Search

Due to the technical barriers between radars and jammers and the poor performance of the traditional detection‐jamming shared signal in integrated radar‐electronic warfare systems, a new detection‐jamming shared signal waveform based on the virtual force field algorithm (VFFA) is proposed in this article. First, a multiobjective and multidimensional characteristic parameter optimization model, based on a virtual force field, is established, and then the design principle of the shared signal is presented in detail. The simulation results show that the detection‐jamming shared signal based on the VFFA presents the deceptive jamming of multiple false targets in a noncollaborative target's radar screen. Further, there is better detection performance with the advantages of multiple pulse repetition frequency (PRF) and pulse accumulation numbers, which are highly sensitive to the multijagged PRF signals emitted by the noncollaborative target. According to the VFFA described in this article, the optimum detection‐jamming shared signal waveform can be output in real time for specific free space targets, to improve the efficiency of integrated radar and electronic warfare systems. [ABSTRACT FROM AUTHOR]

An algorithm based on a support vector machine (SVM) is proposed for hydrometeor classification. The training phase is driven by the output of a fuzzy logic hydrometeor classification algorithm, i.e., the most popular approach for hydrometer classification algorithms used for ground-based weather radar. The performance of SVM is evaluated by resorting to a weather scenario, generated by a weather model; the corresponding radar measurements are obtained by simulation and by comparing results of SVM classification with those obtained by a fuzzy logic classifier. Results based on the weather model and simulations show a higher accuracy of the SVM classification. Objective comparison of the two classifiers applied to real radar data shows that SVM classification maps are spatially more homogenous (textural indices, energy, and homogeneity increases by 21% and 12% respectively) and do not present non-classified data. The improvements found by SVM classifier, even though it is applied pixel-by-pixel, can be attributed to its ability to learn from the entire hyperspace of radar measurements and to the accurate training. The reliability of results and higher computing performance make SVM attractive for some challenging tasks such as its implementation in Decision Support Systems for helping pilots to make optimal decisions about changes inthe flight route caused by unexpected adverse weather. [ABSTRACT FROM AUTHOR]

Space-time adaptive processing (STAP) is a family of algorithms frequently employed in surface moving target indication radar systems to enable detection of moving objects in the presence of fixed (i.e., nonmoving) clutter. In this paper, two new closed-form expressions that quantify the loss associated with the STAP notch centered on clutter are derived in terms of system parameters of interest. Although there are many excellent reports, books, and papers focused on STAP, a simple yet accurate approximation for the STAP notch has not previously appeared. It is also shown that a new, accurate approximation for the important STAP metric known as minimum detectable velocity may be derived from the STAP notch expression. Furthermore, accurate expressions are derived that predict when ?aperture-limited? STAP performance may be obtained. This work provides the first analytical, unifying connection between these STAP metrics. As they are implemented in compact closed-form expressions, the new results are attractive for system design. The significant computational benefits associated with the new results can be very advantageous in trade studies or large simulations in which STAP performance estimates must be computed thousands of times. It is expected that this work will be of great use to both readers with a background in STAP and readers new to this field. For readers with STAP experience, this work provides a new understanding of the drivers of STAP performance and of the interconnections between STAP performance measures. For readers new to the STAP field, this work removes the mystery associated with STAP by providing intuition into how STAP works. With accurate, analytical expressions, system engineers can now implement accurate predictions of STAP performance without the necessity of constructing patch-based analysis tools that estimate STAP performance by computing the notch associated with hundreds to thousands of clutter patches. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of estimating the instantaneous frequency (IF) of multicomponent radar signals, which are assumed to be frequency-modulated (FM). The difficulties lie in that different FM components which overlap in time-frequency (TF) domain have different time-supports, and their spectral contents may present continuous and stepped IF laws. We propose an IF estimation algorithm by exploiting the combination of TF distributions (TFDs) and image processing techniques. Numerical results are provided to demonstrate desirable capabilities of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes an acceleration technique to fast evaluate the monostatic radar cross section (RCS) with the multilevel physical optics (MLPO) algorithm. The proposed method combines the adaptive cross approximation (ACA) algorithm with the MLPO to fast evaluate two-dimensional monostatic RCS (two-dimensional monostatic RCS over a range of elevation angles φ and azimuths angles j at a fixed frequency f, or a range of frequencies f and azimuths angles φ at a fixed elevation angle θ) responses. Owing to the phase compensation in the MLPO, the matrix corresponding to the compensated back-scattered field of each group is highly rank-deficient, which is compressed with ACA in a multilevel fashion therefore a lot of central processing unit time will be saved. The rank-deficiency, accuracy and computational complexity of the proposed method have been studied through a couple of numerical examples, which illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Utilizing microwave continuous-wave Doppler radars to wirelessly detect mechanical vibrations have been attracting more and more interests in recent years. In this paper, aiming to solve the null point and nonlinear issues in small-angle approximation-based Doppler radar sensors and eliminate the codomain restriction in the arctangent demodulation approach, we propose and investigate an extended differentiate and cross-multiply (DACM) algorithm. With an additional accumulator, the noise performance of the original DACM algorithm is improved. Moreover, the amplitude information of the vibration can be directly retrieved from accumulation without involving any distance-dependent issue. Experimental validations show that the proposed algorithm can fully recover the vibration patterns with the measured noncalibrated amplitude agreeing well with the known precalibrated data. Application examples of mechanical fault detection and human vital sign detection are demonstrated, showing a wide range of potential applications of this algorithm. [ABSTRACT FROM PUBLISHER]

