Target Detection In Sea Clutter Research Articles

Small target detection in sea clutter using dominant clutter tree based on anomaly detection framework

It is difficult for maritime high-resolution radars to realize the small target detection in sea clutter, due to weak target returns and complicated clutter characteristics. Cooperation of multiple features is a recognized way to distinguish target returns from clutter. Therefore, it becomes crucial to build a detector, a special classifier, with unbalanced training samples, i.e., ergodic clutter versus non-ergodic target samples. In this paper, a preferential decision tree (pre-decision tree) with the oblique stopping criterion is proposed, where a preferential Gini index (pre-Gini index) is defined to replace the Gini index and considers rigorous false alarm rates and tolerable missed probabilities for radar target detection. Then, an improved pruning is added to the pre-decision tree to generate a dominant clutter tree, which can accurately control the false alarm rate. The two-step decision is based on the anomaly detection framework and solves the unbalance of the training samples. The proposed method can work in the high-dimensional space directly, and its decision only involves linear operations. The experimental results on the recognized IPIX and CSIR databases illustrate that the proposed method performs well among the available feature-based detectors.

Floating Target Detection in Sea Clutter via de-shape Multiple synchrosqueezing Transform

Floating Target Detection in Sea Clutter via de-shape Multiple synchrosqueezing Transform

A PointNet-Based CFAR Detection Method for Radar Target Detection in Sea Clutter

A PointNet-Based CFAR Detection Method for Radar Target Detection in Sea Clutter

Covariance Matrix Estimation of Texture Correlated Compound-Gaussian Vectors for Adaptive Radar Detection

Covariance matrix estimation of compound-Gaussian vectors with texture-correlation (spatial correlation for the adaptive radar detectors) is examined. The texture parameters are treated as hidden random parameters whose statistical description is given by a Markov chain. States of the chain represent the value of texture coefficient and the transition probabilities establish the correlation in the texture sequence. An Expectation-Maximization (EM) method based covariance matrix estimation solution is given for both noiseless and noisy snapshots. An extension to the practically important case of persymmetric covariance matrices is developed and possible extensions to other structured covariance matrices are described. The numerical results indicate that the benefit of utilizing spatial correlation in the covariance matrix estimation can be significant especially when the total number of snapshots in the secondary data is small. From applications viewpoint, the suggested model is well suited for the adaptive target detection in sea-clutter where some spatial correlation between range cells has been experimentally observed. The performance improvements of the suggested approach for small number of snapshots can be particularly important in this application area.

Enhanced CNN-Based Small Target Detection in Sea Clutter With Controllable False Alarm

As targets floating on the sea surface get more invisible, it is becoming vital and challenging to effectively detect small targets from strong sea clutter. Besides, fitting the distribution of sea clutter is a hard task because of the complex characteristics of clutter. Classic model-based detectors are prone to suffer from the mismatch problem and a high probability of false alarm (PFA). In this paper, a PFA controllable and data-driven detection method based on an attention-enhanced convolutional neural network (CNN) is proposed. Different from mainstream CNN-based detectors, the proposed method takes time-frequency maps obtained by the Wigner-Ville distribution (WVD) as inputs. Then time-frequency maps are converted into feature images as inputs to the designed CNN strengthened with representation powers and feature refinement abilities. The designed CNN is capable of automatically learning and classifying different features between targets and clutter. Meanwhile, a PFA control unit is employed to ensure an expected actual PFA. Results with the IPIX database show that the probability of detection of the proposed method is about 0.9066 with the PFA being 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> and the observation time being 1.024 s. Compared with five typical feature-based detectors, the proposed method achieves better detection performance. Besides, results with the Sea-detecting Radar Data-sharing Program database also verify the feasibility and superiority of the proposed detector. The source codes are available at https://github.com/quqizhe-whu/ADN.

