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In the present complex electromagnetic environment, radar target detection is threatened by different kinds of interferences, especially mainlobe deceptive interference, which occupies the same energy distributions of targets spatially, meaning that targets and interferences cannot be discriminated. To make matters worse, the number of suppressible interferences is limited by the number of physical array elements, leading to the degradation of the suppression performance of traditional radar. In this work, we propose a frequency-increment-based interference suppression method for minimum redundancy frequency diverse array multiple-input multiple-output (MR-FDA-MIMO) radar, which effectively solves the aforementioned two problems. The interference suppression method consists of two steps: (i) in the sidelobe barrage interference suppression stage, the interference-plus-noise covariance matrix is reconstructed to overcome the influence of the true targets and mainlobe deceptive interference on the performance of the beamformer; (ii) in the mainlobe deceptive interference suppression stage, a nonadaptive beamforming method is employed to suppress mainlobe deceptive interference and overcome the impact of insufficient virtual samples on interference suppression performance. Additionally, we design a frequency-increment-based MR-FDA-MIMO radar, fully utilizing the advantages of the virtual array to enhance interference suppression performance and increase the number of interferences. Numerical experiments undertaken demonstrate the effectiveness of the algorithm under different scenarios. [ABSTRACT FROM AUTHOR]

To achieve high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) images, this paper focuses on resolving the problem of separating range-ambiguous echoes with the space–time coding (STC) array. At the modeling stage, the transmit elements and pulses of the STC array are configured with time delay and phase coding modulation, which introduces extra degrees of freedom (DOFs) in the transmit domain. To separate the echoes corresponding to different range-ambiguity regions, the equivalent transmit beamforming is performed in the two-dimensional space–frequency domain. Moreover, in order to compensate for the loss of range resolution during the beamforming progress, the frequency splicing method is proposed. At the analysis stage, the distributed target simulation is provided to demonstrate the effectiveness of obtaining HRWS SAR images in the STC radar. Additionally, the performance of resolving range ambiguity is compared with the traditional radar in terms of the range-ambiguity-to-signal ratio (RASR). [ABSTRACT FROM AUTHOR]

This paper proposes a cooperated range ambiguous clutter suppression method for moving target detection in the background of range-ambiguous clutter with a phased array (PA)–frequency diverse array (FDA) dual-mode radar. With the FDA mode, the range-ambiguous clutters are discriminated in the transmit spatial frequency domain, and thus the clutter covariance matrixes (CCMs) corresponding to unambiguous and ambiguous regions can be independently estimated. Therefore, the enhanced CCM can be reconstructed by using a linear combination of these distinguished CCMs from different range regions. With the PA mode, the enhanced CCM is applied, thus taking advantages of its high beampattern gain as well as alleviating the range ambiguous clutter suppression problem. Simulation results are presented to verify the effectiveness of the proposed method in range-ambiguous clutter scenarios. [ABSTRACT FROM AUTHOR]

Range-ambiguous clutter is an inevitable issue for airborne forward-looking array radars, especially with the high pulse repetition frequency (PRF). In this paper, a method to suppress the range-ambiguous clutter is proposed in an FDA-MIMO radar with a forward-looking planar array. Compressed sensing FDA technology is used to suppress the range-ambiguous clutter and the forward-looking non-uniformity short-range clutter of radar. Specifically, first, the range ambiguous clutter in different regions is separated by the characteristics of the planar array radar elevation dimension and FDA radar range coupling. Meanwhile, regarding the issue of the FDA radar main lobe moving between coherent pulses, a main lobe correction (MLC) algorithm proposes a solution for the issue, where the FDA radar cannot coherently accumulate signals in the case of non-full angle illumination. Finally, compressed sensing technology and elevation dimension filtering are utilized to suppress the range ambiguous clutter at the receiver, with the approach alleviating the range dependence of clutter in the observation region. A small number of clutter snapshots can obtain an approximately ideal clutter covariance matrix through compressed sensing sparse recovery. The method not only reduces the number of training samples, but also overcomes the problem of clutter non-uniformity in the forward-looking array. Therefore, the clutter suppression problems faced by the high repetition frequency airborne radar forward-looking array structure are solved. At the analysis stage, a comparison among the conventional MIMO and FDA methods is carried on by analyzing the improvement factor (IF) curves. Numerical results verify the effectiveness of the proposed method in range-ambiguous clutter suppression. [ABSTRACT FROM AUTHOR]