High Frequency Surface Wave Radar (HFSWR) can perform the functions of ocean environment monitoring, target detection, and target tracking over the horizon. However, its system's performance is always limited by the severe ionospheric clutter environment, especially by the nonhomogeneous component. The nonhomogeneous ionospheric clutter generally can cover a few Doppler shift units and a few angle units. Consequently, weak targets masked by the nonhomogeneous ionospheric clutter are difficult to be detected. In this paper, a novel algorithm based on angle-Doppler joint eigenvector which considers the angle-Doppler map of radar echoes is adopted to analyze the characteristics of the nonhomogeneous ionospheric clutter. Given the measured data set, we first investigate the correlation between the signal of interest (SOI) and the nonhomogeneous ionospheric clutter and then the correlation between the nonhomogeneous ionospheric clutters in different two ranges. Finally, a new strategy of training data selection is proposed to improve the joint domain localised (JDL) algorithm. Simulation results show that the improved-JDL algorithm is effective and the performance of weak target detection within nonhomogeneous ionospheric clutter is improved. [ABSTRACT FROM AUTHOR]

Array processing for radar is well established in the literature, but only few of these algorithms have been implemented in real systems. The reason may be that the impact of these algorithms on the overall system must be well understood. For a successful implementation of array processing methods exploiting the full potential, the desired radar task has to be considered and all processing necessary for this task has to be eventually adapted. In this tutorial paper, we point out several viewpoints which are relevant in this context: the restrictions and the potential provided by different array configurations, the predictability of the transmission function of the array, the constraints for adaptive beamforming, the inclusion of monopulse, detection and tracking into the adaptive beamforming concept, and the assessment of superresolution methods with respect to their application in a radar system. 1The problems and achieved results are illustrated by examples from previous publications. [ABSTRACT FROM AUTHOR]

Given a terrain T and an antenna A located on it, we would like to approximate the Radio Map of A over T, namely, to associate a signal strength for each point p∈ T as received from A. This work presents a new Radio Map approximation algorithm using an adaptive radial sweep-line technique. The suggested radar-like algorithm ( RLA) uses a pipe-line method for computing the signal strength along points on a ray, and an adaptive method for interpolating the signal strength over regions between two consecutive rays. Whenever the difference between two consecutive rays is above a certain threshold, a middle ray is created. Thus, the density of the sampling rays is sensitive to the shape of the terrain. Finally, we report on an experiment which compares the new algorithm with other well-known methods. The main conclusion is that the new RLA is significantly faster than the others, i.e., its running time is 3-15 times faster for the same approximation accuracy. [ABSTRACT FROM AUTHOR]

The report examines the current state of methods and algorithms for determining the absolute and relative coordinates of radio emitting objects in single station onboard radar systems. [ABSTRACT FROM AUTHOR]

Non-Cooperative Target Identification (NCTI) of aircraft from radar measurements is a formidable problem that has drawn the attention of engineers and scientists over the last years. NCTI techniques typically involve a database with a huge amount of information from different known targets and a reliable identification algorithm able to highlight the likeness between measured and stored data. This paper uses High Resolution Range Profiles produced with a high-frequency software tool to train Artificial Neural Networks for distinguishing between different classes of aircraft. Actual data from the ORFEO measurement campaign are used to assess the performance of the trained networks. [ABSTRACT FROM AUTHOR]

We investigate the problem of jointly classifying and identifying multiple targets in radar sensor networks where the maximum number of categories and the maximum number of targets in each category are obtained a priori based on statistical data. However, the actual number of targets in each category and the actual number of target categories being present at any given time are assumed unknown. It is assumed that a given target only belongs to one category and one identification number. The target signals are moreover modeled as zero-mean complex Gaussian processes. In this paper, we propose a joint multi-target identification and classification (JMIC) algorithm for radar surveillance using the cognitive radar network. The existing target categories are first classified and then the targets in each category are accordingly identified. Simulation results are presented to evaluate the feasibility and effectiveness of the proposed JMIC algorithm in a query surveillance region. [ABSTRACT FROM AUTHOR]

Multi-Layer Perceptrons (MLP5) are powerful function approxima tars. In the last decades they were successfully applied to many different regression and classification problems. Their characteristics and convergence properties are well studied and relatively well understood, but they were originally designed to work with real-valued data. The main focus of this paper is the classification of polarimetric synthetic aperture radar (POLsAR) data which are a corn plex- valued signal. Instead of using an arbitrarily projection of this complex-valued data to the real domain, the paper proposes the usage of complex-valued MLP5 (Cv-MLP5), which are an extension of MLPs to the complex domain. The paper provides a detailed yet general derivation of the complex backpropagation algorithm and mentions related problems as well as possible solutions. Furthermore, it evaluates the performance of Cv-MLPs in a land-cover classification task in POLSAR images ui~der several learning conditions, and compares the proposed classifier with standard methods. The experimental results show that CV-MLPs are successfully applicable to classification tasks in POLSAR data. They show good convergence properties and a better performance if compared to real-valued MLPS. [ABSTRACT FROM AUTHOR]

