along the moving trajectory and using extra three degrees of freedom, the observation values of m-D signals can be well matched and accumulated as peaks in the RLCT domain. Then, the target can be declared by comparing the peak value with an adaptive threshold. The definition of the RLCT demonstrates that it is the generalization of the popular moving target detection, Radon-Fourier transform, fractional Fourier transform, and linear canonical transform methods. Finally, experiments using a real sea clutter data set show that the proposed method can achieve high integration gain and detection probability of a micromotion target in heavy sea clutter.

Tri-feature-based detection of floating small targets in sea clutter

It is always a challenging problem for marine surface surveillance radar to detect sea-surface floating small targets. Conventional detectors using incoherent integration and adaptive clutter suppression have low detection probabilities for such targets with weak returns and unobservable Doppler shifts. In this paper, three features of a received vector at a resolution cell-the relative amplitude, relative Doppler peak height, and relative entropy of the Doppler amplitude spectrum-are exploited to give returns with targets from sea clutter. Real datasets show that each feature alone has some discriminability, and the three features jointly exhibit strong discriminability. Due to diversity of targets in practice, it is impossible to get features of returns with all kinds of targets. We recast detection of sea-surface floating small targets as a one-class anomaly detection problem in the 3D feature space. A fast convexhull learning algorithm is proposed to learn the decision region of the clutter pattern from feature vectors of clutter-only observations. As a result, a tri-feature-based detector is developed. The experiment results for the IPIX datasets show that the proposed detector at an observation time of several seconds attains better detection performance than several existing detectors.

Detection of low observable moving target in sea clutter via fractal characteristics in fractional Fourier transform domain

Effective detection of low observable moving target at sea is important for remote sensing and radar signal processing. The non-Gaussian property of sea clutter and lack of accurate model make the detection difficult for statistics based detectors. Also the fractal techniques in time domain cannot achieve high detection probability in heavy sea clutter. To help solve the problems, fractal characteristics of IPIX datasets in fractional Fourier transform (FRFT) domain are analysed making use of the fluctuation of FRFT amplitudes and moving target detection algorithms are proposed based on the fractal characteristics in FRFT domain. Firstly, fractal model in FRFT domain is established with fractional Brownian motion model and two judgment and extraction methods are employed for calculating the fractal characteristics in FRFT domain. It is found that sea clutter of different polarisations exhibit fractal behaviours in FRFT domain, that is, self-similarity property, within its corresponding scale-invariant interval. Then, we find that four specific fractal statistics in the best FRFT domain can provide valuable information for developing simple and effective detectors. Finally, traditional amplitude detector and Hurst exponent detector in time domain are compared and the results prove the superior detection ability of low observable moving target without complex computations.

An Efficient Method for Detecting Slow-Moving Weak Targets in Sea Clutter Based on Time–Frequency Iteration Decomposition

The echo scattered from a slow-moving weak target on sea surface is nonstationary due to the influence of waves. Time-frequency distributions are good tools to analyze it. In this paper, we propose a method for detecting slow-moving weak targets in sea clutter, which is based on time-frequency iteration decomposition. This method consists of three stages. First, we present a fast signal synthesis method (FSSM) based on eigenvalue decomposition. The FSSM can synthesize a signal faster and more accurately from the Wigner distribution (WD). Then, we present a signal iteration decomposition method (IDM) from the masked WD and the FSSM. By the IDM, the small component of a signal can be obtained, even when it is very close to a large component in the time-frequency plane. Finally, the proposed method results from the IDM and two criteria. Here the two criteria are defined to select the target signal. The proposed method is evaluated by X-band sea echo with a weak simulated target or a real target. The results demonstrate that it not only detects the slow-moving weak target but also shows its instantaneous state.

Small & Low RCS Target Detection Algorithm Based on Inverse Radon Transform in Sea Clutter

Abstract Low RCS targets detection is a challenging problem for marine radar in sea clutter. In this paper, a new detection algorithm is developed based on Radon and inverse Radon transform which accounts for the linear feature of the targets in sea clutter. After Radon transform the SCR is increased and clutter can be suppressed more easily in Radon domain. Then the data in Radon domain can be inverse Radon transformed into time-range domain, and the target track can be detected. The simulation results show that the proposed algorithm makes the detection of very small targets possible.