Target localization with unambiguous range and angle estimation of the signal-of-interest is a key task of a radar system, especially for wide region surveillance. In this article, we develop a novel dual-mode array radar scheme, namely phased array and frequency diverse array cooperated radar, to provide unambiguous range and angle estimation. Our approach combines the advantages of the PA in high transmit gain and that of the FDA in resolving range ambiguity. In general, two kinds of cooperative parameter estimation algorithms are derived to determine the range and angle parameters jointly. The proposed algorithms can be interpreted as different combination methods of the PA and FDA, including the uniform combination and weighted combination based on weighted least squares, where the weight is designed with user tunable angle parameters. Particularly, the design criterion for the tunable parameter is given. At the analysis stage, the Cramér–Rao bounds are studied to verify the performance of parameter estimation. Extensive simulation results demonstrate the effectiveness of parameter estimation with the dual-mode array radar. [ABSTRACT FROM AUTHOR]

We recently proposed a direction-of-arrival (DOA) estimation method for the coexistence of uncorrelated and coherent signals, which is related to the uniform linear antenna arrays with mutual coupling (MC) in the presence of unknown nonuniform noise. This technique, however, relies on the assumption of mixed signals and is not suitable for scenarios with purely coherent signals. This paper extends the mixed signal scenario to the purely coherent signals one on one hand, and on the other hand, allows us to directly adopt the ESPPRIT algorithm to perform DOA estimation in MC environments. To be specific, with the help of the least squares (LS) criterion, the nonuniform noise is first eliminated, and the traditional middle subarray scheme can then be employed to be immune to the influence of MC. Unlike the former operation, following a different line of attack the latter can make full use of the whole array and provide a larger array antenna aperture. Furthermore, we derive a new strategy to directly determine the DOAs without taking MC into account and performing spectral search. This brings substantial benefits to the DOA estimation accuracy and computational cost. The superiority of the proposed approaches is verified by numerical examples. [ABSTRACT FROM AUTHOR]

It is well known that classic direction-of-arrival (DOA) estimation techniques yield unsatisfactory performance with limited snapshots in unknown nonuniform noise. In this article, a sparse reconstruction (SR) DOA estimation method combining vectorized and reduced signal covariance matrix (SCM) is proposed. The new approach is based on an improved rank-one correlation model for denoising. With an extended virtual aperture array, this technique is able to provide a high-resolution DOA estimation with limited snapshots and is robust to nonuniform noise. In addition, two effective algorithms are also derived to obtain noise-free SCMs. One is to obtain diagonal entries of the noise-free SCM from the off-diagonal entries based on the fact that diagonal entries of SCM across the array are identical. The other is based on matrix completion (MC) theory which uses the low rank property of the SCM. Numerical results demonstrate that the new approaches yield superior performance than existing techniques in DOA estimations for multiple and compactly placed sources. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of mainlobe deceptive jammer suppression via joint transmit–receive design in a multiple-input multiple-output (MIMO) radar. At the design stage, a discrete element-pulse coding (DEPC) phase is implemented in both the slow-time pulses and transmit array antennas. After decoding and compensation of the delayed pulses, the true and false targets can be identified in the joint transmit–receive (JTR) spatial frequency domain. Furthermore, to eliminate the false targets, a data-dependent beamforming technique is adopted, where an optimization problem is constructed to maximize the output signal-to-interference-plus-noise ratio (SINR) by optimizing the DEPC coefficients and the weight vector of the receive filter in an alternating way. Specifically, two methods, including the Single Jammer Suppression (SJS)-Alternating Direction Method of Multipliers (ADMM) and Multiple Jammers Suppression (MJS)-Gradient Descent (GD) methods are developed for the cases of a single jammer and multiple jammers, respectively. A series of numerical results are utilized to verify the effectiveness of the proposed methods for mainlobe deceptive jammer suppression. • The DEPC-MIMO scheme is proposed by discretizing element pulse coding coefficients. • The true and false targets are distinguished in the transmit–receive spatial domain. • The false targets are suppressed by optimizing DEPC coefficients and weight vector. [ABSTRACT FROM AUTHOR]

Generally ground clutter has serious range dependence and range ambiguity in the forward-looking array airborne radar system under a high pulse repetition frequency (PRF) system. In this work, a range ambiguous clutter suppression method based on transmitting beam main lobe correction for the FDA MIMO radar is proposed. Firstly, this method uses the designed transmission pulse weights to compensate for the initial phases formed by the accumulation of frequency increments between pulses, so as to solve the problem that the main lobe high gain illuminated angle is inconsistent between the MIMO equivalent transmitting beampattern pulses synthesized at the receiving in the traditional FDA MIMO system. Secondly, by restoring and compensating received echoes, the influence of the main lobe correction weights of the transmitting signal is eliminated. Finally, through the characteristics of transmitter correction compensation to distinguish different range ambiguous clutters, the echoes from the range ambiguous areas can be discretely distributed in the power spectrum while preserving the signal echoes in the observation area. Thus range ambiguous clutter suppression is realized. The simulation results show that the proposed method can improve the clutter suppression performance compared with the MIMO and linear frequency increment FDA radar. [ABSTRACT FROM AUTHOR]

