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Description of the OHGR database
Abstract
This Technical Note describes the Osborne Head database, collected at Osborne Head Gunnery Range, OHGR, in November 1993, with the McMaster University IPIX radar, under contract to DSS. Representative examples are worked out for the different operational modes of the radar (staring, scanning, alternate/single polarization, single/multi frequency). Strengths and weaknesses of the database are pointed out as well. The purpose of this Technical Note is to serve as a guide to the database, presenting enough information to allow the extraction of individual datasets from the raw data. These can subsequently be used for sea clutter, target model validation and/or testing of signal processing techniques, leading to enhanced target detection in sea clutter.
... Mukherjee et al [18] used support vector machines (SVM) to predict chaotic time series generated by the Mackey-Glass delaydifferential equation [19] or Lorene differential equation [20], the result shows that the SVM algorithm had better performance than RBF functions and NN, etc. SVM technique was also used for sea clutter prediction by Xia and Leung [8]. In 2018, Xing and Yan [21] modeled sea clutter by a Volterra filter [22], and verified the proposed method on the IPIX radar sea clutter dataset [23], the experimental results show that the targets can be detected based on its relatively large prediction error. Gao and Chen [10] predicted sea clutter based on general regression NN (GRNN) algorithm, this method applying adaptive particle swarm optimization algorithm [24] to optimize GRNN Gaussian width coefficient. ...
... The flow chart for sea clutter suppression is shown in Figure 6. The sea clutter data used in this paper consists of three parts: IPIX radar sea clutter collected with the McMaster University IPIX radar [23], UHF-band radar and S-band radar [36] sea clutter collected by the China Research Institute of Radio-wave Propagation. The radar emits electromagnetic waves through the transmitting antenna to the specific area of the sea surface and is scattered by the sea surface, and then the electromagnetic scattering is received by the radar receiver. ...
In the marine environment, shore-based radars play an important role in military surveillance and sensing. Sea clutter is one of the main factors affecting the performance of shore-based radar. Affected by marine environmental factors and radar parameters, the fluctuation law of sea clutter amplitude is very complicated. In the process of training a sea clutter amplitude prediction model, the traditional method updates the model parameters according to the current input data and the parameters in the current model, and cannot utilize the historical information of sea clutter amplitude. It is only possible to learn the short-term variation characteristics of the sea clutter. In order to learn the long-term variation law of sea clutter, a sea clutter prediction system based on the long short-term memory neural network is proposed. Based on sea clutter data collected by IPIX radar, UHF-band radar and S-band radar, the experimental results show that the mean square error of this prediction system is smaller than the traditional prediction methods. The sea clutter suppression signal is extracted by comparing the predicted sea clutter data with the original sea clutter data. The results show that the proposed sea clutter prediction system has a good effect on sea clutter suppression.
... Furthermore, the detailed sea clutter suppression and target detection method is introduced. In Section 5, the effectiveness of the proposed method is validated by numerical experiments using real radar data collected by the popular intelligent pixel (IPIX) processing radar [22] and the Council for Scientific and Industrial Research (CSIR) [23]. The last section concludes the paper and presents its future research direction. ...
... The effectiveness of the proposed algorithm for detection of low-observable marine target with micromotion is validated with database collected by the IPIX radar [22] and the CSIR [23]. Comparisons with other popular coherent integration methods, that is, MTD, FRFT and RFT are also given, including the integration ability, computation time and SCR improvement. ...
The micro-Doppler (m-D) signature of a marine target is used for detection within sea clutter in this study. The marine target usually has translational and rotational motions, and the corresponding radar returns can be modelled as a quadratic frequency modulated signal over a period of time. To compensate the range walk and Doppler migration of the micromotion target simultaneously, a novel long-time coherent integration detection algorithm, that is, Radon-linear canonical ambiguity function (RLCAF), is proposed. It has been proved that the m-D signal can be well matched and accumulated in the RLCAF domain using the long-time instantaneous autocorrelation function and associated with the parameterisation of the linear canonical transform. Furthermore, to suppress sea clutter and improve signal-to-clutter ratio, the authors propose a RLCAF spectrum subtraction method using the different properties of RLCAF representations between the target and sea clutter. Finally, experiments with real radar dataset indicate that the proposed method can achieve better integration and detection performance of a marine target with micromotion in case of high sea state.
... In the literature [20], when Haykin and Puthusserypady analysed the same sea clutter datasets as those in this study, these authors selected the embedding dimension m = 5 based on mutual information and the embedding delay t = 11 using the approach of global false nearest neighbour. We also use the same approach to analyse the #17 dataset. ...
... Therefore we selected the median value 11 as the embedding delay. The results are the same as those in the literature [20]. ...
