Harbor surveillance radar detection performance

System requirements for harbor surveillance radars are reviewed. Experimental sea clutter data are presented and shown to be closely matched by a log-normal clutter model. Characteristics and parameters of the log-normal clutter model are described. Detection performance against a steady target in a log-normal clutter background, using logarithmic receivers, is provided. A log-normal target model is described. Detection curves for log-normal fluctuating targets in log-normal clutter are developed. A Constant False Alarm Rate (CFAR) processor that adaptively functions in log-normal clutter is described. CFAR detection performance is derived and presented in the form of CFAR loss curves.     

Mitigation techniques for non-Gaussian sea clutter

This paper deals with the problem of coherent radar detection of targets embedded in clutter modeled as a compound-Gaussian process. We first provide a survey on clutter mitigation techniques with a particular emphasis on adaptive detection schemes ensuring the constant false-alarm rate (CFAR) property with respect to all of the clutter parameters. Thus, we propose a novel decision rule based on a recursive covariance estimator, which exploits the persymmetry property of the clutter covariance matrix. Remarkably, the devised receiver is fully CFAR in that its threshold can be set independently of the clutter distribution as well as of its covariance, even if the environment is highly heterogeneous; namely, the disturbance distributional parameters vary from cell to cell. At the analysis stage, we compare the performance of the novel detector with some classical radar receivers such as that of Kelly and the adaptive matched filter both in the presence of simulated as well as on real radar data, which statistical analysis has shown to be compatible with the compound-Gaussian model. The results show that the new receiving structure generally provides higher detection performance than the others and, for a fluctuating target, it uniformly outperforms the counterparts. We also provide a discussion on the CFAR behavior of the analyzed receivers as well as on their computational complexity.     

Fast Double Selectivity Index-CFAR Detection Method for the Multi-Beam Echo Sounder

Abstract

Seafloor terrain and water column target detection is of great significance in marine surveys. However, the complex clutter environment can dramatically affect the detection performance of a multi-beam echo sounder. To simultaneously and robustly detect the seafloor terrains and water column targets, this paper proposes a fast two-dimensional double selectivity index-constant false alarm rate (DSI-CFAR) detection method. A two-dimensional cross sliding window, which includes reference cells and guard cells, is used in this paper. The proposed method improves the detection performance by using the appropriate clutter power level estimation strategies in different directions. An exponential model is accurately built by fitting various distributions, so the DSI-CFAR detection performance is greatly ameliorated. To reduce the computational load of the method, this paper uses a fast algorithm based on the global threshold. The region of interest (ROI) is selected by the preset global threshold, and the DSI-CFAR detector is only performed in the region of interest, which significantly reduces the number of pixels that must be calculated. In this paper, the measured results of different sliding windows and CFAR detection methods validate the basic performance of the proposed method.     

一种新的用于极化SAR图像船只检测的散射相似性测度

本文提出了一种新的用于极化合成孔径雷达（SAR）海上船只目标检测的测度。首先利用散射相似性参数研究船只与海杂波散射机制的差异。基于这些差异,提出了一种新的船只检测测度,该测度能够有效区分船只目标和海杂波。然后提出了利用核密度估计（KDE）方法对该测度进行建模的方法。基于统计模型,实现了自适应恒虚警率（CFAR）的检测方案。本文利用多景C波段RADARSAT-2极化SAR（Pol-SAR）数据上进行实验,系统分析了所提出测度的海杂波拟合性能与船只检测性能,并与两个经典的极化测度进行了比较,实验和比较结果证明了所提的测度的可行性。     

A new CFAR ship target detection method in SAR imagery

Many ship target detection methods have been developed since it was verified that ship could be imaged with the space-based SAR systems. Most developed detection methods mostly emphasized ship detection rate but not computation time. By making use of the advantages of the K-distribution CFAR method and two-parameter CFAR method, a new CFAR ship target detection algorithm was proposed. In that new method, we use the K-distribution CFAR method to calculate a global threshold with a certain false-alarm rate. Then the threshold is applied to the whole SAR imagery to determine the possible ship target pixels, and a binary image is given as the preliminary result. Mathematical morphological filter are used to filter the binary image. After that step, we use the two-parameter CFAR method to detect the ship targets. In the step, the local sliding window only works in the possible ship target pixels of the SAR imagery. That step avoids the statistical calculation of the background pixels, so the method proposed can much improve the processing speed. In order to test the new method, two SAR imagery with different background were used, and the detection result shows that that method can work well in different background circumstances with high detection rate. Moreover, a synchronous ship detection experiment was carried out in Qingdao port in October 28, 2005 to verify the new method and one ENVISAT ASAR imagery was acquired to detect ship targets. It can be concluded from the experiment that the new method not only has high detection rate, but also is time-consuming, and is suitable for the operational ship detection system.     

