For along-track multichannel synthetic aperture radar, this paper proposes a novel ground moving target signal model in the high-resolution complex image domain. Based on the range-Doppler imaging of static scene, the 2-D complex response of an isolated rectilinearly moving target is derived via the stationary phase principle (SPP) approximations. It is shown that moving targets in complex domain can be divided into three types according to the 2-D motion distribution and the SPP approximation conditions. Different from the known peaklike response of a static target, different amplitude and phase modulations will appear for different types of moving targets. Moreover, a single target can be split into two targets in the image when its Doppler spectrum spreads over two ambiguous Doppler zones. All types of targets will have the same Doppler interferometric effect along multichannel images, which is decided by the target's ambiguous Doppler frequency. Furthermore, with the proposed signal model, the complex image properties can be completely described and analyzed. Finally, some numerical experiments are also provided to demonstrate the effectiveness of the proposed signal model and analysis.

A new method for estimating the rotational motion of a noncooperative target is presented by exploiting the 2-D space-variant characteristic of inverse synthetic aperture radar imaging. With a space-time analysis technique, the position variation of every extracted scattering center in both the range and cross-range directions can be utilized for rotation estimation. This method differs substantially from conventional methods that exploit high-order terms of the Doppler frequency, i.e., only the position variation in the cross-range direction. An iterative processing scheme is proposed to extract as many scattering centers as possible from the phase history of returned echoes. Experimental results with some measured airplane data demonstrate the effectiveness of the proposed method.

In this letter, we analyze the convergence rate of loaded sample matrix inversion (LSMI) algorithm in amplitude heterogeneous clutter environment. The probability density function of output signal to interference and noise ratio loss (SINR Loss) is derived. Then we give an approximate expression of average SINR loss. Compared with the case where samples used for estimation of covariance matrix of cell under test (CUT) are independent and identically distributed (i.i.d.) with the snapshot of CUT, if the clutter to noise ratio (CNR) of the training samples is larger than that of CUT, the convergence rate of LSMI is faster and output SINR is higher; conversely, the convergence rate of LSMI is slower and SINR is lower. Simulation validates the theoretical analysis.

For better understanding of the imaging result provided by inverse synthetic aperture radar (ISAR), it is more desirable to present it from the conventional range-Doppler domain to the homogeneous range-cross-range domain. To accomplish the process of cross-range scaling, the rotating velocity (RV) of the target should be estimated first. For a uniformly planar rotating object, a novel method based on a concept of rotation correlation is proposed to obtain the RV in this letter, and neither prominent scattering centers nor phase coefficient extraction is required. Furthermore, a three-step fast-Fourier-transform-based convolution interpolation scheme is proposed to realize the above correlation, which makes the proposed method more efficient for implementation. Finally, experimental results with both simulated and real ISAR data are provided to demonstrate the effectiveness of the proposed method for different targets.

Based on Stein's lemma, we use relative entropy as a measure of error exponent to study detection performance of Neyman Pearson (NP) detectors for both multiple-input multiple-output (MIMO) radar and phased array radar. In high signal-to-noise ratio (SNR) region, we introduce the concept of diversity order to get insights of results. In low SNR region, we give instructive approximations for analytical results of relative entropy. Finally, we use relative entropy measure to study the problem of choosing the number of transmitters and receivers for MIMO radar.

In order to characterize the signal of moving target in complex image domain (CID) of synthetic aperture radar (SAR), this paper deduces the model of moving target based on Range-Doppler imaging algorithm and stationary phase principle (SPP). In CID, the target approximately modeled as a linear frequency modulated signal (LFM), which has a great improvement in signal-clutter ratio (SCR), and distributes as mis-located segment in the 2D image. The center and slope of the segment is determined by the Doppler center and Doppler ambiguity integer. Meanwhile, the spreading length is jointly determined by the azimuth velocity and Doppler ambiguity integer. Furthermore, this paper derives the Cramer-Rao Bound (CRB) of parameter estimation in CID. At last, numeric experiments is made to approve the conclusion.