Target Detection in Sea Clutter via Contrastive Learning

Target Detection in Sea Clutter via Contrastive Learning

Maritime Radar Target Detection in Sea Clutter Based on CNN With Dual-Perspective Attention

Radar-based maritime target detection plays an important role in ocean monitoring. Considering the practical application, pulse-compression radar is widely used in terms of civilian offshore surface target detection. The existence of sea clutter will greatly interfere the detection performance of pulse-compression radar. This leads to the low detection performance of traditional algorithms like constant false alarm rate (CFAR). Deep learning methods have made strides in many fields recently, such as natural language processing and speech recognition. Inspired by this idea, we propose a maritime radar target detection method in sea clutter based on convolution neural network (CNN) and dual-perspective attention (DPA). The proposed method first encodes the radar echo in high-dimensional space and then extracts the correlation features from the global and local perspectives through the attention mechanism. We deployed the X-band pulse-compression radar on the coast of Hainan, China, and collected a lot of measured data. Experimental results demonstrate that the detection performance of our method outperforms the traditional CFAR methods and the latest deep learning-based methods. In the measured dataset, our proposed method can reach a detection probability of 93.59% under a false alarm rate (FAR) of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1e-3$ </tex-math></inline-formula> , reaching the practical application level.

Floating Small Target Detection in Sea Clutter Based on Multifeature Angle Variance

Floating Small Target Detection in Sea Clutter Based on Multifeature Angle Variance

Floating Small Target Detection in Sea Clutter Using Mean Spectral Radius

Detection of floating small targets in sea clutter is a challenging problem for maritime surveillance radar. For sea-surface floating targets without regular movement, it is hard for traditional model-based methods to achieve satisfying performance. Considering that the radar echoes will exhibit stronger statistical correlations when the target is present, this work proposes a detector using mean spectral radius (MSR), which is a data-driven method based on the eigenvalue analysis of a random matrix. It is first illustrated that the inner radius of the first-order autoregressive model will decrease with the increase of data correlations when the outer radius is normalized. The spectral radius of the target echo distributes on a smaller value compared with sea clutter due to stronger statistical correlations. The MSR, which is defined to be a specific linear spectral statistics indicating data correlations, is demonstrated to be an effective test statistic to distinguish the target echo from sea clutter. It is shown that by the central limit theorem (CLT), the MSR follows a Gaussian distribution. Experiments on Intelligent PIXel Processing Radar (IPIX) datasets show that the proposed method can effectively improve the detection performance. In addition, the MSR can be applied to construct multiple-feature-based detector to further enhance robustness.

Priori Information-Based Feature Extraction Method for Small Target Detection in Sea Clutter

Under the framework of feature-based detection of small targets on sea surface, existing feature extraction methods only use the echo data of current frame while ignoring the influence of historical echo data. Nevertheless, due to the non-stationarity of sea clutter, it may lead to unstable extraction of detection features, and then affect detection performance. To solve this problem, this paper designs a feature extraction method based on <i>a priori</i> information for small target detection. It firstly obtains <i>a priori</i> information from historical echo data by kernel density estimation (KDE) method. Then, the corresponding feature estimation method is utilized to obtain improved feature according to the relationship between current frame data and <i>a priori</i> information. Finally, the feature information of current frame is integrated into <i>a priori</i> information to prepare next feature extraction. Measured data are utilized to verify the performance of proposed method and the results reveal that, this method can effectively improve detection performance especially when sea clutter and target echo have good separability. In addition, the complexity of algorithm is analyzed to prove that proposed method has certain application potential.

Adaptive Target Extraction Method in Sea Clutter Based on Fractional Fourier Filtering

Target detection in sea clutter is of great significance in military radar research. Because the characteristics of sea clutter are complex and easily affected by wind direction, the method of suppressing sea clutter by using sea clutter characteristics to reproduce is not advantageous. To address this problem, this paper focuses on direct extraction of target echo. According to the difference of fractional characteristics between target echo and sea clutter, fractional Fourier transform is introduced to extract target echo. In this paper, a fractional domain change coefficient clustering method is established to identify the range units of target, and a fractional domain filtering method based on the minimax is proposed to extract the target spectrum of target dominated range units, so as to separate the target echo from sea clutter. Simulated and measured experiments prove that this method can accurately identify targets and extract target information under various sea state and different number of targets.