In this paper, a new method is proposed for direction-of-arrival (DOA) estimation of coherent signals with improved sparse representation in unknown spatially correlated Gaussian noise. To be specific, leveraging a symmetric uniform linear array, the entries of the signal covariance matrix is firstly recasted to eliminate the spatially correlated noise. Subsequently, it is shown that an equivalent source vector can be obtained by squaring any row of the noise-free signal covariance matrix, irrespective of the coherency between the signals. Finally, an improved sparse representation, which enhances signal sparsity via utilizing a designed weight vector, is derived to determine the DOAs of the signals. Numerical examples are provided to demonstrate its superiority of DOA estimation performance in low signal-to-noise ratio (SNR) environments. Besides, it is computationally efficient, which is critical for large array and/or real-time data processing systems. [ABSTRACT FROM AUTHOR]

The coexistence of clutter range ambiguity and range dependence in the airborne forward-looking radar may render the common space–time adaptive processing (STAP) ineffective. To cope with this challenge, a novel vertical element-pulse coding (EPC) radar is proposed in our paper. Note that both the elevation angles and coding factors determine the transmitted spatial frequency of the proposed vertical EPC radar. Therefore, the clutters corresponding to different ambiguous areas can be distinguished in the spatial frequency dimension based on this property. The element-pulse decoding and pre-STAP filtering are devised to separate and extract these clutters corresponding to different ambiguous areas. In the following, the clutter elimination is independently accomplished for each ambiguous area that has been separated. Consequently, the clutter range ambiguity as well as clutter range dependence are addressed simultaneously. The efficacy of the proposed approach is verified with simulation experiments. • A general signal model of vertical EPC array radar is established. • Clutter properties are analyzed in the vertical spatial frequency domain. • Range-ambiguous clutter is separated using element-pulse decoding and pre-filtering. • The design criteria of the element-pulse coding factor are derived. [ABSTRACT FROM AUTHOR]

• Formulate a PA-FDA dual-mode radar to combine the advantages of PA and FDA, which benefits the performance of multi-target detection. • Detect multiple targets sequentially with an iterative detection procedure, which is performed with the developed GLRT and CCBD-GLRT detectors. • Derive the analytical probabilities of detection as well as probabilities of false alarm for multi-target detection. In this paper, the multi-target detection for joint phased array (PA) and frequency diverse array (FDA) radars, referred to as PA-FDA dual-mode radar, is investigated by exploiting the high transmit power efficiency of PA as well as wide spatial coverage of FDA. The PA-FDA dual-mode radar is formulated in a signal-level fusion manner by combining the PA and the FDA, which enables us to exploit the joint information in the PA-FDA dual-mode radar. In the sequel, an iterative multi-target detection method is devised with two main steps: i) the generalized likelihood ratio test (GLRT) is performed, which provides the knowledge of target parameters, and ii) the conditional-cancelation-before-detection GLRT (CCBD-GLRT) detector is carried out, which detects other targets based on the previous detection knowledge. At the analysis stage, the optimal detector is provided for the signal-level fusion of PA and FDA radars. Moreover, the corresponding analytical expressions of the probabilities of detection as well as the probabilities of false alarm with respect to both the developed GLRT and CCBD-GLRT detectors are derived for multi-target detection. Simulation results demonstrate that the proposed multi-target detection method is effective and the PA-FDA dual-mode radar outperforms the PA and the FDA radars. [ABSTRACT FROM AUTHOR]

As a typical problem of sparse frame representations or compressed sensing with frames, direction of arrival (DOA) estimations, via sparse recovery methodologies such as nonlinear optimizations or various greedy algorithms, suffer seriously under severe noisy measurement without proper treatment. In this article, a crucial and effective correlation operation is outlined to mitigate the severe noise effect from the sparse representation point of view. A fast and super resolution method for DOA estimations is therefore established under very low signal-to-noise ratio, and by a sparse recovery technique of null space tuning in the context of compressed sensing and sparse representations. Improvement to the technique using the thresholding difference and alternating grid refinements are also outlined to estimate the source/target numbers dynamically and to improve the precision. Another characteristic is that only a very small number of snapshots are needed in comparison with other techniques. Simulation results demonstrate apparent advantages of the proposed technique over known approaches. The proposed method has the effectiveness at simultaneous high resolution, robustness to noise, and source number estimations. The algorithm is also computationally efficient, which is critical for large array systems and/or mission critical real-time applications. [ABSTRACT FROM AUTHOR]