An approach based on recurrence quantification analysis (RQA) measures is proposed to detect the low observable target within sea clutter in this study. Based on the criterion named the fixed amount of nearest neighbours, recurrence plots (RPs) are constructed. RQA measures are used to describe the line structures of the RPs. According to the differences between the RQA measures of sea clutter with and without a target, the low observable target within sea clutter can be detected. Four sea clutter datasets under different sea conditions in the IPIX OHGR database are selected to analyse the target detection performance of RQA measures. Compared with the multifractal correlation spectrum approach and the cell average constant false alarm rate detector, the author's approach can achieve a higher detection probability. Furthermore, the authors analyse the receiver operating characteristic curves of RQA measures in all of polarisations under different sea conditions. The experimental results show that the horizontal-horizontal polarisation dataset and one of the horizontal-vertical and vertical-horizontal polarisation datasets should be selected for the target detection, and the significant wave height has a greater influence on the target detection performance than the wind speed does.
... Section IV presents the detailed detection and estimation procedure with some remarks and explanations. In Section V, the effectiveness of the RLCAF is validated by numerical experiments using both simulated and real radar data collected by popular intelligent pixel (IPIX) processing radar [31], Council for Scientific and Industrial Research (CSIR) [32], and a sea surveillance radar (SSR). The last section concludes this paper and presents its future research direction. ...
... Comparisons with other popular coherent integration methods, i.e., MTD, FRFT, HAF, and RFT are also given, including the integration ability, SCR improvement, parameters estimation, computation time, and detection probability. The McMaster IPIX radar database [31] and the CSIR database [32] are employed to validate the detection performance of maneuvering marine target with translational motion. Moreover, the detection and estimation performances of a marine target with rotational motion are validated with the SSR data set. ...
Robust and effective detection of a marine target is a challenging task due to the complex sea environment and target's motion. A long-time coherent integration technique is one of the most useful methods for the improvement of radar detection ability, whereas it would easily run into the across range unit (ARU) and Doppler frequency migration (DFM) effects resulting distributed energy in the time and frequency domain. In this paper, the micro-Doppler (m-D) signature of a marine target is employed for detection and modeled as a quadratic frequency-modulated signal. Furthermore, a novel long-time coherent integration method, i.e., Radon-linear canonical ambiguity function (RLCAF), is proposed to detect and estimate the m-D signal without the ARU and DFM effects. The observation values of a micromotion target are first extracted by searching along the moving trajectory. Then these values are carried out with the long-time instantaneous autocorrelation function for reduction of the signal order, and well matched and accumulated in the RLCAF domain using extra three degrees of freedom. It can be verified that the proposed RLCAF can be regarded as a generalization of the popular ambiguity function, fractional Fourier transform, fractional ambiguity function, and Radon-linear canonical transform. Experiments with simulated and real radar data sets indicate that the RLCAF can achieve higher integration gain and detection probability of a marine target in a low signal-to-clutter ratio environment.
... We use MSTAR Public Data two-dimensional clutter data [17] and IPIX measured data [18] to evaluate the performance of the proposed homogenous reference cells selector. ...
... Specifically, we use the data recorded on November 11, 1993. Radar is located at 44 • 36.72 N, 63 • 25.41 W, 100 feet above sea level, the size of the data is 1900 × 160 [18]. Figure 8 is the IPIX clutter intensity map in the rectangular coordinates system. ...
This paper considers the radar scenes which contain numerous rapidly changing terrains, i.e., there are more than one clutter-edge in the environment. This kind of radar scenes incurs sharply degradation in the performance of the present adaptive constant false alarm rate (CFAR) detectors as the statistical characteristic of reference cells is highly heterogeneous. To solve this problem, we propose a homogenous reference cells selector to improve the performance of CFAR detector in highly heterogeneous environment. The selector is comprised of an M-N clutter-edge detector cascading a terrain classifier. The M-N clutter-edge detector is used to obtain multiple clutter-edges in heterogeneous environment. With the detected clutter-edges, the terrain classifier is derived to obtain identical distributed range cells. Based on the selector, a modified Log-t-CFAR detector is suggested. Finally, the performance of the proposed selector and CFAR detector is evaluated by measured data and computer simulation.
... The McMaster IPIX radar is an instrumentation-quality, coherent and dual-polarized X-band (9.39 GHz) radar system. Description of the radar data is shown in Fig. 2. The data were collected in November 1993 conducted at a site in Osborne Head Gunnery Range (OHGR), Dartmouth, Nova Scotia, on the East Coast of Canada [7]. Fig. 2(b) gives the PPI intensity plot of the observation area. ...
Recently, it has been proved that sea clutter is significantly variable and sea surface subjects have their micro-motions influenced by the sea state. The micro-Doppler (m-D) signatures can describe the refined motion characteristics of sea surface target, which provides a new solution for detection and recognition. Accordingly, it needs to find new analysis methods for m-D signals, which should possess high-resolution in time-frequency domain, high efficiency, and anti-clutter ability. In this paper, the sparse properties of m-D signal is analyzed and a novel concept named as sparse time-frequency distribution (STFD) is proposed combing the merits of classical TFD and sparse representation. Finally, the micromotion properties in sparse domain are obtained using real radar data, i.e., X-band intelligent pixel (IPIX) radar data and Council for Scientific and Industrial Research (CSIR) data collected in South Africa.