In the present study, a robust algorithm for Doppler ambiguity resolution is proposed. This new algorithm is based on a recently developed robust phase unwrapping (RPUW) algorithm and the Ferrari–Bérenguer–Alengrin (FBA) method using multiple pulse repetition frequencies (PRF). In the FBA method, the PRFs are grouped into pairs and each paired PRF values are symmetric about 1. Then the Doppler frequency estimation is divided into two steps (folded frequency, i.e. the fractional part and ambiguity order, i.e. the integer part): the folded frequency is estimated by circularly averaging the folded frequency estimates for each pair PRF and the ambiguity order is obtained by searching the finite integers based on a quasi-maximum-likelihood criterion using the estimated folded frequency. By observing that the folded frequency estimates for each pair PRF may have errors because of the finite FFT implementations, noise and interference, the circular mean of the erroneous folded frequency estimates may be erroneous too, which may lead to an erroneous ambiguity order estimate, that is, a large error. In this study, the authors replace the integer ambiguity order searching by a recently developed RPUW algorithm that is robust to the frequency estimates from the FFT implementations. The simulation results of this study show that the newly proposed algorithm significantly outperforms the FBA method and is also better than the RPUW algorithm. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In modern radar systems, using stepped-frequency pulse train is an effective approach to achieve high-range resolution (HRR) with narrow instantaneous bandwidth and low system complexity. However, if the parameters of stepped-frequency (SF) radar (such as single pulse bandwidth, frequency step size and sampling instant) are not prudently designed, ‘ghost image’ phenomenon (also called range ambiguity in some literature) will corrupt the synthetic HRR profiles. The relationship between parameters of the SF radar and the ghost images is provided analytically. A new synthetic range profiling algorithm is developed to generate HRR profiles without ghost images by using the least-squares (LS) estimation. For targets with negligible radial velocities, the new LS profiling algorithm can eliminate the ghost images successfully, and introduces less signal-to-noise ratio loss. With this algorithm, the restriction on the frequency step size is less rigid than the traditional unambiguous criterion. Consequently, a higher range resolution could be obtained. Simulations and field experimental results are also presented. [ABSTRACT FROM AUTHOR]

Results from an experiment that applied one class of multiple-input multiple-output (MIMO) waveform techniques to over-the-horizon radar (OTHR) are reported. The experiment objective was to demonstrate that adaptive transmitter beamforming could be used in an appropriately design radar to reject spatially discrete Doppler-spread clutter. In the particular MIMO radar architecture that the authors call non-causal transmit beamforming, conventional or adaptive transmitter beamforming occurs following waveform transmission, propagation, scatter from targets and clutter sources, return propagation and finally signal reception. In the case reported herein spatially discrete clutter was successfully rejected to the noise floor of the radar return with rejection in excess of 35 dB achieved using common adaptive algorithms and straightforward training data selection. As part of the rejection algorithm the transmitted waveform direction-of-departure (DOD) from the transmitter array to the target was estimated and used as the preserved steer direction in the adaptive beamformer. The DOD estimates agree well with the geometrically determined true values. The demonstration of non-causal transmit beamforming suggests that it will be possible to create multiple simultaneous adaptive range-dependent transmitter beams with an appropriately designed OTHR. This has several applications including for the mitigation of Doppler-spread clutter. [ABSTRACT FROM AUTHOR]

The authors consider space–time adaptive processing (STAP) when the radar returns are recorded by a conformal antenna array (CAA). The statistics of the secondary data snapshots used to estimate the optimum weight vector are not identically distributed with respect to range, thus preventing the customary STAP processor from achieving its optimum performance. The compensation of the range dependence of the secondary data requires precise knowledge of the array response for any direction of arrival (DOA), and, thus, of the spatial steering vectors (SVs). The authors propose a novel registration-based range-dependence compensation algorithm that gives an accurate estimate of the interference-plus-noise covariance matrix under the hypotheses that calibrated spatial SVs are available only for a small set of DOAs, and that the errors in the model giving the true spatial SV for each DOA are DOA dependent. The performance in terms of signal-to-interference-plus-noise ratio loss is promising. [ABSTRACT FROM AUTHOR]

The PARAFAC decomposition of a higher-order tensor is a powerful multilinear algebra tool that becomes more and more popular in a number of disciplines. Existing PARAFAC algorithms are computationally demanding and operate in batch mode-both serious drawbacks for on-line applications. When the data are serially acquired, or the underlying model changes with time, adaptive PARAFAC algorithms that can track the sought decomposition at low complexity would be highly desirable. This is a challenging task that has not been addressed in the literature, and the topic of this paper. Given an estimate of the PARAFAC decomposition of a tensor at instant t, we propose two adaptive algorithms to update the decomposition at instant t + 1, the new tensor being obtained from the old one after appending' a new slice in the 'time' dimension. The proposed algorithms can yield estimation performance that is very close to that obtained via repeated application of state-of-art batch algorithms, at orders of magnitude lower complexity. The effectiveness of the proposed algorithms is illustrated using a MIMO radar application (tracking of directions of arrival and directions of departure) as an example. [ABSTRACT FROM AUTHOR]

The authors address the problem of multipath cancellation in the reference signal used in passive bistatic radar (PBR), which exploits an existing transmitter as emitter of opportunity. The presence of multipath echoes in the reference signal is demonstrated to strongly affect the detection performance of passive radar. Based on the well-known constant modulus algorithm (CMA) approach in the time-domain, new multi-dimensional techniques are considered for the adaptive equalisation of the reference signal based on an array of antennas and multiple receiving channels. The effectiveness of these techniques for PBR purposes is demonstrated with reference to typical simulated scenarios. The considered strategies, addressed as space-CMA and space–time-CMA, are shown to be able to recover the performance loss because of the multipath contribution in the reference signal so that they appear as very appealing solutions for PBR equipped with an array of antennas and multiple receiving channels. [ABSTRACT FROM AUTHOR]