In this paper, a fan-out device for multicore fiber (MCF) based on capillary bridge self-assembly fabrication method is proposed. The relation among the coupling errors (including transverse misalignment, longitudinal misalignment, and angle misalignment), refractive-index match and the coupling efficiency is analyzed in theory and simulation, and the transverse misalignment and angle misalignment are found to be the main factors affecting coupling efficiency. Then, a fan-out for a four-cores MCF is fabricated by using capillary-bridge force self-assembly of the UV adhesive, the coupling process of fan-out for four-cores MCF, consisting of transverse alignment, longitudinal alignment and tilt alignment, is demonstrated through the coupling of one core to the coupling of two cores, until the coupling efficiencies of all cores are the maximum. At last, the coupling loss of the packaged fan-out in the 1.55 μm spectral region is measured, and the actual coupling loss of all cores is less than 1.25 dB. [ABSTRACT FROM AUTHOR]

A robust method is addressed here for ground moving target detection and velocity estimation. This method is proposed by forming multilook Doppler beam sharpening (DBS) images with different illuminated angles which are divided by slow time segment in the azimuth direction. Compared with only a single looking angle, the detection probability with multiple looking angles can be improved under the same condition. For the clutter rejection procedure, we describe an approach which utilizes the fast time information and therefore has the robustness to image coregisrtration error. After clutter rejection and target detection, a new series of single-look and multilook velocity estimation approaches are developed in the presence of channel mismatch and image coregistration error. The robustness and practicability of the proposed algorithm are verified by processing the measured data of the experiment with three-aperture airborne radar. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we describe an approach for Doppler ambiguity resolution. This approach adopts the fact that unwrapped Doppler centroid is a linear function of range frequency for a given antenna squint angle. On this basis, an alternative azimuth compression method is addressed and performed in range frequency domain to accumulate target energy in the azimuth direction. The resulting trajectories behave as some straight lines with the average slope proportional to absolute Doppler centroid. This slope can be well estimated by image-contrast methods. Theoretical analysis and real measured SAR data show that the proposed approach works well in high- and relatively low-contrast scenes. [Copyright &y& Elsevier]

It is well known that the motion of a target induces range migration, especially for high-resolution synthetic aperture radar (SAR) systems. Ground moving target imaging necessitates the correction of the unknown range migration. To finely refocus a moving target, one must accurately obtain the motion parameters for compensating the target trajectory. However, in practice, these parameters usually cannot be precisely estimated. This paper proposes a new imaging approach for ground moving targets without a priori knowledge of their motion parameters. In the devised method, the azimuth compression function is constructed in range frequency domain, which can eliminate the coupling effect between range and azimuth. Theoretical analysis confirms that the methodology can precisely focus targets without interpolation procedure. The effectiveness of the proposed imaging technique is demonstrated by both simulated and real airborne SAR data. [ABSTRACT FROM AUTHOR]

Abstract: For the sake of the robustness to ground moving targets indication (GMTI) for synthetic aperture radar (SAR) in heterogeneous environment, a new approach using multi-channel and multi-look SAR images is presented. This approach has been shown to attain improved suppression of monostatic clutter over its counterpart (the multi-channel and single-look algorithm). In this approach, the joint data vector is utilized which has robustness to image coregistration and deleterious system errors in practice. Consequently, a series of radial velocity estimation approaches are presented. The estimation accuracy can be improved by the multi-look algorithms. Experimental airborne SAR data illustrates the effectiveness of the proposed GMTI schemes. It is shown that, compared with conventional single-look algorithms, the multi-look approach can obtain better clutter cancellation performance and more accurate velocity estimation. [Copyright &y& Elsevier]

The multi-target tracking filter under the Bayesian framework has strict requirements on the prior information of the target, such as detection probability density, clutter density, and target initial position information. This paper proposes a novel robust measurement-driven cardinality balance multi-target multi-Bernoulli filter (RMD-CBMeMBer) for solving the multiple targets tracking problem when the detection probability density is unknown, the background clutter density is unknown, and the target's prior position information is lacking. In RMD-CBMeMBer filtering, the target state is first extended, so that the extended target state includes detection probability, kernel state, and indicators of target and clutter. Secondly, the detection probability is modeled as a Beta distribution, and the clutter is modeled as a clutter generator that is independent of each other and obeys the Poisson distribution. Then, the detection probability, kernel state, and clutter density are jointly estimated through filtering. In addition, the correlation function (CF) is proposed for creating new Bernoulli component (BC) by using the measurement information at the previous moment. Numerical experiments have verified that the RMD-CBMeMBer filter can solve the multi-target tracking problem under the condition of unknown target detection probability, unknown background clutter density and inadequate prior position information of the target. It can effectively estimate the target detection probability and the clutter density. [ABSTRACT FROM AUTHOR]