Signal excess in K-distributed reverberation

Active sonar systems have recently been developed using larger arrays and broad-band sources to counter the detrimental effects of reverberation in shallow-water operational areas. Increasing array size and transmit waveform bandwidth improve the signal-to-noise ratio-and-reverberation power ratio (SNR) after matched filtering and beamforming by reducing the size of the range-bearing resolution cell and, thus, decreasing reverberation power levels. This can also have the adverse effect of increasing the tails of the probability density function (pdf) of the reverberation envelope, resulting in an increase in the probability of a false alarm. Using a recently developed model relating the number of scatterers in a resolution cell to a K-distributed reverberation envelope, the effect of increasing bandwidth (i.e., reducing the resolution cell size) on detection performance is examined for additive nonfluctuating and fluctuating target models. The probability of detection for the two target models is seen to be well approximated by that for a shifted gamma variate with matching moments. The approximations are then used to obtain the SNR required to meet a probability of detection and false-alarm performance specification (i.e., the detection threshold). The required SNR is then used to determine that, as long as the target and scatterers are not over-resolved, decreasing the size of the resolution cell always results in an improvement in performance. Thus, the increase in SNR obtained by increasing bandwidth outweighs the accompanying increase in false alarms resulting from heavier reverberation distribution tails for K-distributed reverberation. The amount of improvement is then quantified by the signal excess, which is seen to be as low as one decibel per doubling of bandwidth when the reverberation is severely non-Rayleigh, as opposed to the expected 3-dB gain when the reverberation is Rayleigh distributed.     

Sea-clutter modeling using a radial-basis-function neural network

Recently, neural networks have been proposed for radar clutter modeling because of the inherent nonlinearity of clutter signals. This paper performs an analysis of the practicality of using a radial basis function (RBF) neural network to model sea clutter and to detect small target embedded in sea clutter. An experiment using an instrumental quality radar was carried out on the eastcoast of Canada to create a rich sea clutter and small surface target database. This database contains both staring and scanning data under various environmental conditions. Using data-sets with different characteristics, we investigate the effects of quantization error, measurement noise, generalization of the neural net over ranges and sampling rate on the RBF clutter model. Despite these physical limitations, the RBF model was shown to approach an optimal predictive performance. The RBF predictor was also applied to detect various small targets in this database based on the constant false alarm rate (CFAR) principle. This RBF-CFAR detector was demonstrated to be able to detect small floating targets even in rough sea conditions     

基于AIS距离-多普勒投影的地波雷达CFAR检测验证方法

恒虚警(CFAR)检测是地波雷达船只目标探测的主要方法。目前基于船舶自动识别系统(AIS)信息的CFAR检测验证方法是一种间接验证方式,容易受地波雷达系统测向误差的影响,且不具备对错检与漏检目标的分析能力。本文提出了一种基于AIS信息的评价地波雷达CFAR检测结果的直接验证方法。该方法将有效的AIS信息转换到地波雷达的距离-多普勒谱中,通过在该谱中AIS信息与CFAR检测结果的关联分析,实现CFAR检测结果的直接评价。论文首先给出了方法的原理和处理流程,然后利用实测数据给出了该方法在CFAR检测结果评价中的实际应用,验证了方法有效性,而且该方法也为低可观测目标的CFAR检测提供了参数优化调整的依据。     

Simulation of non-Rayleigh reverberation and clutter

The simulation of active sonar reverberation time series has traditionally been done using either a computationally intensive point-scatterer model or a Rayleigh-distributed reverberation-envelope model with a time-varying power level. Although adequate in scenarios where reverberation arises from a multitude of scatterers, the Rayleigh model is not representative of the target-like non-Rayleigh reverberation or clutter commonly observed with modern high-resolution sonar systems operating in shallow-water environments. In this paper, techniques for simulating non-Rayleigh reverberation are developed within the context of the finite-number-of-scatterers representation of K-distributed reverberation, which allows control of the reverberation-envelope statistics as a function of system (beamwidth and bandwidth) and environmental (scatterer density and size) parameters. To avoid the high computational effort of the point-scatterer model, reverberation is simulated at the output of the matched filter and is generated using efficient approximate methods for forming K-distributed random variables. Finite impulse response filters are used to introduce the effects of multipath propagation and the shape of the reverberation power spectrum, the latter of which requires the development of a prewarping of the K distribution parameters to control the reverberation-envelope statistics. The simulation methods presented in this paper will be useful in the testing and evaluation of active sonar signal processing algorithms, as well as for simulation-based research on the effects of the sonar system and environment on the reverberation-envelope probability density function.     