This paper proposes an approach to deducing the floor plan of a building using only a set of noisy room dimension measurements that have been obtained indirectly through radar probes. It is not assumed that the set of measurements is complete. The key feature of this approach is a method of relating, via a graph-theoretical construction, a given floor plan topology to a model that depends linearly on a minimal set of parameters. Given a list of floor-plan topologies and a system of realizability constraints, it is possible to formulate this problem as a con- strained least-squares optimization problem. An algorithm for generating all floor plan topologies with a specified number of rooms is presented, as well as an algorithm for deciding if two floor plan representations are isomorphic. The techniques in this paper are intended to serve as an exploration into the theoretical performance bounds on the floor plan estimation problem. [ABSTRACT FROM AUTHOR]

Estimating building layouts using exterior radar measurements is a challenging task involving electromagnetic modeling, many unknown parameters, and a limited number of sensors. We propose using the jump-diffusion algorithm as a powerful stochastic tool that can be used to estimate the number of walls, their unknown positions and other parameters. We develop an efficient iterative procedure that first uses low-frequency transmissions to obtain rough estimates of the building layout. These estimates are then used to initialize the estimation at higher frequencies, thus obtaining more accurate results; We show that with proper frequency selection, the building layout can be estimated using radar measurements at only a few frequencies. Numerical examples are used to illustrate the performance of the proposed algorithm in practical situations. [ABSTRACT FROM AUTHOR]

Inverse synthetic aperture radar (ISAR) imaging of air, space or ship targets with complex motion has attracted the attention of many researchers in the past decade. Complex motion of targets induce cross-range scatterer-variant quadratic phase terms, which will degrade the cross-range resolution and affect focusing quality. A new algorithm is proposed for the ISAR imaging of complex moving targets. First, conventional range alignment, phase compensation and range compression are performed over the raw phase history data such that each range bin can be modelled as the sum of several linear frequency modulation or chirp signals. Secondly, a modified-Wigner–Ville distribution (referred to as M-WVD) approach is proposed, which is based on a scale transform in the time-frequency distribution plane and can effectively suppress the troublesome cross-term interference associated with WVD via coherent integration. Finally, the azimuth ISAR image can be obtained via a simple maximisation projection from the two-dimensional accumulated plot to the azimuth dimension. Compared with existing WVD-based ISAR imaging algorithms, the proposed method has the following features: better cross-term interference reduction achieved at no resolution loss, computationally more efficient with no expensive two-dimensional parameter search, and higher signal processing gain because of coherent integration during the whole imaging time. Both numerical and experimental results are provided to demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a support vector machine (SVM) classifier was designed to identify tornado vortices based on their characteristics that were determined from the Doppler spectra and eigenvalues that were calculated from the data that were collected in the vicinity of these vortices. To collect these data, weather surveillance radar (WSR-88D) was employed, which is locally operated by the National Severe Storms Laboratory (NSSL) on the north campus of the University of Oklahoma, Norman. This particular radar, which is devoted to experimental research rather than operational weather forecasting, has the unique capability of recording massive volumes of in-phase (I) and quadrature (Q) data over many hours. As such, the received radar echo power and Doppler shift information can be determined from these I and Q complex-valued data, which provides a rich environment for evaluating our new postprocessing algorithms. At the current time, most radar systems, including North America's national network of weather radar systems, process these I and Q data in real time to produce environmental measurements known as "spectral moments," which are the first three statistical moments of the data. These three moments (the reflectivity, the radial velocity, and the spectrum width) are then provided to scientists or forecasters, and the abundance of I and Q data is not preserved to save computer resources. One of the aims of this paper is to show how additional information about the atmosphere can be garnered from the land Q data. To assist this mission, an SVM-based classifier evades the pitfalls of the traditional statistical learning algorithms, such as neural networks, by setting up a convex optimization problem with a single global minimum. In addition, through the use of kernels and a nonlinear mapping to higher dimensional spaces, the SVM classifier is able to effectively handle nonlinear classification problems. The idea behind this transformation is to facilitate the separability of classes by taking the input vectors to a higher dimensional space. The SVM classifier has the added advantage of reducing overfitting by constructing a maximum margin to separate hyperplanes in a higher dimensional feature space to ensure a small generalization error bound. Finally, our practical results are in positive agreement with our theoretical predictions. [ABSTRACT FROM AUTHOR]

In a multitude of applications like e.g. in automotive radar systems a localization of multiple passive targets in the observed area is necessary. This contribution presents a robust approach based on trilateration to detect point scatterers in a two-dimensional plane using the reflection and transmission information of only two antennas. The proposed algorithm can identify and remove ambiguities in target detection which unavoidably occur in certain target constellations in such a two-antenna configuration. [ABSTRACT FROM AUTHOR]

A practical passive bistatic radar that is based on target illuminations by digital audio broadcasts is described. As with all such radars, direct signal interference (DSI) is identified as a major limitation, the effect being exacerbated by the multiplicity of illuminators. In addition, such multiplicity can also make the extraction of the original transmitted signal difficult. DSI is reduced by a combination of analogue and digital null formation and isolation by use of cross polarisation. Fast techniques are developed for signal extraction and digital null formation, allowing these operations to be achieved in real time. Both the hardware and algorithms of the system are described and representative radar observations are included to show the systems effectiveness. [ABSTRACT FROM AUTHOR]

Detection and estimation of multiple unresolved targets with a monopulse radar is a challenging problem. For ideal single bin processing, it was shown in the literature that at most two unresolved targets can be extracted from the complex matched filter output signal. A new algorithm is developed to jointly detect and track more than two unresolved targets from a single detection with the help of tracking information. That is, the method involves the use of tracking information in the detection process. For this purpose, target states are transformed into detection parameter space, which involves high nonlinearities. In order to handle the nonlinearities, the particle filter, which has proven to be effective in nonlinear non-Gaussian estimation problems, is used as the basis of the closed loop system for tracking multiple unresolved targets. In addition to the standard particle filtering steps, the detection parameters corresponding to the predicted particles are evaluated using the nonlinear monopulse radar beam model. This in turn enables the evaluation of the likelihood of the monopulse signal given tracking data. Bayesian model selection is then used to find the correct detection event. The corresponding particle set is taken as the correct representation of the target posterior. A simulated amplitude comparison monopulse radar is used to generate the data and validate the joint detection and tracking of more than two unresolved targets. [ABSTRACT FROM AUTHOR]