In this letter, a synthetic aperture radar (SAR) data reformatting approach named Doppler keystone transform (DKT) is proposed to correct the range migration of a moving target. By using the DKT, the SAR imaging program, i.e., the 2-D matched filtering, can be transformed into separate 1-D operations along range or azimuth direction, and therefore, the DKT is suitable for the parallel implementation of SAR imaging of the moving target. Our simulations show that by combining the DKT and the Doppler phase compensation methods, the moving target can be well imaged in high signal-clutter-ratio case.

To evaluate the coherent integration loss caused by the phase noise, an analytic upper bound is proposed in this letter when the phase noise has symmetric distribution. The proposed bound may accurately indicate the possible worst degradation with a maximum integration loss even when the distribution of phase noise is unknown, which is helpful for the evaluation of the negative effects of the phase noise. Furthermore, the specified discrete dual-peak distribution (DPD) is also given to achieve the proposed bound. Finally, some numerical experiments are provided to demonstrate the effectiveness of the proposed bound.

Velocity synthetic aperture radar (VSAR) is equipped with a linear array to receive the echoes from a radar illuminating area via multiple channels, each of which can reconstruct a reflectivity image for the same stationary scene. Based on analysis of pixel vector sampled among multi-images, VSAR may effectively suppress the strong ground clutter and improve moving target detection and location. In this paper, different Doppler-distributed properties are derived for the moving target and clutter, respectively. Then, we propose a novel parametric statistical model for VSAR by dividing the pixel vector into three components, namely, target, clutter, and noise. Furthermore, a method of adaptive implementation of optimal processing (AIOP-VSAR) is presented for moving target detection. It is shown that the optimum detection performance may be obtained via AIOP-VSAR, particularly for the slowly moving target in an inhomogeneous clutter environment. Also, the Cramer-Rao bounds (CRBs) are derived for the estimation of unknown model parameters, as well as the azimuth locations of moving targets, and the maximum-likelihood methods are proposed to reach these CRBs. Based on the proposed target detection and parameter estimation methods, we present a complete parametric flowchart for VSAR. It is demonstrated that the proposed flowchart may effectively mitigate the "azimuth location ambiguity" of VSAR and has the super-resolution ability to resolve "velocity layover" for multiple targets. Finally, some detailed numerical experiments and scene simulations are provided to show the effectiveness of the proposed methods.

Based on a parametric model and some optimum methods, it has been previously proved that parametric velocity synthetic aperture radar (VSAR) may improve the performances of moving target detection and parameter estimation simultaneously. In this paper, multilook processing is studied for parametric VSAR. At first, statistical signal models are established for azimuth multilook processing (AMLP) and range multilook processing (RMLP), respectively. By combining the multiple AMLP sublook pixel vectors, it is shown that the clutter parameter estimation accuracy can be further improved via the maximum-likelihood estimation methods. Meanwhile, based on the adaptive implementation for the optimum processing of VSAR, RMLP can be used to improve slowly moving target detection performance via the noncoherent integration of multiple range sublooks. Furthermore, it is shown that the Doppler frequencies of moving targets vary linearly with different range sublooks due to the different carrier frequencies of sublooks. Therefore, based on a proposed novel two-step multilook diversity (TS-MLD) estimator for RMLP, the "azimuth location ambiguity" of VSAR can be well resolved via least squares linear regression, without configuration change of the conventional VSAR. Also, the estimation accuracy of target's unambiguous Doppler frequency, as well as target's azimuth location, is derived for the proposed TS-MLD method. Finally, numerical experiments and scene simulations are provided to demonstrate the effectiveness of the proposed multilook-based methods.