... We use IPIX Dartmouth real-life data from McMaster University's X-band polar metric coherent radar [13] [14]. It's a coherent and dual-polarized X-band radar system. ...
... The data collected by the IPIX radar of McMaster University were used to assess the detector performance in real environment [19]. IPIX radar has the ability of dual-polarized and frequency agile operation. ...
The degree of polarization (DoP) can be utilized as a detection statistic in the polarimetric radar to achieve target detection performance improvement. In this paper, a polarimetric radar model is established, which includes reflections from both target and clutter at first. Then, probability density functions (PDFs) of the estimated DoP are expressed in closed form, which is derived from joint eigenvalue distributions of complex noncentral Wishart matrices. The detector is developed and evaluated theoretically on the basis of the statistical properties of the DoP. Finally, a comparison between the new DoP detector and single-polarization detector is presented against real data. The performance improvement is demonstrated by the comparison results.
... IPIX雷达海杂波数据分析粗糙海面散射信号的FPS 特性及多种参数(波长、 变换角、 风速)对FPS的影响。 数据及说明见文献 [12]和表1， 本文主要研究HH极化 与VV极化的情况。仿真满足采样定理，量纲作归一 化处理 [11] ...
To investigate the problem of classical two-scale sea surface model, which can not effectively analyze the non-Bragg scattering at high sea state and low grazing angle, a more practical scattering model for one-dimension (1-D) time-varying sea surface is adopted, in which a chirp parameter is employed to describe the changes of Doppler frequency. Then, the angular scattering characteristic of the improved model is investigated based on the mean scattered power. Next, the Fractional Power Spectrum (FPS) of the improved model is analyzed, which shows that the FPS of the scattered field is found to be distributed as sum of impulse signals and the peaks are relative to the frequency parameters of the improved model. Finally, X-band real sea data is used for verification and the influences of incident wavelength and transform angle on the FPS are discussed. The results prove that the improved model is suitable for the analysis and extraction of the frequency changes and Doppler shift for the time-varying sea surface.
... Accordingly, texture variables are sampled from a gamma distribution and correlated by passing them through a linear filter. In order to have a realistic speckle covariance matrix, we estimated the autocorrelation function of the speckle component from experimental clutter data, collected at the Osborne Head Gunnery Range (OHGR) with the McMaster University IPIX radar [13]. In each sub-dwell, K = 10 pulses of a 1.5 ,s duration signal are transmitted. ...
Inthis paper, wepropose amethod toexploit waveform agility inmodernradars toimprove performance inthechallenging task ofdetecting small targets ontheoceansurface inheavy clutter. Theapproach exploits thecompound-Gaussian model forseaclutter returns toachieve clutter suppression byform- inganorthogonal projection ofthereceived signal into the clutter subspace. Waveform scheduling isthenperformed by incorporating theinformation about theclutter into thedesign ofthenexttransmitted waveform. A simulation study demon- strates theeffectiveness ofourapproach. Index Terms-Clutter, radar detection, signal design
... Accordingly, texture variables are sampled from a gamma distribution and correlated by passing them through a linear filter. In order to have a realistic speckle covariance matrix, we estimated the autocorrelation function of the speckle component from experimental clutter data, collected at the Osborne Head Gunnery Range (OHGR) with the McMaster University IPIX radar [13]. In each sub-dwell, K = 10 pulses of a 1.5 ,s duration signal are transmitted. ...
In this paper, we propose a method to exploit waveform agility in modern radars to improve performance in the challenging task of detecting small targets on the ocean surface in heavy clutter. The approach exploits the compound-Gaussian model for sea clutter returns to achieve clutter suppression by forming an orthogonal projection of the received signal into the clutter subspace. Waveform scheduling is then performed by incorporating the information about the clutter into the design of the next transmitted waveform. A simulation study demonstrates the effectiveness of our approach
... The results presented in this paper relate to Osborn Head database, collected at Osborne Head Gunnery Range (OHGR), in November 1993, with McMaster University IPIX radar of RF 9.39GHz[13]. The radar site was located on a cliff facing the Atlantic Ocean, at a height of 100 feet above the mean sea level and an open view of about 130°. ...
This paper introduces fractal-based variable step-size least mean square(FB-VSLMS) algorithm and proposes a model for radar target detection in sea clutter. FB-VSLMS algorithm deals with a specific class of fractal signals and except one parameter requiring time-varying constraints, the constraints on the remaining parameters are time-invariant. And the step-size matrix is determined completely with the knowledge of the deterministic Hurst exponent. The model based on this algorithm is suited for tracking signals from the family of fractal signals that are inherently nonstationary. In the end, the performance of the novel model is analyzed. By the verification of X-band real sea clutter, the model is shown to be effective for low-observable point target detection in sea clutter.