Eigenvalues-Based Detector Design for Radar Small Floating Target Detection in Sea Clutter

In this letter, the correlation structure inherent in the received data is exploited to improve detection performance for sea-surface small floating targets in short observation time. Three detectors are devised resorting to the eigenvalues of covariance matrix that are versatile statistics reflecting the signal correlation. Specifically, the proposed detectors respectively exploit the maximum eigenvalue to arithmetic mean (MAM) of all eigenvalues, the maximum eigenvalue to geometric mean (MGM) of all eigenvalues, and the maximum eigenvalue to minimum eigenvalue (MME) to form test statistics. At the analysis stage, the three-parameter Burr function is exploited to approximate the statistical distributions of the test statistics under null hypothesis and alternative hypothesis. Besides, the analytic expressions of false alarm probability, detection probability, and thresholds of the proposed detectors are derived. Finally, simulation results on real sea clutter show that the proposed detectors provide effective solutions to the problem of target detection in sea clutter.

An Improved MSR-Based Data-Driven Detection Method Using Smoothing Pre-Processing

The mean spectral radius (MSR) which indicates data correlations is used as a test statistic in data-driven detection approaches based on random matrix theory (RMT). To further improve the detection performance of MSR-based detectors, a smoothing pre-processing method is proposed in this letter. By performing a smoothing pre-processing step on the original sampled data, the divergence between the distributions of the MSR under different hypotheses will be increased, effectively improving the detection probabilities. The AR(1) model is used to illustrate the effect of the smoothing pre-processing coefficients on detection performance. The optimum smoothing coefficients under different AR(1) coefficients and the change of detection probability under certain choices of smoothing coefficients are analyzed. It is verified that the proposed smoothing pre-processing method can effectively improve detection performance by the simulation of low-frequency oscillation detection in colored noise under low signal-to-noise ratio and experiments on floating small target detection in sea clutter using IPIX datasets.

Bipercentile parameter estimators of bias reduction for generalised Pareto clutter model

The generalised Pareto distribution is an efficient model to characterise the high-resolution sea clutter. Robust and precise estimation of the model's parameters is a precondition of effective target detection in sea clutter. It is known that the explicit bipercentile (BiP) estimators are efficient in computation and robust to outliers. In this study, it is proved that the explicit BiP estimators are also consistent with respect to sample size. In practical applications, only limited samples are available and the bias of the BiP estimators degrades the estimation precision. The properties of the biases are analysed and BiP estimators of bias reduction are constructed by the look-up table method. The BiP estimators of bias reduction are verified by simulated data and measured sea clutter data.

Small target detection in sea clutter using all-dimensional Hurst exponents of complex time sequence

In this paper, nonstationary sea clutter time sequence, and corresponding amplitude sequence and phase sequence are modeled as statistically similar fractional-order Brownian motion (fBm) processes at different time scales. Their Hurst exponents form an all-dimensional description of fractal characteristics of sea clutter time sequence. Radar returns with targets and sea clutter exhibit salient differences in the all-dimensional description. Averaging three Hurst exponents followed by an adaptive threshold decision gives a simple but effective detector of sea-surface small targets. Experimental results on the recognized IPIX radar database show that the proposed detector attains much better performance than the detectors using single amplitude features and is competitive in performance with the tri-feature-based detector that requires time-consuming learning and decision processes.