Curvilinear synthetic aperture radar (SAR), as a more practicable 3-D SAR imaging system, utilizes parametric target feature estimates extracted from the received data to reconstruct the target image. The reconstructed image quality is then impacted by the estimation accuracy of the features. In this letter, through discussing the correlation between the system parameters and the estimation performance of the curvilinear SAR, a conclusion can be drawn on how the overall location accuracy of a target is determined by the correlation between the azimuth and elevation coordinates of the flight path, compactly characterizing the curvilinear aperture. Consequently, a new index, determined only with the aperture parameters, is proposed as an aperture evaluator, which is referred to as the feature-independent aperture evaluator (FAE). FAE can be used for guiding the operational aperture design

A robust phase-unwrapping algorithm has been recently proposed and applied to accurately locate fast moving targets in multifrequency antenna array synthetic aperture radar imaging. This algorithm is based on two dimensional (2-D) searching and its complexity may be high. In this letter, we propose an efficient implementation of the robust phase-unwrapping algorithm. We show that the range of the 2-D searching, and therefore the complexity of the robust phase-unwrapping algorithm, can be significantly reduced.

In this paper, we propose a bistatic linear array synthetic aperture radar (BLA-SAR) system for moving target detection, location, and imaging. In the BLA-SAR system, a geostationary satellite is used as a transmitter, and two airborne linear array radars are used as passive receivers, where the transmitted waveforms from the geostationary satellite may have two different carrier frequencies, two linear array antennas on two different airplanes may be equipped with different spacings, or two airplanes may fly with two different velocities. It is shown that, using the BLA-SAR, not only the stationary clutter can be suppressed but also locations of both slow and fast moving targets can be accurately estimated. Furthermore, an effective BLA-SAR algorithm of moving target imaging is also proposed. Lastly, some numerical experiments are given to demonstrate the effectiveness of the BLA-SAR

For some special case, Huynen's decomposition cannot be used to extract a desired target from an average Kennaugh matrix. In this paper, the authors modify Huynen's method for overcoming its disadvantage, based on a simple transform of a Kennaugh matrix. Using an example, the effectiveness of the modified method is validated.

We presented a novel Bayesian estimator for the correlation parameters of the multivariate Nakagami-m distributions. The proposed estimator has a two-stage structure. First, a generalized Gibbs sampler - the group-based sampler is constructed to analyze the posterior distributions of the parameters, which leads to a lower time complexity compared to the traditional Gibbs sampler. Second, a multi-proposal Metropolis-Hastings algorithm is implemented within the group-based sampler. In the Metropolis-Hastings stage, estimation values are obtained by computing the weighted mean of both accepted and rejected states. It makes the estimation variance lower compared to the traditional methods using only the accepted states. Numerical simulation results demonstrate that our new estimator is practically unbiased except some points and it offers a relatively small estimation variance.

Curvilinear synthetic aperture radar (CLSAR), whose aperture is formed via a curvilinear trajectory, is considered as a practical three-dimensional (3D) imaging system. 3D images obtained by using non-parametric methods, however, have little practical use because the data collected by CLSAR is sparse in 3D frequency space. Some parametric methods have been successfully applied to CLSAR for imaging, but are computationally expensive since they are iteration methods. A non-iterative imaging (NII) algorithm is proposed. The new algorithm estimates the range parameters of all scatterers via a modern spectrum method. It then uses these range estimates and the received data to form two-dimensional (2D) data slices, from which the cross-range parameters are estimated. Once the position (range and cross-range) estimates are obtained, the radar cross section (RCS) can be calculated from the data. Simulation results show that the new algorithm can efficiently form the target's 3D image via CLSAR.

A novel noise subspace based method is applied to the minimum localization system using time-of-arrival (TOA) measurements from three base stations (BS). Since the distance measurement between the mobile station (MS) and the BS bears analogy to the multidimensional similarity (MDS) between their coordinates, we express the MS coordinate as the linear combination of the BSs' coordinates, where the weight vector lies in the noise subspace of the MDS matrix. It is proved that this weight vector is the area coordinate of the MS when the triangle formed by the three BSs serves as the reference frame. Because the dimension knowledge of the localization problem is utilized to estimate the noise subspace and to mitigate the errors in TOA measurements, the proposed method is superior to the ordinary linear localization method in most of the enhanced quadrants of the area coordinates system.