A novel implementation of the polar format algorithm (PFA) using the principle of chirp scaling (PCS) for spotlight synthetic aperture radar (SAR) image formation is addressed. A PFA completely free of interpolation has been achieved in moderate squinted imaging geometry. The presented approach consists of the range and azimuth scaling of the polar samples, in which only fast Fourier transforms (FFTs) and complex vector multiplications are involved. Following different processing chains, the dechirped and the chirped SAR signals are treated separately in this paper. Relative to the classic polyphase filter that uses the long sinc kernel, the new solution is able to attain a comparable result, but is much quicker by exploiting inherent properties of the linear frequency modulated (LFM) signal. Point target simulation has validated the presented methodology. [ABSTRACT FROM AUTHOR]

A single algorithm for various radar imaging applications is presented. The idea behind the proposed approach is that any motion between radar and target can always be modelled as a composition of translational and rotational components. Novel techniques to estimate and compensate these two motion components are discussed and also validated by application to real radar data for different targets (ships, aircraft, ground). By the translational-rotational-motion-compensation algorithm, good results can be achieved, especially in cases characterised by evident variation of the rotation rate during the coherent processing time for imaging. This usually happens in aircraft, ship or ground mover inverse synthetic aperture radar (ISAR) imaging as well as in squinted or forward-looking SAR. In any case, the efficiency and the accuracy of the algorithm presented for translational motion compensation might be the initial step of alternative radar imaging processing. The simplicity of the proposed algorithms makes them suitable and convenient for real-time application even for compact and low-weight radar systems to be fitted on board unmanned aerial vehicles (UAV) or other airborne platforms. The proposed concept is the result of the authors' decennial experience devoted to achieve a single, simple, robust, reliable and efficient signal processing algorithm for several real-time radar imaging applications. [ABSTRACT FROM AUTHOR]

A sparse approximate inverse (SAI) preconditioning of deflated block-generalized minimal residual (GMRES) algorithm is proposed to solve large dense linear systems with multiple right-hand sides arising from monostatic radar cross section (RCS) calculations. The multilevel fast multipole method (MLFMM) is used to accelerate the matrix–vector product operations, and the SAI preconditioning technique is employed to speed up the convergence rate of block-GMRES (BGMRES) iterations. The main purpose of this study is to show that the convergence rate of the SAI preconditioned BGMRES method can be significantly improved by deflating a few smallest eigenvalues. Numerical experiments indicate that the combined effect of the SAI preconditioning technique that clusters most of eigenvalues to one, coupled with the deflation technique that shifts the rest of the smallest eigenvalues in the spectrum, can be very beneficial in the MLFMM, thus reducing the overall simulation time substantially. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Ultrawideband radar is commonly used in the frequency range of 50-500 MHz to detect buried pipes at a depth of about 1-2 m depending on the soil characteristics. The typical feature used to locate the pipes is the hyperbolic pattern of the time of flight generated by a linear scan of the antenna above the surface. When the pipes are close together, the hyperbolas overlap, and a straightforward least squares fit is not possible. The Hough transform provides one possible solution. This paper extends the Hough transform by introducing a weighting factor depending on the differentials of the unknown parameters with respect to the experimental errors, namely, the probe position error and the time-of-flight error. This enables optimally placed sets of data pairs to be given greater weight than "ill-conditioned" sets, as for example when all data pairs lie near one end of the arc. The result is a decrease in the background amplitude with respect to the maximum of the peaks in the Hough accumulator space. It is shown that this improvement persists even when many arcs are present. A mathematical analysis with analytical results is given for the case of four unknowns: pipe radius R, pipe center position (Y, Z), and soil propagation velocity V. The results are presented through simulations introducing controlled uncertainties in the probe position, the time of flight, and its bin size. The simulations demonstrate the correlations that occur between the radius, depth, and velocity for given experimental uncertainties. [ABSTRACT FROM AUTHOR]

When tracking targets using radars and sonars, the number of targets and the origin of data is uncertain. Data may be false measurements or clutter, or they may be detections from an unknown number of targets whose possible trajectories and detection processes can only be described in a statistical manner. Optimal all-neighbor multi-target tracking (MTT) in clutter enumerates all possible joint measurement-to-track assignments and calculates the a posteriori probabilities of each of these joint assignments. The numerical complexity of this process is combinatorial in the number of tracks and the number of measurements. One of the key differences between most MTT algorithms is the manner in which they reduce the computational complexity of the joint measurement-to-track assignment process. We propose an alternative approach, using a form of soft assignment, that enables us to bypass this step entirely. Specifically, our approach treats possible detections of targets followed by other tracks as additional clutter measurements. It starts by approximating the a priori probabilities of measurement origin. These probabilities are then used to modify the clutter spatial density at the location of the measurements. A suitable single target tracking (STT) filter then uses the modified clutter intensity for updating the track state. In effect, the STT filter is transformed into an MTT filter with a numerical complexity that is linear in the number of tracks and the number of measurements. Simulations show the effectiveness of this approach in a number of different multi-target scenarios. [ABSTRACT FROM AUTHOR]