It is hard to tackle the near-range clutter when range ambiguity exists in the non-sidelooking moving target detection (MTD) application. The angle-Doppler spectra of the far- and near-range clutter cannot be aligned simultaneously in this case due to the range dependence, which would significantly degrade the performance of the space–time adaptive processing (STAP) technology. To address this problem, an elevation element multifrequency subpulse coding (EMFSPC) array framework is proposed in this article. For each pulse duration, the main-lobe beam of the proposed framework can automatically sweep the full space by coding the subpulses and the transmitting elements, which steers the subpulses to different directions. Besides, these multiple subpulses occupy different range-frequency bands, and thus corresponding bandpass filters could be carefully designed to extract the unambiguous signals. After that, one can align the clutter spectra centers and employ the full-dimensional or dimension-reduced STAP techniques to achieve clutter cancellation. Furthermore, the simulation experiments are conducted to demonstrate the validity of the proposed EMFSPC system in near-range strong clutter suppression. [ABSTRACT FROM AUTHOR]

When range ambiguity and range dependence coexist in forward-looking ground moving target indication radar, the traditional space-time adaptive processing (STAP) approaches may fail to work. To cope with this issue, a range-ambiguous clutter suppression approach based on elevation time diverse array (TDA) radar system and azimuth phase coding technique is presented in this article. Due to the advantages of frequency scanning, the elevation TDA radar is able to cover all directions by transmitting a single time-shifted waveform. Therefore, a frequency bandpass filter is devised for each single range bin to achieve the mainlobe echo extraction procedure. In the sequel, the azimuth phase decoding and Doppler filtering are performed to deal with the residual sidelobe ambiguous energy. With two-dimensional filtering, one can extract the unambiguous signals for each range region independently. Then, the traditional clutter compensation and STAP methods are employed to suppress the ground clutter. Finally, the effectiveness of the proposed framework in resolving range ambiguity is verified by the numerical simulation experiments. [ABSTRACT FROM AUTHOR]

A range-ambiguous clutter suppression approach based on extended azimuth phase coding (EAPC) is proposed to handle the range ambiguity of the multiple-input multiple-output synthetic aperture radar (MIMO-SAR) system for ground moving target indication (GMTI) application. The echoes from different ambiguous range regions can be well separated in the transmit spatial frequency domain by properly designing the EAPC shift factor. In the sequel, a set of transmit filters are employed to extract the echoes of each ambiguous region independently. Then the azimuth deramp operation is applied to the extracted data to focus the target energy of the desired region while the residual target energy of other regions is still smeared due to the mismatched reference function. After this, the adaptive matched filtering algorithm is adopted to suppress the clutter and detect the moving target. Finally, numerical simulation experiments are presented to demonstrate that the developed framework can obtain good results for range-ambiguous clutter suppression and ground moving target detection. [ABSTRACT FROM AUTHOR]

Non-side-looking airborne radar provides important applications on account of its all-round multi-angle airspace coverage. However, it suffers clutter range dependence that makes the samples fail to satisfy the condition of being independent and identically distributed (IID), and it severely degrades traditional approaches to clutter suppression and target detection. In this paper, a novel reweighted extreme learning machine (ELM)-based clutter suppression and range compensation algorithm is proposed for non-side-looking airborne radar. The proposed method involves first designing the pre-processing stage, the special reweighted complex-valued activation function containing an unknown range compensation matrix, and two new objective outputs for constructing an initial reweighted ELM-based network with its training. Then, two other objective outputs, a new loss function, and a reverse feedback framework driven by the specifically designed objectives are proposed for the unknown range compensation matrix. Finally, aiming to estimate and reconstruct the unknown compensation matrix, special processes of the complex-valued structures and the theoretical derivations are designed and analyzed in detail. Consequently, with the updated and compensated samples, further processing including space–time adaptive processing (STAP) can be performed for clutter suppression and target detection. Compared with the classic relevant methods, the proposed algorithm achieves significantly superior performance with reasonable computation time. It provides an obviously higher detection probability and better improvement factor (IF). The simulation results verify that the proposed algorithm is effective and has many advantages. [ABSTRACT FROM AUTHOR]

It is known that the performance of classical direction-of-arrival (DOA) estimators may be deteriorated considerably in the presence of non-uniform noise and low signal-to-noise ratio (SNR). Focusing on this issue, based on the matrix completion theory, a sparse reconstruction algorithm combining second-order statistical vectors and weighted L1-norm is developed in this paper. In the proposed method, the elastic regularization factor is firstly introduced into the matrix completion model to reconstruct the signal covariance matrix as a noise-free covariance matrix. In what follows, the obtained multi-vector issue associated with the noise-free covariance matrix can be recast as a single vector one by exploiting matrix sum-average operation in the second-order statistical domain. With the constructed single vector, DOA estimation can be complemented by employing the sparse reconstruction weighted L1-norm (WL1) approach. Numerical simulation results show that the proposed algorithm has improved angle estimation accuracy and resolution effectively under low SNR and can suppress the effect of noise non-uniformity significantly. [ABSTRACT FROM AUTHOR]

