Clutter Statistics Research Articles

Simulation of coherent sea clutter

This study provides a brief overview of methods used to simulate sea clutter. It then offers a simple and flexible way of generating non-stationary coherent sea clutter that matches higher order statistics of real clutter in the time domain, as well as the shape and intensity distribution within the Doppler domain. Results confirm that the simulated signal intensity can also be well matched by the K-distribution.

Automatic target detection in simulated ground clutter (Weibull distributed) by multilayer perceptrons in a low-resolution coherent radar

This study presents the problem of detecting known targets (Swerling 0 model) in simulated ground clutter (land cultivated). As a modelled low-resolution coherent radar system is used, the clutter is modelled by a complex-valued time-correlated Weibull distribution. The research exposed in this study looks for two objectives. First, finding a detection scheme that permits the final user (radar operator) determine if a target is present or not, and its position, size and shape. And second, designing a detection scheme as robust as possible against clutter and target condition changes and approximating the specification and measurement of the radar performance by using clutter models. However, these tasks are really complicated, because high-level clutter echoes are received. A detection scheme based on neural networks is proposed, where feedforward multilayer perceptrons are used. The performance obtained with this detection scheme is discussed from subjective (visual analysis of the achieved detection scans) and objective (receiver operating characteristics) points of view. Finally, this scheme is compared with a coherent detector commonly used when Weibull-distributed clutter is present, the target sequence known a priori detector. This comparison empirically demonstrates the performance improvement achieved by the proposed detector comparing with the commonly used.

Measurement and modelling of bistatic radar sea clutter

Bistatic radar is a subject of considerable present interest. Despite this, current understanding of the properties of bistatic clutter, and in particular, bistatic sea clutter, is limited at best. The purpose of this study is to present an analysis of the limited existing published radar data and to derive an empirical model, which expresses the variation of mean σ0 with the measurement geometry and sea conditions. This empirical model is then compared with electromagnetic (EM) scattering calculations using the composite model (sometimes called the two-scale model) to show the extent to which the EM model is able to reproduce the trends observed in the data. The results indicate where improvements to bistatic EM modelling are required (very low grazing angles and out-of-plane scattering), and a general need for more radar data to extend the empirical σ0 model and expand it to the bistatic clutter statistics.

Artificial Neural Network-Based Clutter Reduction Systems for Ship Size Estimation in Maritime Radars

The existence of clutter in maritime radars deteriorates the estimation of some physical parameters of the objects detected over the sea surface. For that reason, maritime radars should incorporate efficient clutter reduction techniques. Due to the intrinsic nonlinear dynamic of sea clutter, nonlinear signal processing is needed, what can be achieved by artificial neural networks (ANNs). In this paper, an estimation of the ship size using an ANN-based clutter reduction system followed by a fixed threshold is proposed. High clutter reduction rates are achieved using 1-dimensional (horizontal or vertical) integration modes, although inaccurate ship width estimations are achieved. These estimations are improved using a 2-dimensional (rhombus) integration mode. The proposed system is compared with a CA-CFAR system, denoting a great performance improvement and a great robustness against changes in sea clutter conditions and ship parameters, independently of the direction of movement of the ocean waves and ships.

Error bounds for classifying targets in non-Rayleigh clutter.

False alarms in active sonar systems are often represented statistically as having a probability density function (PDF) with tails heavier than the traditionally assumed Rayleigh PDF. Distributions such as the Weibull, K, and Poisson–Rayleigh have been used to represent such non-Rayleigh clutter and to derive the associated probabilities of false alarm and detection, the latter for standard target models (e.g., nonfluctuating and fluctuating). In this presentation, the probability of at least one misclassification per ping (Pc) is evaluated when classifying the standard target types against non-Rayleigh clutter having equal average power. The Bhattacharyya bound is used as an upper bound for the Bayesian error probability (Pe) of a classifier operating on a single cluster and combined with the average number of clusters per ping (Nc) to form Pc. As expected, Pc increases as the clutter becomes more non-Rayleigh. Clutter statistics are seen to affect Pc through both Pe and Nc. However, the effect of clutter statistics on Nc is significantly greater than on Pe. Increasing bandwidth reduces Pc (and therefore Pe) when its impact is modeled as an increase in the number of independent samples available for classification. [Work sponsored by the Office of Naval Research under Contract No. N00014-09-C-0318.]

Target/clutter discrimination using Bayesian active/passive data fusion.