Target Detection in Sea Clutter Based on Multifractal Characteristics After Empirical Mode Decomposition

Characteristic analysis of sea clutter is important in utilizing radar observations and detecting sea-surface targets. Real data signals are analyzed to determine the multifractal characteristics of sea clutter signals. Sea clutter is a nonlinear, nonstationary radar echo signal. A novel method that detects targets in sea clutter is proposed by completely utilizing the strengths of empirical mode decomposition (EMD) and combining it with multifractal characteristics. The EMD method is applied to decompose sea clutter signals into several intrinsic mode functions (IMFs). Multifractal detrended fluctuation analysis is utilized to calculate the generalized Hurst exponent for the main functions of IMF after which real sea clutter data are used for training and testing. Results show that targets in sea clutter can be effectively observed and detected through the proposed method, the performance of which is better than that of the target detection method for the generalized Hurst exponent under typical time, fractional Fourier transform and wavelet transform domains.

Target detection in sea clutter via weighted averaging filter on the Riemannian manifold

This paper proposes a weighted averaging filter procedure combined with a Riemannian geometry method to carry out a target detection in sea clutter. In particular, the weighted averaging filter, conceived from a philosophy of the bilateral filtering in image denoising, is presented on a Riemannian manifold of Hermitian positive-definite matrix. This filter acts as a clutter suppression procedure in the detection framework of the algorithm proposed in this paper, and can improve the detection performance. The principle of detection is that if a location has enough dissimilarity from the Riemannian mean or median estimated by its neighboring locations, targets are supposed to appear at this location. Numerical experiments and real sea clutter data are given to demonstrate the effectiveness of the proposed target detection algorithm.

Target Detection in Sea-Clutter Using Stationary Wavelet Transforms

Small target detection in the maritime environment remains a challenging problem. Maritime radars traditionally use non-coherent processing methods due to the time-varying and range-varying nature of the Doppler spectra. However, the radar backscatter may contain sea-spikes, which can last for seconds and resemble targets. This paper presents an application of stationary wavelet transforms (SWT) to improve target detection in medium grazing angle X -band sea-clutter. Since different components (sub-bands) of the SWT decomposition reveal different spectral characteristics of the returned signal, a method of selecting the appropriate sub-band is presented using an entropy-based metric. A Monte Carlo simulation using a cell averaging constant false alarm rate detector is then used to demonstrate the improvement of the SWT scheme against unfiltered data.

Range Distributed Floating Target Detection in Sea Clutter via Feature-Based Detector

We investigate the detection of range distributed floating target in sea clutter that consists of stationary speckle modulated by nonstationary texture. With the elimination of the nonstationary part of sea clutter, here a feature-based detector using the consistency factor of speckle is proposed. Experimental results of IPIX radar data sets show that the proposed feature-based detector achieves the better detection performance in comparison with other methods. The nonstationary property of sea clutter has a negative effect on the probability of detection, thus warrant consideration in designing the detector.

A reassigned bilinear transformation and Gaussian kernel function-based approach for detecting weak targets in sea clutter

ABSTRACTThis article proposes an efficient approach for weak target detection in sea clutter by using reassigned bilinear transformation based on the Gaussian kernel function (KF). This approach, which makes no assumptions on the sea clutter, mainly incorporates three steps: time-frequency analysis, feature extraction, and pattern classification. Bilinear transformation is utilized to transform sea clutter data into a time-frequency image, and the suitable Gaussian KF is applied to restrain the cross-term interference. Subsequently, to obtain a better performance in focus-ability, readability, and time-frequency resolution, we reassign the time-frequency distribution. In addition, we employ its marginal distribution of frequency (MARF) as classification feature vectors to decrease the dimensions of time-frequency features. Finally, a multilayered feed-forward neural network (NN) is selected as a classifier in the pattern classification process. Detection performances using the IPIX radar experimental sea clutter data are compared among the proposed method, the method based on continuous wavelet transform, and the conventional constant false alarm rate (CFAR) method based on spectral properties. A comparison of the results shows that the proposed method is more effective and efficient than the other two methods for weak target detection in sea clutter.