The MUSIC algorithm is a high-resolution direction finding technique which has been successfully applied to enhance radar imaging in inverse synthetic aperture radar (ISAR). Al- though this signal subspace-based method has proven effective when dealing with point targets and high signal-to-noise ratio (SNR), it may fail to work when directly applied to extended targets or target returns of low SNR. The Beamspace MUSIC (BS-MUSIC), in which the MUSIC algorithm is applied to multiple beams, is capable of locating spatially extended targets in low SNR environments. In this paper, we consider BS-MUSIC as an image formation method for indoor radar imaging problems and sensing through-the-wall applications. We compare BS-MUSIC performance to conventional beamforming and element-space MUSIC. Imaging results, using both synthesized and real data, demonstrate the advantages of the proposed algorithm in depicting targets behind walls. [ABSTRACT FROM AUTHOR]

Given a terrain and a point p on or above it, we wish to compute the region R p that is visible from p. We present a generic radar-like algorithm for computing an approximation of R p . The algorithm interpolates the visible region between two consecutive rays (emanating from p) whenever the rays are close enough; that is, whenever the difference between the sets of visible segments along the cross sections in the directions specified by the rays is below some threshold. Thus the density of the sampling by rays is sensitive to the shape of the visible region. We suggest a specific way to measure the resemblance (difference) and to interpolate the visible region between two consecutive rays. We also present an alternative algorithm, which uses circles of increasing radii centered at p instead of rays emanating from p. Both algorithms compute a representation of the (approximated) visible region that is especially suitable for is-visible-from- p queries, i.e., given a query point q on the terrain determine whether q is visible from p. Finally, we report on the experiments that we performed with these algorithms and with their corresponding fixed versions, using a natural error measure. Our main conclusion is that the radar-like algorithm is significantly better than the others. [ABSTRACT FROM AUTHOR]

Phase unwrapping is a key step in extracting digital elevation models (DEMs) from interferometric synthetic aperture radar (InSAR) data. A new two-dimensional (2-D) phase unwrapping algorithm based on ant colony optimization (ACO) is proposed, which is used to configure the shortest path linking all residues in an interferogram. Using an optimization strategy to establish the branch cuts, the unwrapping error can be significantly reduced. Simulated and real InSAR image datasets are studied to evaluate the performance of the algorithm. The results of the simulated datasets show that the errors of the algorithm are lower than some conventional methods, and the results from a real InSAR image dataset demonstrate that the ACO approach has no isolated regions in comparison with some conventional approaches. It indicates that our ACO algorithm is an optional compromise strategy between preferable phase unwrapping precision and time-consuming computation. [ABSTRACT FROM AUTHOR]

Binary integrators are an important part of the receiver in many operating radar systems. The optimisation of a binary integrator is not a simple task, because it requires the solution of a (k×n)-dimensional nonlinear optimisation problem, where n is the number of integrated bits (or the number of sensors in a distributed radar or sensor network) and k is the number of the design parameters of the single-pulse detector. An algorithm that converts the multi-dimensional optimisation problem into a one-dimensional problem, so reducing considerably the computational complexity, is developed. This reduction in computational complexity makes the real-time optimisation possible and practical, so it is very helpful for mobile sites in which the optimisation should be performed continually. The proposed algorithm can be applied when either the ‘AND’ or the ‘OR’ integration rule is adopted. The results are illustrated by means of two study cases. In the first case, the binary integrator of a constant false alarm rate radar detector is optimised; in the second one a decentralised detection system composed by n similar sensors is considered and the decision rules are jointly optimised according to the Neyman–Pearson criterion. [ABSTRACT FROM AUTHOR]

We develop the time reversal adaptive interference canceler (TRAIC) time reversal beamformer (TRBF), a new algorithm to detect and locate targets in rich scattering environments. It utilizes time reversal in two stages: 1) Anti-focusing: TRAIC time reverses and then reshapes the clutter backscatter to mitigate the clutter response; 2) Focusing: TRBF time reverses the residual backscatter to focus the radar image on the target. Laboratory experiments with electromagnetic radar data in a highly cluttered environment confirm the superiority of TRAIC-TRBF over conventional direct subtraction (DS) beamform imaging. [ABSTRACT FROM AUTHOR]

An algorithm based on space fast time adaptive processing to estimate the physical location of an interference source closely associated with a physical object and enhancing the detection performance against that object using a phased array radar is presented. Conventional direction finding techniques can estimate all the signals and their associated multipaths usually in a single spectrum. However, none of the techniques are currently able to identify direct path (source direction of interest) and its associated multipath individually. Without this knowledge, we are not in a position to achieve an estimation of the physical location of the interference source via ray tracing. The identification of the physical location of an interference source has become an important issue for some radar applications. The proposed technique identifies all the terrain bounced interference paths associated with the source of interest only (main lobe interferer). This is achieved via the introduction of a postprocessor known as the terrain scattered interference (TSI) finder. [ABSTRACT FROM AUTHOR]

The extension of the dynamic range performance of existing radar systems is integral to the requirement for current sensors to see deeper into clutter, that is, to detect weak signals (e.g. small targets) in the presence of strong interference (e.g. urban clutter signals). The rationale behind this requirement is that this additional capability greatly enhances the utility of military and commercial systems. Two approaches to improving the dynamic range of modern radar and surveillance receivers are described: (1) linearisation of intermodulation distortion (IMD) using digital post-distortion algorithms and (2) linearisation of IMD using frequency retranslation mixer (FRM). The experimental results presented illustrate that the digital post-distortion and FRM approaches are effective in providing IMD improvements for the hardware and test scenarios measured. The improvements measured ranged from 15 to 35 dB for narrowband signals to 2–6 dB for frequency-modulated continuous wave (FMCW) wideband signals. The digital post-distortion approach provided the greatest performance improvement when the input signals were between the analogue-to-digital converter full range and 20 dB below that level. The improvements measured for the FRM linearisation approach ranged from 20 to 25 dB for the narrowband two-tone tests and 16–18 dB for the wideband FMCW signal chirps. [ABSTRACT FROM AUTHOR]