Abstract —The polarization entropy is an important parameter and it has been used for target classification, target detection and so on. For calculating the polarization entropy, we have to obtain the eigenvalues of a covariance matrix and then use logarithm. Therefore, some calculation cost is necessary to get all the entropy values of adjacent n by n windows for all pixels in a polarimetric SAR image. In this paper, the authors propose a new method to calculate the polarization entropy, based on the least square method. Using a polarimetric SAR image, we validate the effectiveness of the proposed method. Keywords-polarization; Synthetic Aperture Radar (SAR); remote sensing I. I NTRODUCTION In polarimetric radar remote sensing, the polarization entropy is one of important parameters for target classification, target detection and so on [1-6]. For calculating the polarization entropy, however, one has to obtain the eigenvalues of a covariance matrix and then use logarithm. Consequently, some computation cost is necessary when we need get all the entropy values of adjacent n by n windows of every pixel in a polarimetric SAR image. In general, there may be more than one million pixels in a SAR image. So it is important to propose a simple method to calculate the polarization entropy. This paper will solve this problem. II. T

This paper considers the scattering environment and demonstrates that we can solve the localization problem in the case of non-line-of-sight if we could separate the multipath components with the same absolute value of Doppler-shifted frequency and estimate accurate time and direction of arrival of each multi-path component. Using a Doppler symmetric constraint on the angles between moving direction of the mobile station and line-of-sight of scatterers, a novel set of localization equations is established and the target position is estimated by a 2D searching method. Simulation results show that the proposed method is superior to the conventional localization method by taking advantage of the environment information.

A method for evaluating both the robustness and detection performance of constant false alarm rate (CFAR) detectors is presented in this paper, which is based on the powerful methodology of influence function (IF) developed in the literature on robust statistics. The robustness of different kinds of CFAR detector can be evaluated and compared by calculating the first derivative of the false alarm probability (FAP) at an underlying distribution, which is named IF-FAP. From the analysis of IF-FAP, several conclusions are given here: the robustness of the detector can be asymptotically represented by the IF of the clutter power estimator, while the detection performance can be approximately evaluated by the asymptotic variance of the clutter power estimator which is an explicit functional of the corresponding IF.

The problem of super-resolution time delay estimation in multipath environments is addressed in this paper. Two cases, active and passive systems, are considered. The time delay estimation is first converted into a sinusoidal parameter estimation problem. Then the sinusoidal parameters are estimated by generalizing the multiple signal classification (MUSIC) algorithm for single-experiment data. The proposed method, referred to as the MUSIC-type algorithm, approximates the Cramer-Rao bound (CRB) in terms of the mean square errors (MSKs) for different signal-to-noise ratios (SNRs) and separations of multipath components. Simulation results show that the MUSIC-type algorithm performs better than the classical correlation approach and the conventional MUSIC method for the closely spaced components in multipath environments.

A generalized optimization of polarimetric contrast enhancement (GOPCE) is proposed in this letter. For this problem, it is not only necessary to find the optimal polarization states such that the received power ratio of a desired target and clutter is maximal, but also necessary to find three optimal coefficients such that the ratio of two factors associated with the desired target and clutter is maximal, where both the factors consist of three parameters, i.e., the Cloude entropy and two special similarity parameters. The optimal coefficients of the GOPCE are obtained by solving an eigenvalue problem. Using an example, we demonstrate that the GOPCE can be employed for detecting roads in a forest area by using polarimetric synthetic aperture radar data.

In this paper, a new approach to speckle filtering of synthetic aperture radar (SAR) data is presented. We define a parameter space consisting of two orthogonal subspaces-the signal subspace and the noise subspace. Then, the full polarimetric information from the signal subspace is obtained after speckle filtering. In this way, edges of different kinds of targets are preserved. The effectiveness of this method is demonstrated using the National Aeronautics and Space Administration Jet Propulsion Laboratory airborne L-band polarimetric SAR data.

In synthetic aperture radar (SAR) imaging, low scene contrast may degrade the performance of most of the existing autofocus methods. In this paper, by dividing a slow-time signal into three isolated components, namely target, clutter, and noise, in SAR imaging, a novel parametric statistical model is proposed during the coherent processing interval. Based on the model, Cramer-Rao bounds (CRBs) of the estimation of unknown parameters are derived. It is shown that the CRBs of the target parameter estimation strongly depend on the background, i.e., clutter and noise, and the CRBs of the background parameter estimation may be obtained regardless of the target component. Motivated from this result and using the estimated background parameters, a novel effective parametric autofocus method is developed, which is applicable to any scene contrast. In addition, a preprojection is also introduced to simplify the subsequent parameter estimation. Finally, the proposed model and the novel method are illustrated by some real SAR data.