Discriminating targets of interest from background clutter is a key challenge in undersea active sonar. This is particularly true in littoral areas, where scattering from the bottom, motion in the water column, and activity on the surface all contribute to produce echoes which are difficult to distinguish from targets of interest. Statistically, these effects manifest themselves in non-Rayleigh, heavy-tailed amplitude distributions. An approach to target/clutter discrimination is described which uses the complementary information from both active and passive acoustic sensors to facilitate this task. The method uses an efficient, grid-based Bayesian track-before-detect scheme to combine data from the two types of sensors by carefully modeling the effects of array processing, replica correlation, normalization, and clutter statistics. Representing measurements and uncertainty in terms of likelihood functions then provides a common framework for fusion. In this manner, active returns with coincident and appropriate passive signals are given more credence, while the presence of only one or the other, while perhaps suggestive, is not as compelling. The presentation will give a general overview of the approach, and an example will be used to illustrate the potential power of using passive data to mitigate active clutter. [Work supported by the Office of Naval Research.]

High Resolution Angle-Doppler Imaging for MTI Radar

To reduce the need for training data or for accurate prior knowledge of the clutter statistics in space-time adaptive processing (STAP), we consider high resolution angle-Doppler imaging by processing each range bin of interest independently. Specifically, we use a weighted least-squares-based iterative adaptive approach (IAA) to form angle-Doppler images of both clutter and targets for each range bin of interest. The resulting angle-Doppler images can be used with localized detection approaches for moving target indication (MTI). We show via numerical examples that the robust and nonparametric IAA algorithm can be used to enhance the MTI performance significantly as compared with existing approaches.

Enhancement of dim small target through modified top-hat transformation under the condition of heavy clutter

A new algorithm to enhance dim small target through modified top-hat transformation is proposed in this paper. Firstly, the property of top-hat transformation is analyzed following the property of small target regions. Secondly, a judging value is calculated following the properties of target region and top-hat transformation. Finally, the judging value is imported into the top-hat transformation to form the modified top-hat transformation. Because of the judging value in modified top-hat transformation, dim target can be significantly enhanced and heavy clutter can be effectively suppressed. Experimental results verified that the modified top-hat transformation for target enhancement under the conditions of heavy clutter and dim target intensity was effective and robust.

Azimuth-invariant bistatic multichannel synthetic aperture radar for moving target detection and location

Ground moving target indication (GMTI) is one of the most important applications of the bistatic synthetic aperture radar (SAR) system as well as the monostatic system. An algorithm for moving target detection and location is presented with an azimuth-invariant bistatic multichannel SAR, which consists of one transmitter (channel) and multireceivers (multichannel). The algorithm is based on the discussion of the particularities of the bistatic SAR configuration including coherence improvement and clutter characteristics. Then, the corresponding compensating methods including two-dimensional range-azimuth prefiltering and bistatic differential range correction are proposed to solve these particularities. It is shown that using the compensating methods, the stationary clutter can be suppressed and the moving parameters of ground targets can be estimated accurately. Finally, simulation results demonstrate the effectiveness of the proposed algorithm.

Modulated backscatter microwave security sensor

AbstractA system capable of deployment as a microwave security sensor which can automatically reject background clutter is presented. The principle of operation is based on analog homodyne detection using I, Q single side‐band down conversion of an AM backscattered modulating signal envelope. A demonstrator is presented which operates with a carrier frequency of 2 GHz and 500 KHz backscattered signal. When deployed in a multipath rich open plan office environment the S/N ratio obtained at the detection output was better than 20 dB at 20 m range with 20 dBm EIRP in a 2 MHz detection bandwidth despite the presence of time varying and static clutter. © 2009 Wiley Periodicals, Inc. Microwave Opt Technol Lett 51: 2492–2495, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.24636

Flower classification by bats: Radar comparisons

In recent years, with the development of high-range resolution radars, the desire to identify targets under all weather and clutter conditions has become of great importance. This is an activity carried out with great success by echo-locating mammals such as nectar feeding bats that are able to detect and select flowers of bat-pollinated plants, even in a dense clutter environment. Herein, data consisting of acoustically-generated high-range resolution profiles of four bat-pollinated flower heads are analysed. Multi-perspective classification performance is assessed and compared with the radar case. There are close parallels that suggest lessons can be learnt from nature.