... To evaluate the performance of the polarimetric detectors in a real application situation, we use data collected at the Osborne Head Gunnery Range (OHGR), Dartmouth, Nova Scotia, Canada, with the McMaster University IPIX radar [22], [23]. Specifically, we use the data recorded on November 11 and 12, 1993, listed in Table I We ran our test , in addition to the polarimetric detectors PST-GLR and TF-GLR, against the former datasets. ...
Polarization diversity in radar is useful to detect targets , particularly when Doppler discrimination is not possible. We introduce a new polarimetric radar model that includes the realistic dependence of the clutter reflections on the transmitted signal. We then develop a statistical polarimet- ric detection test, robust to heavy inhomogeneous clutter. We prove that the detector's false-alarm rate is invariant to the space and time variabilities of the clutter; hence, it has the constant false-alarm (CFAR) property, while still maintaining a good probability of detection. We demon- strate the improved performance of the proposed detector in comparison with existing detectors using numerical ex- amples and real data. from the scheme presented in (4) has a closed-form expres- sion only for the special case of two polarimetric channels, and the detector proposed in (5) does not support the con- stant false-alarm rate (CFAR) property with respect to the clutter covariance. In this paper, we develop a polarimetric detector whose decision-making capability is based on the data collected only from the range cell under test, without resorting to sec - ondary data or prior knowledge of the target and clutter. In order to derive the detector, we first introduce a new polari- metric radar model that states the dependence of the clut- ter reflections on the transmitted signal. We prove that it is robust against inhomogeneous and non-stationary clutter; i.e. the detector's false-alarm rate is insensitive to spac e and time variations of the clutter, while still maintaining a go od probability of detection. Moreover, this test is CFAR. In that sense, this detector resembles that of (6); however, ou r
... The database contains a variety of targets, including small boats and beach ball targets. A data set is used in this study to investigate the spatial temporal dynamics of sea clutter (Drosopoulos 1994). Its original radar image plotted inFig. ...
Constrained optimization problems arise in a wide variety of scientific and engineering applications. Since several single recurrent neural networks when applied to solve constrained optimization problems for real-time engineering applications have shown some limitations, cooperative recurrent neural network approaches have been developed to overcome drawbacks of these single recurrent neural networks. This paper surveys in details work on cooperative recurrent neural networks for solving constrained optimization problems and their engineering applications, and points out their standing models from viewpoint of both convergence to the optimal solution and model complexity. We provide examples and comparisons to shown advantages of these models in the given applications.
... The radar site was located at N, W on a cliff at Osborne Head, Nova Scotia, Canada, facing the Atlantic Ocean at a height of about 30 m above mean sea level and an open ocean view of about 130 . The database contains a variety of targets, including small boats and beach ball targets [35]. Three data sets are used in this study to investigate the spatial-temporal dynamics of sea clutter. ...
The problem of spatial-temporal signal processing and modeling has been of great interest in recent years. A new spatial-temporal prediction method is presented in this paper. An optimal fusion scheme based on fourth-order statistic is first employed to combine the received signals at different spatial domains. The fused signal is then used to construct a spatial-temporal predictor by a support vector machine. It is shown theoretically that the proposed method has an improved performance even in non-Gaussian environments. To demonstrate the practicality of this spatial-temporal predictor, we apply it to model real-life radar sea scattered signals. Experimental results show that the proposed method can provide a more accurate model for sea clutter than the conventional methods.
... From analysis of experimental sea clutter data collected at the Osborne Head Gunnery Range (OHGR) with the McMaster university IPIX radar [15], it has been found that the speckle is correlated over 1-5 ms while the texture remains correlated over 50-60 s [16,17,13]. If the PRF is high enough to ensure that the K pulses in a sub-dwell are transmitted with a duration of 1-2 ms, we can expect that the clutter returns will not be independent so that Σ = I K and that the texture variables are practically constant across a complete dwell. ...
In this paper, we consider a radar system capable of adaptively adjusting its transmitted waveform to mitigate the effect of the environment and improve detection performance. We thus bring the potential of waveform agility to bear on the challenging problem of target detection in heavy sea clutter. Using a two-stage procedure, we first estimate the statistics of the sea clutter in the vicinity of a predicted target. A phase-modulated waveform is then designed and transmitted so as to maximize the generalized likelihood ratio at the predicted target location, thus improving the signal-to-clutter ratio (SCR). Employing the compound-Gaussian (CG) model, we exploit a subspace-based approach to further mitigate sea clutter and deliver improved detection performance. Simulations illustrate the effectiveness of our method.
... The radar was mounted on a cliff facing the Atlantic Ocean, at a height of 100 feet above the mean sea level, and scans the site over 370 Deg in 10 seconds in a continuous azimuth scan mode. More details on the experiment can be found in [18], [19]. The illuminated area (Dataset13) ranges from the Atlantic Ocean, to the shoreline of Cow Bay, and to the lands near Halifax. ...