Subband ANMF Detection of Moving Targets in Sea Clutter

For detecting moving targets in high-resolution sea clutter, an adaptive normalized matched filter (ANMF) detector is restricted in performance, because the nonstationarity of the clutter restricts spatial samples available for clutter covariance matrix estimation and integration duration. To overcome this limitation, this paper proposes a subband ANMF detector. It consists of a prepositive, discrete Fourier transform, modulated filter bank followed by a set of ANMF detectors in individual subbands. The prepositive filter bank is used to decompose high-rate received signals into low-rate subband signals. The subband decomposition realizes clutter suppression outside each subband and improves the short-time stationarity of sea clutter. Improved short-time stationarity and the low rate of the subband signals allow the subband ANMF detector to have longer integration duration than allowed by the traditional ANMF detector. The ANMF detector in each subband performs clutter suppression inside the subband and matched reception of target returns. The experimental results using real sea clutter data show that the subband ANMF detector attains much better detection performance than the traditional ANMF detector does.

Impact of Measurement Model Mismatch on Nonlinear Track-Before-Detect Performance for Maritime RADAR Surveillance

In this paper, the application of track-before-detect (TkBD) processing against airborne radar surveillance data is examined. The sensitivity of TkBD processing to the choice of clutter model was examined through processing of real and simulated data containing noncoherent radar echo amplitude returns of a small maritime target in sea clutter. The utilization of K- and KA-distributed clutter models in place of the simpler Rayleigh distribution was examined through analysis of simulated and real data. Significant shortcomings were identified. An alternate empirical approach, clutter event based characterization, is proposed for characterizing and simulating sea clutter based on the identification and grouping of so-called “clutter events”.

CCTV 카메라 시스템에 의한 ARPA 레이더 추적선박의 자동식별

This paper describes a automatic video surveillance system(AVSS) with long range and 360 coverage that is automatically rotated in an elevation over azimuth mode in response to the TTM(tracked target message) signal of vessels tracked by ARPA(automatic radar plotting aids) radar. This AVSS that is a video security and tracking system supported by ARPA radar, CCTV(closed-circuit television) camera system and other sensors to automatically identify and track, detect the potential dangerous situations such as collision accidents at sea and berthing/deberthing accidents in harbor, can be used in monitoring the illegal fishing vessels in inshore and offshore fishing ground, and in more improving the security and safety of domestic fishing vessels in EEZ(exclusive economic zone) area. The movement of the target vessel chosen by the ARPA radar operator in the AVSS can be automatically tracked by a CCTV camera system interfaced to the ECDIS(electronic chart display and information system) with the special functions such as graphic presentation of CCTV image, camera position, camera azimuth and angle of view on the ENC, automatic and manual controls of pan and tilt angles for CCTV system, and the capability that can replay and record continuously all information of a selected target. The test results showed that the AVSS developed experimentally in this study can be used as an extra navigation aid for the operator on the bridge under the confusing traffic situations, to improve the detection efficiency of small targets in sea clutter, to enhance greatly an operator s ability to identify visually vessels tracked by ARPA radar and to provide a recorded history for reference or evidentiary purposes in EEZ area.

Multifractal correlation characteristic for radar detecting low-observable target in sea clutter

This paper studies the multifractal correlation characteristic of sea clutter which can be applied to radar low-observable target detection in sea clutter. Multifractal correlation is a generalization of multifractal ‘single point’ statistic, and takes the spatial correlation of two points with different singularity intensity into account. The important content of this paper is the derivation of multifractal correlation from multifractal, and obtains the multifractal correlation spectrum. Then the multifractal correlation spectrum is used as a characteristic to analyze the membership degree to match board. In fact, radar target detection can be regarded as a binary-classification question, therefore the SVM (support vector machine) is applied to target detection. In the end, the real sea clutter under several conditions is used to verify the method proposed in this paper. The results show that the method has a strong performance of target detection and sea clutter suppression.