Exploitation of Ship Scattering in Polarimetric SAR for an Improved Classification Under High Clutter Conditions

This paper evaluates the potentialities of polarimetric ship scattering for basing classification methods that provide reasonable performance within cluttered scenes. Both simulated and airborne polarimetric synthetic aperture radar (SAR) images have been used to validate the conclusions of a previous phenomenological study. Numerical simulations have been carried out with GRECOSAR, a polarimetric interferometric SAR simulation tool that is able to process highly complex targets with a fast and accurate radar-cross-section prediction module. A representative set of scenarios has been defined, which includes various realistic ship models, sea states, and imaging geometries. In all of them, a two-scale sea surface model precisely accounting for sea-ship interaction and sea clutter has been added. The analysis of different images has shown that, with an adequate spatial resolution, ships may be characterized by a particular spatial arrangement and polarization state distribution of dominant scattering centers. This feature has allowed one to propose a new classification algorithm, which shows a promising behavior after various preliminary tests. In this paper, the performance of this technique is further evaluated with realistic clutter. The results show that robust classification is possible even in highly cluttered scenes if quad-pol imagery is available. On the contrary, in low clutter conditions, the usage of less restrictive solutions, like circular dual-pol schemes, is feasible and may still get an acceptable performance.

Propagation focusing in the context of clutter statistics.

Fluctuations in reverberation (clutter) from scatterers at long range in shallow water can be affected by many mechanisms. Their statistics, in particular the scintillation index, depend not only on the statistics of the scatterers (their physical distribution, shape, orientation, etc.) but on the outward and return propagation paths. Focusing effects due to forward scattering from weak undulations in the seabed will be presented. Existing Fresnel–Kirchhoff theory for monostatic, vertical incidence on a two‐dimensional surface is adapted to demonstrate the behavior of the intensity’s scintillation index in bistatic geometry. In addition, using a simulation of bistatic geometry and a one‐dimensional surface, the intensity statistics are extended to include autocorrelation function and probability distribution. Bistatic geometries include source and receiver moving with constant separation, and moving source with fixed receiver. Fairly modest vertical undulations can result in significant fluctuation due to reflection focusing with scintillation indices possibly greater than unity.

Midfrequency active sonar clutter statistics segregated by broad clutter types.

The empirical distributions of normalized matched‐filter echoes from a midfrequency active sonar with hyperbolic frequency‐modulated waveforms in a myriad of oceanic environments are studied for three broad clutter classes: bottom structures, diffuse compact clutter (e.g., seaweed), and compact nonstationary clutter (e.g., biologics). The distributions are characterized using the K distribution and the generalized Pareto distribution. Methods of parameter estimation are discussed, and parameters are computed for small subregions of the clutter fields. A plot of the Kolmogorov–Smirnov goodness‐of‐fit statistic of individual subregions is presented for each model and class to highlight the versatility of the models when applied to large quantities of data. Cumulants are computed from the data and are utilized as features in a classifier to demonstrate separability between the classes. An important aspect of this work is the use of distinct clutter classes as opposed to collectively characterizing all clutter as reverberation. Environmental effects are not considered, as the goal of this work is to determine the utility of local clutter estimation models in practical sonar processing systems where accurate environmental data is unavailable. [This work is sponsored by the Office of Naval Research undersea signal processing discovery and invention program.]

Adaptive Radar Detection in Doubly Nonstationary Autoregressive Doppler Spread Clutter

The problem of adaptive radar detection in clutter which is nonstationary both in slow and fast time is addressed. Nonstationarity within a coherent processing interval (CPI) often precludes target detection because of the masking induced by Doppler spreading of the clutter. Across range bins (i.e., fast time), nonstationarity severely limits the amount of training data available to estimate the noise covariance matrix required for adaptive detection. Such difficult clutter conditions are not uncommon in complex multipath propagation conditions where path lengths can change abruptly in dynamic scenarios. To mitigate nonstationary Doppler spread clutter, an approximation to the generalized likelihood ratio test (GLRT) detector is presented wherein the CPI from the hypothesized target range is used for both clutter estimation and target detection. To overcome the lack of training data, a modified time-varying autoregressive (TVAR) model is assumed for the clutter return. In particular, maximum likelihood (ML) estimates of the TVAR parameters, computed from a single snapshot of data, are used in a GLRT for detecting stationary targets in possibly abruptly nonstationary clutter. The GLRT is compared with three alternative methods including a conceptually simpler ad hoc approach based on extrapolation of quasi-stationary data segments. Detection performance is assessed using simulated targets in both synthetically-generated and real radar clutter. Results suggest the proposed GLRT with TVAR clutter modeling can provide between 5-8 dB improvement in signal-to-clutter plus noise ratio (SCNR) when compared with the conventional methods.