This paper deals with the design and the analysis of constant false alarm rate (CFAR) detectors exploiting knowledge-based (KB) processing techniques. The proposed algorithms are composed of two stages. The former is a KB data selector which, exploiting the a-priori information provided by a geographic information system (GIS), chooses the training samples for threshold adaptation. The latter stage is a conventional CFAR processor. The performance of the new schemes are analysed in the presence of real radar data, collected by the McMaster IPIX radar, also in comparison with other common CFAR detectors. The results show that noticeable performance improvements can be obtained suitably exploiting the a-priori information available about the sensed environment.
To effectively detect low-altitude small targets under complex sea surface environment, an innovative method has been developed. This method harnesses the chaotic characteristics of sea clutter and employs a combination of Adaptive Noise Complete Ensemble Empirical Modal Decomposition (CEEMDAN), Adaptive Wavelet Thresholding (AWT), and Polynomial Fitting Filtering (SG) for denoising sea clutter data. Subsequently, the Improved Zebra Optimization Algorithm-Extreme Learning Machine (IZOA-ELM) detector is utilized to identify low-altitude small targets amidst the sea clutter background. To begin, the CEEMDAN method is applied to disentangle the measured sea clutter data into a set of Intrinsic Mode Functions (IMFs). Afterwords, the Refined Composite Multiscale Dispersion Entropy (RCMDE) is computed for each individual IMF. This process categorizes the IMFs into three distinct components: noise-dominant, signal-noise mixture, and signal-dominant segments. The noise-dominate of IMF component is subjected to denoising through AWT, the signal-noise mixture of IMF components are processed using SG filtering, while the signal-dominant of IMF remains unaltered. The denoised sea clutter signal is reconstructed by concatenating the denoised and unprocessed IMFs. Based on the chaotic nature of sea clutter signals, first-order sea clutter data is transformed into high-dimensional data through phase space reconstruction. The initial weights and thresholds of the ELM are optimized through the IZOA to establish an optimal prediction model. This model is then used to detect small, low-altitude targets by analyzing the prediction error. The algorithm's effectiveness in noise removal is validated using IPIX and SPRR measured sea clutter data, demonstrating a significant improvement in the root mean square of prediction error (RMSE) by one order of magnitude after denoising compared to the pre-denoising state. Furthermore, we observed that the IZOA-ELM method can be effectively applied to detect small targets at low altitudes across various sea conditions. However, when the sea state is complex and greatly affected by the surrounding noise, an effective approach is to first employ CEEMDAN-AWT-SG to denoise the original signal, and then utilize IZOA-ELM for target detection.
This work addresses the problem of target detection in coherent radar systems equipped with multiple polarimetric channels. In "Part I" of this two-part study, a multi-channel auto-regressive model based polarimetric detection scheme has been developed and its performance has been studied against clutter with characteristics exactly matching the adopted parametric model. In this second part of the study, the performance assessment is extended, by means of theoretical and simulated analyses, to include the case of disturbance components with diverse spectral characteristics. Consequently, an appropriate modification is introduced to the detection scheme to make it robust to typical spectral mismatches occurring in practical situations. Finally, the effectiveness of the resulting detection scheme is proved against simulated and experimental data.
In this paper, a novel time series heteroskedastic model is proposed for sea clutter modeling application. In the light of characteristics of the practical clutter at low grazing angle, the original generalized autoregressive conditional heteroskedasticity (GARCH) process, which has been widely used in various fields of economics, is extended from three aspects. First, the autoregressive moving-average (ARMA) terms are introduced for modeling the temporal correlation of both clutter returns and innovations. Second, the exponential of the conditional variance is generalized from one to arbitrary positive value, to capture the nonlinearity existing in the practical clutter. Third, the traditional Gaussian innovation is replaced by the Johnson Su random variable, which is a monotonic transformation of the Gaussian random variable and is capable of modeling the skewness and kurtosis. By systematically analyzing a large number of practical sea clutter data sets, we show that the proposed time series model fits the data better than some commonly used statistic-based distributions, such as the Weibull and compound Gaussian (CG) distributions.
This paper devises a new constant false alarm rate (CFAR) detection scheme to deal with the problem of radar target detection in heterogeneous environment. The proposed scheme, called“Clustering-CFAR Detector”, is data-dependent and composed of three stages: an adaptive clustering procedure which, exploiting the recorded measurements of the clutter environment, divides the detection area into different classes to provide auxiliary information, a dynamic reference cell selector which chooses appropriate secondary data according to the classes, and a conventional CFAR processor to make the final decision about the target presence. The performance of“Clustering-CFAR Detector” is analyzed by computer simulation and public radar measured data (IPIX data and MSTAR data), and compared with existing CFAR detectors. The results show that the new detector achieves a better performance in the aspects of terrain classification, control of false alarm points, and probability of detection. IEEE
To improve radar detection of targets in the presence of sea clutter, polarization decomposition analysis is used to output data to a neural network. To detect the target in heavy sea clutter, the received signal of each range bin is decomposed for polarimetric analysis. By applying the decomposed signal to the Self Organizing Map (SOM) network, the bin that contains the actual target range bin is identified. In simulations the algorithm located the target successfully in conditions of both mild and heavy clutter.