HRR Detector for Slow-Moving Targets in Sea Clutter

The radar detection of targets in the presence of sea clutter has historically relied upon the radial velocity of targets with respect to the radar platform either by exploiting the relative target Dopplers (for targets with sufficient radial velocity) or by discerning the paths targets traverse from scan to scan. For targets with little to no radial velocity component, though, it can become quite difficult to differentiate targets from the surrounding sea clutter. This paper addresses the detection of slow-moving targets in sea clutter using a high resolution radar (HRR) such that the target has perceptible extent in range. Under the assumption of completely random sea clutter spikes based on an epsiv-contaminated mixture model with the signal and clutter powers known, optimal detection performance results from using the likelihood ratio test (LRT). However, for realistic sea clutter, the clutter spikes tend to be a localized phenomenon. Based upon observations from real radar data measurements, a heuristic approach exploiting a salient aspect of the idealized LRT is developed which is shown to perform well when applied to real measured sea clutter.

Time-frequency method for detecting an accelerating target in sea clutter

The authors design a time-frequency (TF) method for use in high-frequency surface-wave radar (HFSWR) for detecting a small accelerating target in sea clutter. The clutter is modelled by pseudo targets moving with Bragg velocity towards and away from the radar. The design is based on the Wigner distribution (WD) defined by Chan (type-III WD, in our terminology) rather than the WD defined by Claasen and Mecklenbrauker (1980) (2times type-I WD, in our terminology). Like the type-I WD, the type-III WD also concentrates a chirp signal onto a straight line in the TF plane. The type-III WD has the following advantages: 1) Its range of unambiguously measurable frequencies (RUMF) is [-pi,pi] rad/s, whereas for the type-I WD the RUMF is [-pi/2,pi/2] rad/s. 2) It allows a target separated from the clutter by pi rad/s to be detected, whereas the type-I WD coalesces such a target with the clutter and thereby mask it. An ambiguity function (AF) was defined corresponding to the type-III WD and use it to derive a smoothed type-III WD that mitigates the clutter. The smoothed type-III WD method is applied to real radar data and shown to be superior to the conventional Fourier transform method. The advantages of the type-III WD over the type-I WD are also demonstrated. The design principles laid out in the paper can also be used to develop a TF method for use in air traffic control radar (ATCR) for detecting an accelerating target in land clutter

Detection of a manoeuvring air target in sea-clutter using joint time–frequency analysis techniques

Traditionally, radar signals have been analysed in either the time or the frequency domain. Joint time–frequency representations characterise signals over a time–frequency plane. They thus combine time-domain and frequency-domain analyses to yield a potentially more revealing picture of the temporal localisation of a signal's spectral components. Therefore, for air target returns with time-varying frequency content, joint time–frequency representations offer a powerful analysis tool. A concise review of time–frequency transforms is provided as background and is needed to appreciate how time–frequency processing methods can improve conventional time or frequency processing methods. The authors use time–frequency analysis techniques for the detection of a manoeuvring aircraft using high frequency (HF) radar in heavily cluttered regions. They compare the ability of different time–frequency transforms to resolve several experimental aircraft returns. The relative speeds of the different transforms are also quantitatively studied. The results clearly demonstrate that time–frequency analysis techniques can significantly improve the detection performance of the HF radar and add considerable physical insight over what can be achieved by conventional Fourier transform methods currently used by HF radars.

Wavelet detection scheme for small targets in sea clutter

A radar detection scheme based on wavelet determination of scatterer lifetime within the Doppler spectra of non-Gaussian sea clutter is reported. The method is shown to be complementary to simple intensity thresholding when applied to a low-observable time varying target.

Signal detection using the radial basis function coupled map lattice

Conventional detection methods used in current marine radar systems do not perform efficiently in detecting small targets embedded in a clutter environment. Based on a recent observation that sea clutter, radar echoes from a sea surface, is chaotic rather than random, we propose using a spatial temporal predictor to reconstruct the chaotic dynamic of sea clutter because electromagnetic wave scattering is a spatial temporal phenomenon which is physically modeled by partial differential equations. The spatial temporal predictor used here is called radial basis function coupled map lattice (RBF-CML) which uses a linear combiner to fuse either measurements in different spatial domains for an RBF prediction or predictions from several RBF nets operated on different spatial regions. Using real-life radar data, it is shown that the RBF-CML is an effective method to reconstruct the sea clutter dynamic. The RBF-CML predictor is then applied to detect small targets in sea clutter using the constant false alarm rate (CFAR) principle. The spatial temporal approach is shown, both theoretically and experimentally, to be superior to a conventional CFAR detector.