In this paper, the radar target recognition is given by using LDA (linear discriminant algorithm) on angular-diversity RCS (radar cross section). The goal is to enhance the separating ability and then achieve reliable prediction in the recognition of radar targets. Initially, the angular-diversity RCS data from a target are collected to constitute RCS vectors (usually high-dimensional). These RCS vectors are first projected onto a low-dimensional PCA (principal components analysis) space. The elementary radar recognition is performed on the PCA space. However, the separating ability for such an elementary recognition is usually poor. This poor separation of radar target recognition will make the prediction results unreliable. To enhance the separating ability of radar target recognition, the projection features on the PCA space are further projected onto the LDA space and the recognition is performed on the LDA space. Our simulation shows that the separating ability for RCS based recognition of targets is much increased by using the LDA in the radar recognition process. In addition, the use of LDA in the recognition process increases the ability to tolerate noise effects. This study will be helpful in many applications of radar target recognition. [ABSTRACT FROM AUTHOR]

A model-based fuzzy-logic method for hydrometeor classification using C-band polarimetric radar data is presented and discussed. Membership functions of the fuzzy-logic algorithm are designed for best fitting simulated radar signatures at C-band. Such signatures are derived for ten supervised hydrometeor classes by means of a fully polarimetric radar scattering model. The Fuzzy-logic Radar Algorithm for Hydrometeor Classification at C-band (FRAHCC) is designed to use a relatively small set of polarimetric observables, i.e., copolar reflectivity and differential reflectivity, but a version of the algorithm based on the use of specific differential phase is also numerically tested and documented. The classification methodology is applied to volume data coming from a C-band two-radar network that is located in north Italy within the Po valley. Numerical and experimental results clearly show the improvements of hydrometeor classification, which were obtained by using FRAHCC with respect to the direct use of fuzzy-logic-based algorithms that are specifically tuned for S-band radar data. Moreover, the availability of two C-band rainfall observations of the same event allowed us to implement a path-integrated attenuation correction procedure, based on either a composite radar field approach or a network-constrained variational algorithm. The impact of these correction procedures on hydrometeor classification is qualitatively discussed within the considered case study. [ABSTRACT FROM AUTHOR]

Inverse synthetic aperture radar (ISAR) images are frequently used in target classification and recognition applications. Some classifiers often require features that can be more easily obtained by extracting scattering centers from ISAR data rather than by reconstructing ISAR images. An available method for scattering center extraction, namely, the CLEAN technique, was proposed in a recent paper by Yang et al. In this paper, an improvement of this CLEAN technique is proposed that introduces a new method for detecting scattering centers. The proposed technique is based on a Gaussianity test, and its effectiveness is first theoretically proven and then tested on real data. Moreover, a comparison with the technique proposed by Yang et al. is shown. [ABSTRACT FROM AUTHOR]

The combination of frequency-modulated continuous-wave (FMCW) technology and synthetic aperture radar (SAR) techniques leads to lightweight cost-effective imaging sensors of high resolution. One limiting factor to the use of FMCW sensors is the well-known presence of nonlinearities in the transmitted signal. This results in contrast- and range-resolution degradation, particularly when the system is intended for high-resolution long-range applications, as it is the case for SAR. This paper presents a novel processing solution, which solves the nonlinearity problem for the whole range profile. Additionally, the conventional stop-and-go approximation used in pulse-radar algorithms is not valid in FMCW SAR applications under certain circumstances. Therefore, the motion within the sweep needs to be taken into account. Analytical development of the FMCW SAR signal model, starting from the deramped signal and without using the stop-and-go approximation, is presented in this paper. The model is then applied to stripmap, spotlight, and single-transmitter/multiple-receiver digital-beamforming SAR operational mode. The proposed algorithms are verified by processing real FMCW SAR data collected with the demonstrator system built at the Delft University of Technology. [ABSTRACT FROM AUTHOR]

We describe in detail the theoretical and practical implementation aspects of a simulation for marine radars which can, in particular, be used in multistatic configurations. Since the simulator is intended to deliver pseudoraw signals, it can be used later as a tool to benchmark and improve postprocessing algorithms such as bistatic synthetic aperture radar focusing algorithms and ship wake detection algorithms. The work is divided into two parts. This paper reviews and recalls theoretical prerequisites necessary in implementing such a simulator. Included are the full derivation of the bistatic radar equation from the transmitter to the receiver, accounting also for the transmit-receive time, a description of the sea state phenomenology, a review of the theory of electromagnetic scattering from the sea surface, and the presentation and validation of the method used in the simulation. A companion paper discusses the practical implementation aspects of the simulator as well as an analysis of our results. [ABSTRACT FROM AUTHOR]