In the field of radar target detection, modeling of sea clutter is an effective means to restrain and eliminate background clutter. Calculated according to a single fractal theory in conventional multi-fractal modeling of sea clutter, the extracted fractal parameters have defects of instability and low amount of information. In this paper, multi fractal parameters of the observed sea clutter are extracted to be Multi-fractal modeling parameters by using multi-fractal de-trended fluctuation analysis (MF-DFA) method, and a sea clutter modeling method based on multi-factual spectrum is put forward. This method uses the "single fractal subsets with the same fractal dimension" weighted combination of probability distributions to simulate the observed sea clutter. The fractal dimension of the single fractal subsets is determined by the generalized Hurst index and corresponding weight coefficients are codetermined by Multi-fractal spectrum value and the number of small areas that are obtained by dividing fractals. The simulation process is given and the results show that the simulated sea-clutter data is identical to that of the observed one regarding Multi-fractal characteristics. What's more, their trailing data as per statistic characteristics of is also very consistent. Therefore, the effectiveness and practicability of the sea clutter modeling method presented in this paper is verified.
A new moving target detection algorithm is proposed based on the joint fractal properties discriminant of sea clutter in FRactional Fourier Transform (FRFT) domain. At first, statistical characteristic of sea clutter data in FRFT domain is analyzed and simulations of fractal curves in different polarizations are conducted, which indicates the self-similarity feature. Then, determination method of fractal parameters and scale-invariant interval is given and influence of transform order on the estimation of fractal parameters is also discussed. Finally, differences of fractal dimension and intercept in FRFT domain, which are calculated from adjacent range bin or time series of radar echo, can be used as test statistic. Real sea clutter in different polarizations is used for verification and the results present that the proposed algorithm has good performance for weak moving target detection and can also give high estimation accuracy of moving conditions.
In case of high-resolution, low grazing angle, high sea state, and horizontal transmitting, horizontal receiving polarization, the radar returns are strengthened, resulting in sea spikes. The sea spikes have the characteristics of high amplitudes, nonstationary, and non-Gaussian, which have a strong impact on the radar detection of weak marine moving targets. This study proposes a method for sea clutter suppression. Firstly, based on the sea spikes identification and selection method, the amplitude, temporal correlation, Doppler spectrum, and fractional power spectrum properties of sea spikes are analyzed. Secondly, the data to be detected are chosen by selecting the background clutter with minimum mean power, which can also eliminate the sea spikes. Correspondingly, sea clutter is suppressed with improved Signal-to-Clutter Ratio (SCR). Finally, the results of experiment with real radar data verify the effectiveness of the proposed method.
We propose a method for estimating the space-time covariance matrix of rapidly-varying sea clutter following a dynamic state space matrix model. The covariance matrix dimension can become computationally infeasible as it increases with the number of range bins and dwell pulses required for coherent processing. In order to reduce the computational complexity, we apply the Kronecker product (KP) approximation and particle filtering to estimate the space-time covariance matrix, and we demonstrate the proposed method's validity using real clutter data. We also demonstrate that the method ensures that the covariance matrix estimate is always positive definite. The covariance matrix estimation is integrated with a track-before-detect filter for tracking a low radar cross-section (RCS) target in strong sea clutter.
The real sea clutter has been proved to be nonlinear and non-stationary. The meta recurrence plot (MRP) is a tool to analyze the non-stationary time series. So in this paper, the MRP is imported to analyze the real sea clutter data firstly. For quantifying the structures of the recurrence points in the MRPs, the recurrence quantification analysis (RQA) is also used. The results show the measures based on the RQA can detect the targets in the real sea clutter effectively.
Notice of Violation of IEEE Publication Principles"Fractal-Based Variable Step-Size Least Mean Square Algorithm for Radar Target Detection in Sea Clutter"by Ningbo Liu, Zhiyu Che, Jian Guan, and Jian Zhang in the Proceedings of the IEEE Radar Conference, May 2009After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.This paper contains substantial duplication of original text from the paper cited below. The original text was copied with insufficient attribution (including appropriate references to the original author(s) and/or paper title) and without permission.Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:"Variable Step-Size LMS Algorithm for Fractal Signals"by Anubha Gupta and ShivDutt Joshiin the IEEE Transactions on Signal Processing, Vol. 56, No. 4, April 2008, pp. 1411-1420This paper introduces fractal-based variable step-size least mean square(FB-VSLMS) algorithm and proposes a model for radar target detection in sea clutter. FB-VSLMS algorithm deals with a specific class of fractal signals and except one of the step-size parameters requiring time-varying constraints, the constraints on the remaining parameters are time-invariant. And the step-size matrix is determined completely with the knowledge of the deterministic Hurst exponent. The model based on this algorithm is suited for tracking signals from the family of fractal signals that are inherently nonstationary. In the end, the performance of the novel model is analyzed. By the verification of X-band real sea clutter, the model is shown to be effective for point target detection in sea clutter.
