Target Detection In Sea Clutter Research Articles

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.

Floating small target detection in sea clutter via normalised Doppler power spectrum

This paper, proposes a detector based upon normalised Doppler power spectrum (NDPS) is proposed to find floating small targets in sea clutter. Doppler power spectra (DPS) of sea clutter are modelled by a positive stochastic process in Doppler bins. To measure the power fluctuation of sea clutter at each Doppler bin, the NDPS is constructed which equals the DPS of the received time series subtracting the mean function and divided by the standard deviation function of the stochastic process. In view of the Doppler spread when the target return power is integrated within a long observation time, a double detection scheme is developed. The NDPS at the cell under test (CUT) is first estimated from the DPS at the reference range cells and CUT. Then, the first-stage detection is conducted by the shape-parameter-dependent thresholds to yield the thresholded NDPS at individual Doppler bins. The second-stage detection is made by selective integration of the thresholded NDPS. The proposed detector is compared with the fractal-based and tri-feature-based detectors by using the real high-resolution sea clutter datasets. The experimental results show that the proposed detector attains better performance than the fractal-based detector and has comparable performance with the tri-feature-based detector but lower computational complexity.

Low-observable target detection in sea clutter based on the adaptive 3D-IFS algorithm

This paper mainly studies the application of the fractal self-affine theory for weak target detection in sea clutter. In this paper, the adaptive 3D-IFS (3D-IFS: three-dimensional iterated function system) algorithm is presented and a novel weak target detection model is proposed based on the algorithm. To accurately extract the weak target from the complicated background of sea clutter, we use the radar echo model of the LFM radar and the target detection model to calculate the prediction error of radar echoes. Furthermore, based on extensive analysis and simulation, we identify the scale factor, polarization model and target state as three key factors that affect the detection performance of our proposed model. Using the real data from IPIX radar and C-band radar for simulations, we can see that the model has a significant performance improvement for weak target detection compared to the traditional 3D-IFS algorithm. The detection probability of the model reaches 70% at the SCR near −10dB, and signal processing time of the model is approximately 0.3s at the SCR near −6dB, thus it meets the requirement for low-observable (SCR>−8dB) and radial velocity about 600m/s high speed weak target detection in sea clutter.

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

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.

Floating small target detection in sea clutter via normalised Hurst exponent

An improved fractal-based detector using the normalised Hurst exponent is proposed to detect floating small targets in sea clutter. Analysis of the IPIX radar datasets shows that the Hurst exponent of sea clutter varies with sea state and the viewing geometry of the radar. Referring to the exploitation of the reference range cells in adaptive detection, the mean and variance of the Hurst exponent of sea clutter are first computed from clutter-only time series at the reference range cells around the cell under test (CUT). Then the Hurst exponent at the CUT is normalised by the estimated mean and variance to attain the normalised Hurst exponent. The experimental results of the IPIX radar datasets show that the fractal-based detector using the normalised Hurst exponent achieves better detection performance.

A New Feature Based on the Fluctuation Analysis to Detect Low Observable Target within Sea Clutter

In this paper, a novel approach based on the fluctuation analysis for the target detection in sea clutter is proposed. The self-affinity and scaling behaviors of sea clutter is analyzed by using the mean fluctuation. The q-th order normalized slope of the mean fluctuation curve is used as the characteristic parameter to describe the fractal property. The tests on the real data show that the target could be clearly distinguished from the sea clutter background with the proposed approach.

A Novel Method of Small Target Detection in Sea Clutter

Detecting low observable targets within sea clutter at low grazing angle is one of the research hotspots in radar signal processing community. In this paper, we have proposed a novel method based on polarimetric decomposition theorem. The polar characteristics of sea clutter has been analyzed, with the parameters after the decomposition of target scattering matrix. The scattering entropy and the scattering angle are the key parameters to discriminate the target from the sea clutter. The technique is designed to suppress unwanted sea clutter at polarimetric domain. Datasets from real marine radar are used to illustrate the performance of the new approach.

Small Target Detection in Sea Clutter Based on Polarization Characteristics Decomposition

Small Target Detection in Sea Clutter Based on Polarization Characteristics Decomposition

Low-Observable Target Detection in Sea Clutter Based on Fractal-based Variable Step-Size LMS Algorithm

该文主要研究了基于Hurst指数与可变步长LMS算法相结合的分析方法在海杂波微弱目标检测中的应用。一直以来，分形理论与统计理论是分别应用到目标检测中的，该文将分形可变步长LMS算法引入到海杂波微弱目标检测中，并在此基础上提出一个海杂波中的微弱目标检测模型，初步实现了基于LMS算法的检测方法与基于单一分形特征的检测方法的结合。最后，采用X波段雷达实测海杂波数据进行验证，结果表明该检测模型具有良好的检测海杂波中微弱目标的能力。

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.

Fast versus slow scan radar operation for coherent small target detection in sea clutter

Small maritime surface targets can be difficult to distinguish from sea clutter in radar backscattered signals, but discrimination may be improved by using coherent detectors within the dwell time of a scanning radar. Non-coherent integration, coherent integration, the Kelly detector and the adaptive linear quadratic detector are considered. Target detectability may also be improved by combining the results of a single dwell across multiple scans. Overall target detection times of 2, 5 and 10 s are considered and the trade-off between within-scan dwell time and multiple scan processing gain is investigated. Analysis of high-range-resolution coherent X-band data of small boats reveals that faster scan rates with corresponding shorter dwell times provide improved target detection performance over slower scan rates and longer dwell times.