ATR has been the most important part of the research plan for detecting and recognizing targets in many countries. In this paper, we address fast ATR algorithms for air-to-air missiles (AAMs) flying at endgame. These algorithms can be used to detect, recognize and track a target as well as determine the vulnerable components of an object. It can estimate engagement parameters such as Tgo (time to go) and adaptively control burst-point in various missile and target encounter models. Experimental results have showed that the performance and the real-time of algorithms are both satisfied to general demand of missiles.

In this paper, a video cut detection algorithm with constant false-alarm ratio (CFAR) is proposed. Applying the non-parameter based CFAR processing technique from radar signal detection, a theoretical threshold determination strategy for video cut detection is developed, which results in controllable precision and evaluative recall performances. Detailed deductions and simulation results show that this algorithm leads to very good detection performance as compared to the previous works.

In this paper, the concept of cross-entropy is introduced for measuring the difference between two scatterers in polarimetric SAR image classification. The difference between two scatterers is composed of three parts, i.e., the difference of average scattering mechanism, the difference of scattering randomness and the difference of the two spans of scattering matrices. The minimum cross-entropy principle is adopted to make the classification decision. The capability of this method is demonstrated by the experimental results.

Characteristic polarization state theory is restudied for the symmetric coherent Sinclair scattering matrix case. First, the geometric relations of the characteristic polarization states on the Poincare sphere are derived. Based on these relations, simple formulas are given for all of the characteristic polarization states of this Sinclair matrix in Stokes vector form. From the formulation, it is clear that the CO-POL Nulls are fundamental characteristic polarization states for the symmetric coherent Sinclair scattering matrix case, in that the others can straightforwardly be obtained from the Stokes vectors of the CO-POL Nulls. For further study of the characteristic polarization state and the distribution of the received powers on the Poincare sphere, the authors introduce the concept of the equi-power curve. It is defined as the curve on the Poincare sphere on which the received powers in some defined channel have the same value. They deal with the characteristics of the equi-power curves for various special cases. In addition, they show how the characteristic polarization states are generated by the equi-power curves. It is demonstrated that the characteristic polarization states can usually be regarded as the points of contact of the Poincare sphere and a conicoid representing a power-related quadratic form. This leads to a new method to introduce the characteristic polarization states.

In a co- or cross-polarized channel, the polarization states of the transmitting and receiving antennas are the same or orthogonal, and the corresponding target nulls (i.e., the co-pol nulls or x-pol nulls) are defined as the polarization states of the transmitting antenna such that the received power equals zero. However, no systematic studies have been carried out to solve the problem of the corresponding target nulls if the polarization states of the transmitting and receiving antennas are independent. In this paper, the target null theory is extended to the case of two independent polarization states. For two arbitrary independent symmetric scattering matrices, it is proved that there exists only one pair of polarization states such that both of the received powers equal zero. This polarization states' pair is called the co-null of the two targets, which can easily be obtained by solving an eigenvalue problem. Based on this concept and algebraic theory, the concept of the co-null space is introduced for the symmetric scattering matrix case, and many important results are presented, e.g., the relations between the co-null and the co-pol/x-pol nulls, the properties of the co-null space, and the relation between the co-null and target decomposition. Finally, the co-null for the asymmetric scattering matrix case is studied. The concepts of the mono-co-null space and the bi-co-null space are introduced, and the relations between both spaces are presented.

In order to improve the clearness of images captured at night, a new enhancement algorithm is proposed in this paper. Its characters are: (1) enhancing the dark image with contrast enhancement and histogram equalization in series, so both the local and global information can be used; (2) a contrast transforming function which is suited to deal with dark image is proposed; (3) it can be realized in real time. The new method shows good performance in dealing with night vision images.