基于模型相似度拟合的海杂波统计方法

本文提出一种基于模型相似度拟合的海杂波统计方法。首先根据合成孔径雷达(SAR)图像计算瑞利分布、对数正态分布、韦布尔分布、K分布、G0分布5种经典的海杂波分布的概率密度函数,然后根据模型间的相似度准则拟合得到新的海杂波分布模型。文章利用四景不同类型的真实SAR数据对算法的拟合性能进行了评价,结果显示利用该算法得到的拟合模型与真实SAR数据的平均Kullback-Leibler距离仅为0.015 84,远优于其他分布模型。基于该拟合模型的恒虚警率舰船检测算法对四景SAR数据的平均检测精度达到95.75%,在控制虚警和漏检方面均优于采用其他模型的同类方法。     

高频地波雷达海杂波的边界约束循环对消

在对海洋监视监测的过程中,高频地波雷达的船只目标检测能力与其对海杂波的抑制能力密切相关。但是,传统海杂波时域抑制方法存在目标回波参数与海杂波相近时难以区分其各自分量,对消时目标被误消除的问题。针对上述问题,本文提出了一种适用于高频地波雷达海杂波的边界约束循环对消方法。该方法综合海杂波频移理论和实际海杂波特性分析制定出边界条件,约束建模对消过程,实现海杂波分量的抑制。实测地波雷达数据和船舶自动识别系统(AIS)数据检验的结果表明,本文方法克服了传统方法的不足且信杂比改善更加稳定,能够更加有效地实现海杂波循环对消。     

基于小波阈值算法的海杂波信号降噪

为有效提取噪声背景下的海杂波信号,针对海杂波信号非线性非平稳的特点,提出基于小波阈值算法对实测海杂波数据去噪。在噪声水平未知条件下,提出基于噪声主要在高频段且能量较小、信号主要集中在低频段思想的噪声判断准则。为验证小波去噪效果,将该算法对含有噪声的海杂波实测数据进行去噪,采用均方差和降噪信号信噪比两项指标衡量去噪效果,并与均值和中值等去噪方法对比,小波算法在这两项指标均优于其他算法;此外,实验结果还表明,db2小波在双曲线阈值函数和HeurSure阈值模式下优于其他小波去噪效果。     

Signal detection for communications in the underwater acousticenvironment

Signal detection is a critical stage in the implementation of any effective communications system. The underwater acoustic environment, particularly in the presence of underwater vehicles, presents significant challenges to reliable detection without excessive false alarms. While there is often sufficient signal-to-noise ratio with respect to stationary broad-band background noise to permit reliable operation, the presence of strong event-like interference signals such as narrow-band signals and impulsive broad-band signals complicates the detection problem significantly. Frequency-hopped signals interleaved with quiescent bands are proposed as the basis of a robust detection system. These signals also make robust detection possible in a multi-access communications system. Two new detection algorithms that exploit the particular structure of these frequency-hopped signals are developed and their performance is analyzed. This analysis uses a modification of the doubly noncentral F-distribution that has not been used previously for such analysis. This distribution makes possible the direct calculation of probabilities of detection and false-alarm under interference and signal scenarios that cannot be analyzed using the better known noncentral F-distribution. With this analysis, the two developed detectors are shown to offer superior performance to that of either the CFAR detector or the binary data sequence detector. Experimental data confirms the theoretically derived results     

Study on dim target detection and discrimination from sea clutter

Dim target detection from sea clutter is one of the difficult topics in ocean remote sensing application. By aiming at the shortcoming of false alarms when using track before detect (TBD) based on dynamic programming, a new discrimination method called statistics of direction histogram (SDH) is proposed, which is based on different features of trajectories between the true target and false one. Moreover, a new series of discrimination schemes of SDH and Local Extreme Value method (LEV) are studied and applied to simulate the actually measured radar data. The results show that the given discrimination is effective to reduce false alarms during dim targets detection.     