Extended self-similar (ESS) process, as a generalized fractional Brownian motion (fBm) process, characterizes rough surfaces by a series of extended Hurst exponents instead of one single Hurst parameter that is used in the fBm process. So the ESS process is able to provide more clues about the local roughness of the surface at different scales. In this paper, the ESS process is applied to modelling the sea clutter frequency spectrum, for the reason that (a) for coherent radar, the signal-to-clutter ratio (SCR) can be promoted effectively by Fourier transform (namely in frequency domain); (b) through Fourier transform, the self-similarity in time domain can be held in frequency domain in statistical sense. Then based on X-band real sea clutter data, the extended self-similarity and the local roughness of the sea clutter frequency spectrum at different scales are analyzed. The results show that at some specific scales the extended Hurst exponents are sensitive to the existence of a target. And this sensitivity is propitious to promoting the performance of detecting weak moving target within sea clutter.
This letter introduces a kind of algebraic fractal model-Paretian Poisson process to the field of sea spike modeling and target detection. Sea spikes are strong rapidly varying echoes lasting for up to some seconds, which can be judged from the clutter background according to three parameters, i.e., the spike amplitude, the minimum spike width, and the minimum interval between spikes. Paretian Poisson process performs well in describing a power-law connection between positive-valued measurements and their occurrence frequencies. In this letter, Paretian Poisson process is used for modeling the relation between the spike durations and the spikes' occurrence frequencies. By the verification of X-band radar data, we find that Paretian Poisson process can well model sea spikes, and its parameters, the Paretian exponent and the residual sum of squares, have the potential for distinguishing targets from sea spikes. Consequently, a target detection method is proposed, and the detecting performance is analyzed. The results show that the proposed method performs well in target detection except the high requirement of the quantity of samples.
In this paper, a novel constant false alarm rate (CFAR) approach for detecting weak targets in sea clutter spectrum based on chaos synchronization is proposed. The weak target signal is detected when the synchronization between two identical chaotic systems is realized, even if the target spectrum lies inside the clutter spectrum. The threshold for the proposed CFAR detection is derived theoretically. The proposed chaos-synchronization-based CFAR technique is shown to be able to enhance the detectability of the target when the signal-to-clutter ratio and signal-to-noise ratio are low. Numerical experiments based on real radar sea clutter data confirm the effectiveness of the proposed chaos-synchronization-based CFAR detection method. The performance is superior to those of the standard autoregressive estimation-based and the cell-averaging CFAR detectors. Copyright © 2007 John Wiley & Sons, Ltd.
A key issue in detection of small targets on the ocean surface using active radar is low signal-to-clutter ratio (SCR), particularly in situations involving low grazing angle and high sea state. When sufficiently high pulse repetition rates are available, it is possible to obtain several samples of the clutter in a time interval short enough that key contributors to the clutter remain approximately stationary. This paper develops an approach to estimate the clutter subspace in such scenarios and exploit this estimate for clutter suppression and improved detection performance.
A novel cooperative neural learning (CNL) algorithm based on a new linearly constrained least absolute deviation (LCLAD) method for data fusion is proposed in this paper. The state model of the proposed CNL algorithm combines adaptively three recurrent modular neural networks and is sample for implementation using both software and hardware. Unlike the conventional LAD approach, the propose LCLAD method can obtain the optimal fusion solution. Compared with the minimum variance method and linearly constrained least square method, the proposed LCLAD method can minimize an augmented least absolute deviation energy of the linearly fused information and has the robustness performance in non-Gaussian noise environments. Illustrative examples of signal and image fusion show that the quality of the solution can be more enhanced by the proposed CNL algorithm.
We exploit the joint fractal properties of sea clutter extracted from detrended fluctuation analysis (DFA) for targets detection. We find that two specific fractal statistics, i.e., the intercept at the crucial scale and the Hurst exponent of optimal scales provide valuable information for targets detection. The first statistic measures the discrepancy between sea clutter and low observable targets at the crucial fractal scale, and the second one evaluates the average fractal difference within the optimal multi-scales. A target detection method integrating these two statistics is proposed, which is validated by real-life IPIX radar datasets. We find that this joint fractal detection approach achieves more accurate results for low observable targets detection.
The contribution of this paper is the derivation of the joint maximum likelihood (ML) estimator of complex amplitude and Doppler frequency of a radar target signal embedded in correlated non-Gaussian clutter modelled as a compound-Gaussian process. The estimation accuracy of the ML frequency estimator is investigated and compared with that of the well-known periodogram and ESPRIT estimators under various operational scenarios. The hybrid Cramer-Rao lower bound (HCRLB) and a large sample closed form expression for the mean square estimation error are also derived for Swerling I target signal. Finally, numerical results obtained by Monte Carlo simulation are checked by means of measured sea clutter data for the general case of fluctuating target amplitude.