Standard synthetic aperture radar (SAR) processing algorithms use analytically derived transfer functions in the 2-D frequency and range/Doppler domains. These rely on the assumption of hyperbolic range histories of monostatic SARs with straight flight paths. For bistatic SARs, the range histories are no longer hyperbolic, and simple analytic transforms do not exist. This paper offers two solutions for bistatic SAR data processing under the restriction of quasi-stationarity, i.e., sufficiently equal velocity vectors of transmitter and receiver. 1) Moderately bistatic configurations can be handled satisfactorily by using hyperbolic range functions with a modified velocity parameter, which is a solution already well known for the accommodation of curved orbits in the monostatic case. This "equivalent velocity" approach is shown to be of surprising range of validity even for pronounced bistatic situations. It is not to be confused with the "equivalent monostatic flight path" approximation, which is shown to be inapplicable for any practical case. 2) With increasing separation of transmitter and receiver, the equivalent velocity approximation deteriorates. To cope with extreme bistatic configurations, a general approach named "NuSAR" is proposed, where the involved transfer functions are replaced by numerically computed ones. This paper describes how the transfer functions are computed from the given orbits and the shape of the Earth surface. In any of these two cases, the bistatic SAR data can be processed by standard SAR processors; only the conventional transfer functions need to be replaced. Neither are there time-domain prefocusing or postfocusing steps required nor complicated mathematical expansions involved. The presented algorithms are also applicable to very high resolution wide-swath (or squinted) SARs on curved orbits. [ABSTRACT FROM AUTHOR]

The Digital Elevation Model that has been derived from the February 2000 Shuttle Radar Topography Mission (SRTM) has been one of the most important publicly available new spatial data sets in recent years. However, the 'finished' grade version of the data (also referred to as Version 2) still contains data voids (some 836,000 km2) - and other anomalies - that prevent immediate use in many applications. These voids can be filled using a range of interpolation algorithms in conjunction with other sources of elevation data, but there is little guidance on the most appropriate void-filling method. This paper describes: (i) a method to fill voids using a variety of interpolators, (ii) a method to determine the most appropriate void-filling algorithms using a classification of the voids based on their size and a typology of their surrounding terrain; and (iii) the classification of the most appropriate algorithm for each of the 3,339,913 voids in the SRTM data. Based on a sample of 1304 artificial but realistic voids across six terrain types and eight void size classes, we found that the choice of void-filling algorithm is dependent on both the size and terrain type of the void. Contrary to some previous findings, the best methods can be generalised as: kriging or inverse distance weighting interpolation for small and medium size voids in relatively flat low-lying areas; spline interpolation for small and medium-sized voids in high-altitude and dissected terrain; triangular irregular network or inverse distance weighting interpolation for large voids in very flat areas, and an advanced spline method (ANUDEM) for large voids in other terrains. [ABSTRACT FROM AUTHOR]

Computer simulations are widely used in multi-sensor system tests. Algorithms for detection, tracking, classification, identification, intent/threat evaluation, etc., are tested either by using measured real data, or with synthetically generated computer data. Real data is expensive to record and can only be available for simple, sample scenarios. Therefore, a synthetic representation of the radar signal environment is essential. How realistic the synthetic data is depends on the physical and statistical understanding of the radar signal environment, and its stochastic modeling based on random-number generation. This tutorial briefly reviews the generation of a synthetic radar-signal environment. [ABSTRACT FROM AUTHOR]

This paper addresses the capability of synthetic aperture radar and optical images in combination with theoretical models to detect the vegetation water content (VWC) at field level. In this paper, a retrieval algorithm for the estimation of VWC from AirSAR acquired on vegetated fields during the SMEX'02 experiment is addressed. The aforementioned campaign has been chosen because, along with sensor observations, extensive ground truth measurements were acquired. The retrieval procedure, which is based on a Bayesian approach, has been initially developed for soil moisture extraction, It consists of two modules: one is pertinent to bare soils and the other one has been modified for vegetated fields. The last one uses the synergy with optical images to correct for the contribution of VWC. The VWC, a variable in the inversion procedure, as well as soil moisture can be estimated. The results indicate a good correlation with both ground measurements and VWC calculated from Landsat images through the use of normalized difference water index (NDWI). Furthermore, in the inversion procedure, the introduction of the dependence on roughness improves the estimates. This indicates that, even for dense vegetation, the contribution from bare soil greatly influences the radar signal. Three main levels of VWC are discriminated in the inversion procedure: values below 1 kg/m², values between 1 and 3 kg/m², and values greater than 3 kg/m². [ABSTRACT FROM AUTHOR]

An accurate knowledge of snow thickness and its variability over sea ice is crucial in determining the overall polar heat and freshwater budget, which influences the global climate. Recently, algorithms have been developed to extract snow thicknesses from satellite passive microwave data. However, validation of these data over the large footprint of the passive microwave sensor has been a challenge. The only method used thus far has been with meter sticks during ship cruises. To address this problem, we developed an ultrawideband frequency-modulated continuous-wave radar to measure the snow thickness over sea ice. We synthesized a very linear chirp signal by using a phase-locked loop with a digitally generated chirp signal as a reference to obtain a fine-range resolution. The radar operates over the frequency range from 2-8 GHz. We made snow-thickness measurements over the Antarctic sea ice by operating the radar from a sled in September and October 2003. We performed radar measurements over 11 stations with varying snow thicknesses between 4 and 85 cm. We observed an excellent agreement between radar estimates of snow thickness with physical measurements, achieving a correlation coefficient of 0.95 and a vertical resolution of about 3 cm. Comparison of simulated radar waveforms using a simple transmission line model with the measurements confirms our expectations that echoes from snow-covered sea ice are dominated by reflections from air-snow and snow-ice interfaces. [ABSTRACT FROM AUTHOR]

This paper introduces a fast and efficient algorithm to calculate the radar cross section of complex targets using shooting and bouncing rays. The method combines a multiresolution grid division algorithm, with an algorithm that divides space using an octree structure. Applying the two division algorithms simultaneously, accelerates computation speed in ray tracing without reducing accuracy. Numerical results show the accuracy and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]