Aimingat non-side-looking airborne radar, we propose a novel unsupervised affinity propagation (AP) clustering radar detection algorithm to suppress clutter and detect targets. The proposed method first uses selected power points as well as space-time adaptive processing (STAP) weight vector, and designs matrix-transformation-based weighted input data, with which the first unsupervised weighted AP clustering is proposed by means of their similarity matrix, responsibility values and availability values. Then, new reconstructed weighted power inputs are designed, and the second weighted AP clustering is proposed. Finally, with their cluster results, a detection-discriminant criterion is designed for the judgment of target detection, and simultaneously, the clutter is suppressed. Compared with the conventional and important STAP, ADC and JDL algorithms, and several SO-based, GO-based and OS-based CFAR algorithms, the proposed unsupervised algorithm achieves much higher probability of detection and provides distinctly superior target-detection performance. With reasonable computation time, it can better conquer the range dependence in characteristic of clutter and better process non-independent identically distributed (non-IID) samples of non-side-looking radar. Sufficient simulations are performed, and they demonstrate that the proposed unsupervised algorithm is preferable and advantageous. [ABSTRACT FROM AUTHOR]

A new approach is addressed for Doppler ambiguity resolution. This method exploits the fact that, in compressed range time and special scaled azimuth time domain, all the trajectories exhibit the same slope, which is proportional to the Doppler ambiguity number. The method can directly estimate the ambiguity number without a priori knowledge on the baseband Doppler centroid. It is confirmed that this approach is robust to the partially exposed strong targets in azimuth, ground slowly moving targets and the motion variation of platform in realistic scenarios. Results of real measured data processing show that this method works well in medium- to high-contrast scenes and can basically (not always) provide the right estimate in low-contrast scenes. [ABSTRACT FROM AUTHOR]

Clutter suppression is a key problem for airborne radar, and space-time adaptive processing (STAP) is a core technology for clutter suppression and moving target detection. However, in practical applications, the non-uniform time-varying environments including clutter range dependence for non-side-looking radar lead to the training samples being unable to satisfy the sample requirements of STAP that they should be independent identical distributed (IID) and that their number should be greater than twice the system's degree of freedom (DOF). The lack of sufficient IID training samples causes difficulty in the convergence of STAP and further results in a serious degeneration of performance. To overcome this problem, this paper proposes a novel autoencoder neural network for clutter suppression with a unique matrix designed to be decoded and encoded. The main challenges are improving the accuracy of the estimation of the clutter-plus-noise covariance matrix (CNCM) for STAP convergence, designing the form of the data input to the network, and making the network successfully explored to the improvement of CNCM. For these challenges, the main proposed solutions include designing a unique matrix with a certain dimension and a series of covariance data selections and matrix transformations. Consequently, the proposed method compresses and retains the characteristics of the covariances, and abandons the deviations caused by the non-uniformity and the deficiency of training samples. Specifically, the proposed method firstly develops a unique matrix whose dimension is less than half of the DOF, meanwhile, it is based on a processing of the selected clutter-plus-noise covariances. Then, an autoencoder neural network with l 2 regularization and the sparsity regularization is proposed for the unique matrix to be decoded and encoded. The training of the proposed autoencoder can be achieved by reducing the total loss function with the gradient descent iterations. Finally, an inverted processing for the autoencoder output is designed for the reconstruct ion of the clutter-plus-noise covariances. Simulation results are used to verify the effectiveness and advantages of the proposed method. It performs obviously superior clutter suppression for both side-looking and non-side-looking radars with strong clutter, and can deal with the insufficient and the non-uniform training samples. For these conditions, the proposed method provides the relatively narrowest and deepest IF notch. Furthermore, on average it improves the improvement factor (IF) by 10 dB more than the ADC, DW, JDL, and original STAP methods. [ABSTRACT FROM AUTHOR]

Simulated false targets cannot be easily discriminated and suppressed with traditional radar systems, especially for the mainbeam deceptive jamming. In this work, we explore the multiple-input multiple-output (MIMO) radar with frequency diverse array (FDA) as the transmit array and study its ability to suppress the deceptive jamming in joint transmit–receive domain. In FDA-MIMO radar, the steering vector is dependent on both range and angle. This implies that the false targets can be suppressed due to the mismatch in either range or angle. In particular, even the mainbeam deceptive jamming can be handled with the FDA-MIMO radar. The effectiveness of our proposed scheme in suppressing jamming under different scenarios is demonstrated via computer simulations. [ABSTRACT FROM AUTHOR]