Detection of signals in chaos

In this paper, we present a new method for the detection of signals in noise, which is based on the premise that the noise is chaotic with at least one positive Lyapunov exponent. The method is naturally rooted in nonlinear dynamical systems and relies on neural networks for its implementation. We first present an introductory review of chaos. The subject matter selected for this part of the paper is written with emphasis on experimental studies of chaos using a time series. Specifically, we discuss the issues involved in the reconstruction of chaotic dynamics, attractor dimensions, and Lyapunov exponents. We describe procedures for the estimation of the correlation dimension and the largest Lyapunov exponent. The need for an adequate data length is stressed. In the second part of the paper we apply the chaos-based method to a difficult task: the radar detection of a small target in sea clutter. >

Neural network-based adaptive radar detection scheme for small ice targets in sea clutter

An adaptive radar detection algorithm for the detection of small pieces of ice floating in an open sea environment is presented. The detection is carried out in the time-frequency domain, which eliminates the limitations of standard Doppler processing. Instead of looking for the 'best' time-frequency representation, the detection scheme is based on the ambiguity function and the focus is more on extracting information needed for classification. The detection scheme uses a multilayer perceptron as a pattern classifier. Using actual radar data, the adaptive detection results are compared with a traditional time-frequency domain constant false-alarm rate (CFAR) processor.

Radar detection prediction in sea clutter using the compound K-distribution model

In the paper the radar detection of targets in sea clutter modelled by the compound K-distribution is examined from a statistical detection viewpoint. Optimum performance is determined for both fixed threshold and ideal constant false alarm rate (CFAR) receivers for targets modelled by Swerling cases 0, 1 and 2. In particular, the performance of dual threshold binary integrators is assessed. The compound K-distribution sea clutter model is not only a good fit to observed amplitude statistics over a wide range of conditions, but also allows the temporal and spatial correlation characteristics to be accurately modelled. The modelling of pulse-to-pulse correlation characteristics is very important for accurate performance prediction when pulse-to-pulse integration is used. The use of the compound K-distribution model not only gives more accurate results than models based on amplitude distribution alone, but also gives a new insight to the detection characteristics of different radar detection radar detection systems over a wide range of sea conditions.

Observations on the further optimization of radar signal processing for the display and detection of targets in sea clutter

Abstract The opportunity to report the results of trials of new radar processors in really heavy seas is strictly limited. This paper gives a series of display photographs using a log-log receiver in a pair of pieces of otherwise conventional coastal surveillance equipment during severe gales in January 1983.

Further observations on the detection of small targets in sea clutter

It is shown, contrary to usual belief, that a 2° beamwidth antenna will (for usual display parameters) give better detection of small targets in sea clutter than a 1° beamwidth antenna for short pulse (0.05 to 0.1 μs) radar. The inadequacy of the logarithmic receiver as a constant false alarm rate processor for high definition sea-clutter is discussed, and the improvement from using a log-log characteristic demonstrated. Further advantage may accrue from following this system by a median detector. The advantages of high speed antenna scanning (400 rev/min or greater) to decorrelate clutter and establish pattern recognition on fast moving targets are considered, and proposals for a system embodying fast antenna scanning, median detector processing, and a memory display are advanced.

Detection of surface targets in sea clutter by shipborne radar

Doppler techniques do not aid detection by shipborne radar of small (drifting) surface targets in sea clutter, and such targets have no velocity spectrum distinguishable from that of the clutter. However, clutter decorrelation by rapid antenna scan, followed by camera or direct-view storage-tube integration, can give impressive gains in target/clutter visibility. With relative-motion displays, own ship's motion severely limits possible integration time by shifting the effective spot area of integration as the target is approached; this is neatly avoided by using a ‘true-motion’ display. Further gains may be possible (at least for hovercraft radar) by using an antenna with a vertical difference pattern in which the null is laid on the sea surface as a fence.