The effect of time resolution (bandwidth) on clutter statistics: A vertical echo sounder as a simple test case.

The scintillation index (SI) of sonar returns from a rough seabed was recently simulated using a Kirchhoff approach both numerically and semi‐analytically [C. Harrison, J. Acoust. Soc. Am. 125, 2661 (2009); IEEE JOE. In press]. Of particular interest was the potentially high SI associated with seabed reflection focusing. In carrying out this kind of numerical investigation, one needs to be very careful of what precisely constitutes the ensemble. Here we perform the same analysis but now with a finite pulse length. At sufficient altitude, the tail of the pulse may contain contributions from many different almost specular facets. Depending on the sonar’s time resolution these may, or may not, be seen as many intersecting echoes. Even with a pulse length long enough to incorporate the vertical extent of the roughness, it is possible to find sharp spikes in the echo tails. Thus a plot of Si versus travel time (the ensemble being ping‐to‐ping variation) has a peak more than one pulse length after the main arrival. Amongst other things, this makes it clear that SI is highly dependent on time resolution and furthermore may be more a function of the observing system than the object being observed.

Target Detection in Single- and Multiple-View Through-the-Wall Radar Imaging

A detector of targets behind walls and in enclosed structures is presented. The detector is applied to through-the-wall radar images obtained by wideband delay and sum beamforming. We consider the detection problem using single- and multiple-view imaging. The statistics of noise, clutter, and target images are examined and formulated using sample scenes. The effects of wall parameter errors on the image statistics are shown. An iterative detection scheme, which adapts itself to the image statistics, is presented. The proposed detection schemes are evaluated using real data.

Radar detection based on Bayesian estimation of target amplitude

A new approach to detect a target with an unknown amplitude in clutter is proposed. The received signal models under two hypotheses, H 0 and H 1 , are assumed to be the same, except that the target amplitude is zero under H 0 . Using the Bayesian approach, it is shown that the likelihood ratio can be calculated as the ratio of the prior to posterior probabilities of the target amplitude. Based on this relation, a new method for target detection in Gaussian clutter is presented. This method is applied to cases with both known and unknown clutter statistics and in each case, white and coloured clutters are considered. Simulation results show that the proposed detector has a much better performance compared with conventional generalised likelihood ratio test detectors.

Two-Dimensional Multivariate Parametric Models for Radar Applications—Part II: Maximum-Entropy Extensions for Hermitian-Block Matrices

In a series of two papers, a new class of parametric models for two-dimensional multivariate (matrix-valued, space-time) adaptive processing is introduced. This class is based on the maximum-entropy extension and/or completion of partially specified matrix-valued Hermitian covariance matrices in both the space and time dimensions. The first paper considered the more restricted class of Hermitian Toeplitz-block covariance matrices that model stationary clutter. This second paper deals with the more general class of Hermitian-block covariance matrices that model nonstationary clutter. For our recently proposed 2-D time-varying autoregressive (TVAR) model, we derive optimal and computationally practical suboptimal methods for calculating such parametric models. The maximum-likelihood covariance matrix estimate for the 2-D TVAR model is also derived. The efficacy of the introduced models is illustrated by signal-to-interference-plus-noise ratio (SINR) degradation results obtained when applying the covariance matrix models to space-time adaptive processing filter design, compared with the true clutter covariance matrix provided by the DARPA KASSPER dataset.

Two-Dimensional Multivariate Parametric Models for Radar Applications—Part I: Maximum-Entropy Extensions for Toeplitz-Block Matrices

In a series of two papers, a new class of parametric models for two-dimensional multivariate (matrix-valued, space-time) adaptive processing is introduced. This class is based on the maximum-entropy extension and/or completion of partially specified matrix-valued Hermitian covariance matrices in both the space and time dimensions. This first paper considers the more restricted class of Toeplitz Hermitian covariance matrices that model stationary clutter. If the clutter is stationary only in time then we deal with a Toeplitz-block matrix, whereas clutter that is stationary in time and space is described by a Toeplitz-block-Toeplitz matrix. We first derive exact expressions for this new class of 2-D models that act as approximations for the unknown true covariance matrix. Second, we propose suboptimal (but computationally simpler) relaxed 2-D time-varying autoregressive models (ldquorelaxationsrdquo) that directly use the non-Toeplitz Hermitian sample covariance matrix. The high efficiency of these parametric models is illustrated by simulation results using true ground-clutter covariance matrices provided by the DARPA KASSPER Dataset 1, which is a trusted phenomenological airborne radar model, and a complementary AFRL dataset.