Phased array is widely used in radar systems with its beam steering fixed in one direction for all ranges. Therefore, the range of a target cannot be determined within a single pulse when range ambiguity exists. In this paper, an unambiguous approach for joint range and angle estimation is devised for multiple-input multiple-output (MIMO) radar with frequency diverse array (FDA). Unlike the traditional phased array, FDA is capable of employing a small frequency increment across the array elements. Because of the frequency increment, the transmit steering vector of the FDA-MIMO radar is a function of both range and angle. As a result, the FDA-MIMO radar is able to utilize degrees-of-freedom in the range-angle domains to jointly determine the range and angle parameters of the target. In addition, the Cramér–Rao bounds for range and angle are derived, and the coupling between these two parameters is analyzed. Numerical results are presented to validate the effectiveness of the proposed approach. [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of detecting a signal in partially homogeneous and homogeneous environments. In partially homogeneous environments, i.e., both the test data and training data share the same noise covariance matrix structure up to an unknown scaling factor, a persymmetric adaptive coherence estimator (Per-ACE) detector is proposed. By exploiting the persymmetric structure of the covariance matrix, the Per-ACE can reduce training data requirements. Furthermore, the expressions for the probabilities of false alarm and detection are derived along with the distribution of the loss factor \beta. In homogeneous environments, a persymmetric adaptive matched filter (Per-AMF) detector has been presented. However, its probability of detection has not been obtained yet. Thus, we derive the expression for the probability of detection. For both the Per-ACE and Per-AMF, numerical results of these proposed expressions are confirmed with those of Monte Carlo trials. In addition, simulation results show that the proposed Per-ACE outperforms the conventional ACE in training-limited scenarios. [ABSTRACT FROM AUTHOR]

Highlights: [•] SCP is applied to measure the dimension of micro-cavity with high aspect ratio. [•] Location method based on OJFMs is proposed to improve the performance of SCP. [•] Improved SCP is experimentally tested by measuring a blind micro-hole. [ABSTRACT FROM AUTHOR]

The development is described of fiber Bragg grating (FBG) probes with single or four cores using spheres fixed onto optical fibers comprising Bragg gratings in their cores. Simulations and preliminary experiments were conducted to evaluate the performance of these FBG probes for dimensional metrology with micro parts of high aspect ratio. Simulation and experimental results indicate that a one-dimensional FBG probe with single core can be used to achieve an axial resolution of 100 nm at an aspect ratio of 15:1. A three-dimensional FBG probe with four cores can be used to achieve a theoretical axial resolution as high as that of a FBG probe with single core, with a capability of decoupling two-dimensional radial displacements with a resolution of 13 nm. [ABSTRACT FROM PUBLISHER]

Abstract: A direction-of-arrival (DOA) estimation method for coherent sources is presented for MIMO radar. It uses symmetrical array mode for both the transmit and receive arrays and reconstructs a special data matrix from the range-compressed receive data. In the reconstructed matrix, the signal term is a Toeplitz matrix with the rank only related to the DOAs of the signals and independent with their coherency. Taking the noise term into account, the average method of multiple pulses is utilized to obtain the signal and noise subspaces. And then the DOA can be resolved via the SVD-based ESPRIT algorithm. Furthermore, the presented method is also useful in spatial colored noise scenario for MIMO radar. Theoretical and numerical simulations show the effectiveness of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: The range dependence of clutter spectrum is influenced by both the orientation of the receiver array axis and bistatic geometry. In order to mitigate the range dependence, there is a strong requirement of finding out the relationship between the Doppler frequency and the look direction of targets relative to the receiver array. The paper presents the analytical expressions of the clutter Doppler frequency and range for four types of bistatic radar system (the side looking and forward looking arrays with the vertical and horizontal baselines). According to these expressions the clutter spectrum can be compensated by the Doppler warping method. Analysis and simulations show that the clutter Doppler frequency trajectories exhibit a group of lines with different slopes for the side looking array with the vertical baseline, of which the clutter spectrum has a small dispersion in the main beam; the side looking array with the horizontal baseline has a wide clutter spectrum region in the main beam; there is a non-linear relation between Doppler and range for the forward looking array with the vertical or horizontal baseline wherein the Doppler frequency spread mainly depends on the orientation of the receiver array axis relative to the aircraft velocity vector; Doppler warping can mitigate the range-dependence effect on space-time adaptive signal processing (STAP) in four types of bistatic radars. [Copyright &y& Elsevier]

Forward-looking correlated imaging plays an increasingly important role in modern radar imaging systems. It overcomes disadvantages of traditional side or squint synthetic aperture radar (SAR) which is dependent on specific relative motion between the radar and target scene. A new microwave forward-looking correlated 3-D imaging method based on random radiation field combined with sparse reconstruction is proposed in this article. Firstly, phased array radar (PAR) is adopted to form different and random antenna patterns. Then, combined with the compressed sensing (CS) theory, the target image can be recovered with very few samples which can break through Rayleigh resolution limitation. Furthermore, the proposed method can achieve resolution at least 5.5 times higher than real aperture imaging. To raise computation efficiency of sparse reconstruction, an improved quasi-Newton iteration method based on graphics processing unit (GPU) platform is developed. Meanwhile, a GPU-based (NVIDIA Tesla K40c) accelerated computing method can significantly reduce the processing time compared with the time given by a personal computer (PC). Both simulation and field experiment verify the validity of the proposed method. [ABSTRACT FROM AUTHOR]