快速衰落信道环境下SRC检测器的性能分析

分段拷贝相关器是高斯白噪声背景下,快速衰落水声信道中,对应最大似然比准则的最佳接收机,其输出服从χ2分布。推导出有信号时接收机输出端的概率密度函数和检测概率的解析表达式,计算机仿真结果验证了所获得结果的正确性。最后通过计算无信号时接收机输出的概率密度函数,在给定虚警概率的情况下,求出了接收机的检测门限。     

Ionospheric decontamination and sea clutter suppression for HF skywave Radars

In this paper, a cascaded correction and suppression method of reducing ionospheric phase path contamination and sea clutter to enable detection of targets travelling at speeds near the Bragg Doppler is addressed. The Hankel rank reduction (HRR) technique based on singular value decomposition (SVD) has been used to estimate the ionospheric phase distortion and suppress the sea clutter. Simulation results show that such a technique is helpful for the worse conditions when the target masking effect happens even after ionospheric phase decontamination. Finally, an attempt to combine another phase decontamination algorithm based on the piecewise polynomial phase modeling with the clutter cancellation stage for faster phase fluctuation is discussed briefly and some results are given.     

Adaptive techniques for radar detection and identification of objects in an ocean environment

New technology opens very interesting possibilities in regard to target adaptive radar systems. The paper illustrates this by considering a multifrequency radar concept applied to the detection and identification of ships against a sea clutter background.     

A novel CFAR tonal detector using phase compensation

A novel tonal detector is presented in this paper. The detector uses phase compensation for detecting sinusoidal signal with unknown amplitude, frequency, and phase in complex Gaussian noise with unknown variance. We analyze the statistical properties of the discrete Fourier transform (DFT) of the observations and consider the leakage effect. Then we develop the detector by using generalized likelihood ratio test (GLRT). The new detector fully utilizes the phase information of the signal and achieves constant false alarm rate (CFAR) property. Theoretical analysis and simulation results show its improvement in detection performance.     

Applying chaos to radar detection in an ocean environment: anexperimental study

Ship navigation through ice-infested waters is a problem of deep concern to the oil exploration industry of Northern countries. Conventional marine radars do not perform satisfactorily in detecting small targets such as small pieces of iceberg. This paper reports a new method for detection in an ocean environment. The approach is based on the recent observation that sea clutter, radar echoes from the sea surface, can be modeled as a nonlinear deterministic dynamical system which can be used as an alternative to the conventional stochastic process. Based on this model, detection in sea clutter is considered as dynamical system classification instead of statistical hypothesis testing. Two dynamical detection methods are proposed. The first one uses a dynamical invariant called the attractor dimension to distinguish a target from a pure clutter process. The second approach tries to detect the existence of a target by observing the “difference” of the motion of the target and the clutter process. To show the validity of the idea of dynamical detection in sea clutter, real sea clutter, and target data were used in this study     

A synthetic aperture radar sea surface distribution estimation by n-order Bézier curve and its application in ship detection

To dates,most ship detection approaches for single-pol synthetic aperture radar(SAR) imagery try to ensure a constant false-alarm rate(CFAR).A high performance ship detector relies on two key components:an accurate estimation to a sea surface distribution and a fine designed CFAR algorithm.First,a novel nonparametric sea surface distribution estimation method is developed based on n-order Bézier curve.To estimate the sea surface distribution using n-order Bézier curve,an explicit analytical solution is derived based on a least square optimization,and the optimal selection also is presented to two essential parameters,the order n of Bézier curve and the number m of sample points.Next,to validate the ship detection performance of the estimated sea surface distribution,the estimated sea surface distribution by n-order Bézier curve is combined with a cell averaging CFAR(CA-CFAR).To eliminate the possible interfering ship targets in background window,an improved automatic censoring method is applied.Comprehensive experiments prove that in terms of sea surface estimation performance,the proposed method is as good as a traditional nonparametric Parzen window kernel method,and in most cases,outperforms two widely used parametric methods,K and G0 models.In terms of computation speed,a major advantage of the proposed estimation method is the time consuming only depended on the number m of sample points while independent of imagery size,which makes it can achieve a significant speed improvement to the Parzen window kernel method,and in some cases,it is even faster than two parametric methods.In terms of ship detection performance,the experiments show that the ship detector which constructed by the proposed sea surface distribution model and the given CA-CFAR algorithm has wide adaptability to different SAR sensors,resolutions and sea surface homogeneities and obtains a leading performance on the test dataset.