In this work we present a thorough performance analysis of two algorithms for estimating Toeplitz covariance matrices, the structured sample covariance matrix estimator (SCME) and the structured normalised SCME (NSCME), which are employed by adaptive radar detectors against Gaussian and compound-Gaussian clutter. Performance predictions are checked with real-life sea clutter data.ZusammenfassungIn dieser Arbeit wird eine sorgfältige Analyse der Leistungsfähigkeit zweier Schätzalgorithmen für Toeplitz-Kovarianzmatrizen präsentiert: den Schätzer für die aus den strukturierten Abtastwerten gewonnene Kovarianzmatrix (SCME) und den strukturierten normalisierten Schätzer SCME (NSCME), die bei adaptiven Radardetektoren gegen Gaußsche und verbundgaußsche Störungen eingesetzt werden Die vorhergesagten Leistungsfähigkeiten werden mit wirklichen Daten von Seeklutter verglichen.RésuméDans ce travail nous présentons une analyse complète des performances de deux algorithmes pour l'estimation des matrices de covariances de Toeplitz, de l'estimateur de matrices de covariances d'échantillons structurés (EMCS) et de EMCS normalisés structurés, qui sont employés par des détecteurs radars adaptatifs contre des entassements gaussiens et gaussiens composés. Les prédictions de performances sont vérifiées avec des données réelles d'entassements marins.
In this paper, we propose a novel blind equalization approach based on radial basis function (RBF) neural networks. By exploiting the short-term predictability of the system input, a RBF neural net is used to predict the inverse filter output. It is shown here that when the prediction error of the RBF neural net is minimized, the coefficients of the inverse system are identical to those of the unknown system. To enhance the identification performance in noisy environments, the improved least square (ILS) method based on the concept of orthogonal distance to red the estimation bias caused by additive measurement noise is proposed here to perform the training. The convergence rate of the ILS learning is analyzed, and the asymptotic mean square error (MSE) of the proposed predictive RBF identification method is derived theoretically. Monte Carlo simulations show that the proposed method is effective for blind system identification. The new blind technique is then applied to two practical applications: equalization of real-life radar sea clutter collected at the east coast of Canada and deconvolution of real speech signals. In both cases, the proposed blind equalization technique is found to perform satisfactory even when the channel effects and measurement noise are strong.
Identification of a general nonlinear noisy system viewed as an estimation of a predictor function is studied in this article. A measurement fusion method for the predictor function estimate is proposed. In the proposed scheme, observed data are first fused by using an optimal fusion technique, and then the optimal fused data are incorporated in a nonlinear function estimator based on a robust least squares support vector machine (LS-SVM). A cooperative learning algorithm is proposed to implement the proposed measurement fusion method. Compared with related identification methods, the proposed method can minimize both the approximation error and the noise error. The performance analysis shows that the proposed optimal measurement fusion function estimate has a smaller mean square error than the LS-SVM function estimate. Moreover, the proposed cooperative learning algorithm can converge globally to the optimal measurement fusion function estimate. Finally, the proposed measurement fusion method is applied to ARMA signal and spatial temporal signal modeling. Experimental results show that the proposed measurement fusion method can provide a more accurate model.
Polarization diversity has proved to be a useful tool for radar detection, especially when discrimination by Doppler effect is not possible. In this paper, we address the problem of improving the performance of polarimetric detectors for targets in heavy inhomogeneous clutter. First, we develop a polarimetric detection test that is robust to inhomogeneous clutter. We run this polarimetric test against synthetic and real data to assess its performance in comparison with existing polarimetric detectors. Then, we propose a polarimetric waveform-design algorithm to further improve the target-detection performance. A numerical analysis is presented to demonstrate the potential performance improvement that can be achieved with this algorithm.
Adaptive detection of fluctuating radar targets in unknown correlated Gaussian disturbances has received considerable attention in the past. Kelly's (1986) generalized likelihood ratio test (GLRT) is the preferred algorithm for detecting Swerling-I targets in Gaussian noise. However, the problem of adaptive detection in non-Gaussian environment is still under investigation. In this paper we pursue two aims: to investigate the performance of Kelly's GLRT in non-Gaussian clutter; (ii) to derive a detection algorithm with constant false alarm rate (CFAR) behavior with respect to the amplitude probability density function (apdf) parameters and to the correlation structure of the disturbance that outperforms Kelly's GLRT in non-Gaussian clutter. Performance analysis is presented using both simulated data and real sea clutter data.
Chaos has been shown recently to be a powerful tool for modeling
sea clutter, radar echoes from a sea surface. However, electromagnetic
wave scattering from a rough surface is basically a spatial temporal
phenomenon. The paper extended the chaotic sea clutter model to a
spatial temporal chaotic model using a novel spatial temporal neural
network predictor called radial basis function coupled map lattice
(RBF-CML). The RBF-CML predictor was used to reconstruct the spatial
temporal dynamic of sea clutter, and was applied to detect small surface
targets embedded in sea clutter. Real life radar data were collected to
validate the effectiveness of this spatial temporal chaotic approach.
Results showed that the spatial temporal approach outperformed the
purely temporal method in terms of both sea clutter modeling and target
detection
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