The image of ground maneuvering targets may be defocused due to the Doppler ambiguity, high-order range migration (RM), and Doppler frequency migration (DFM) caused by the target's complex motions in a synthetic aperture radar (SAR) system. To settle these problems, an efficient algorithm based on discrete polynomial-phase transform (DPT), keystone transform (KT), and matched filtering processing (MFP) is presented for ground maneuvering target refocusing and motion parameter estimation in this paper. Firstly, the DPT is applied to transform the cubic phase into the quadratic phase and simultaneously eliminate the quadratic RM, cubic RM, and quadratic DFM. Furthermore, the Doppler ambiguity containing Doppler center blur and Doppler spectrum ambiguity is also dealt with effectively by introducing a very small fixed lay time after DPT operation. Then, the KT is performed to correct the linear RM. After that, the matched filtering function related to the target's equivalent third-order coefficient is constructed to compensate for the residual linear DFM in the range-time and slow-time domain. Lastly, a well-refocused image of the maneuvering target can be acquired, and the target's motion parameters can be estimated effectively. The proposed algorithm has high computational efficiency and possesses favorable refocusing performance and motion parameter estimation precision. Simulation and real data processing results prove the effectiveness of the presented algorithm. [ABSTRACT FROM AUTHOR]

The range ambiguity degrades the clutter suppression performance and incurs parameters estimation ambiguities in application of ground moving target indication (GMTI). Thus, in this paper, an adaptive clutter suppression approach based on extended azimuth phase coding (EAPC) for multiple-input multiple-output (MIMO) radar system is presented. The EAPC array encode the transmit pulses for each transmit element, which can introduce additional controllable degrees-of-freedom (DOFs) in coding dimension at the transmit elements. Then, with the DOFs in transmit, receive and pulse dimensions, the coding-angle-Doppler processing can be performed. The echoes from different range-ambiguous regions can be well separated in joint transmit-receive spatial frequency domain by properly designing the EAPC shift factor. In the sequel, the three-dimensional localization (3DL) technique is employed to suppress the range ambiguous clutters, which can evidently reduce the dimensions of the processor. Finally, the results of the experiments based on simulated data demonstrate that the proposed framework can effectively alleviate the space-time adaptive processing (STAP) performance degradation resulting from the range ambiguity. [ABSTRACT FROM AUTHOR]

• We quantitatively derive the relationship between spatial angle and frequency points. The transmit beampattern design problem can be transformed into the simple spectral shaping issue in coherent FDA. • We devise the piecewise LFM waveform to synthesize the desired beampattern. It enjoys the advantage of constant modulus, reduced computation complexity and low sidelobe level. • We derive the transmit beampattern response and the multi-dimensional ambiguity function to evaluate relevant properties in range and angle domain. In transmit beampattern design, it is desirable to maximize the power around locations of interest. Multiple-input multiple-output (MIMO) radar transmits orthogonal waveforms, which is hardly achievable in practical applications. Moreover, the constant modulus constraint and the computational burden limit the system performance. In this paper, we propose an effective piecewise linear frequency modulation (PLFM) waveform for coherent frequency diverse array (FDA) radar to synthesize the desired transmit beampattern. By deriving the relationship between the beam direction and the transmit signal frequency, we find that different sub-band of transmitted signal corresponds to different angular sector. With the relationship between frequency band and mainlobe of beampattern, the beampattern synthesis is transformed into the design of waveform spectrum which is realized by designing the PLFM waveform in this paper. Compared with the state-of-the-art technique, the proposed PLFM waveform in coherent FDA enjoys the advantage of the constant modulus, low sidelobe level and reduced computation complexity. In addition, the multi-dimensional ambiguity function and the transmit beampattern response are derived to examine the properties. Numerical results are presented to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Highlights • Propose novel robust beamformer against large errors using multiple uncertainty sets. • Use multiple uncertainty sets to describe large errors. • Propose two algorithms to solve the derived nonconvex problem. • The proposed method offers significant SINR improvement in existence of large errors. Abstract Many efforts have been recently devoted to adaptive beamformers using one uncertainty set to achieve robustness against steering vector mismatch. However, these robust adaptive beamformers based on worst-case performance optimization suffer from difficulty in selecting an appropriate size of the uncertainty set. Besides, their performances degrade dramatically if large steering vector mismatch occurs. This motivates us to develop a robust beamforming approach to handle the large uncertainty set problem. In this paper, we utilize multiple small uncertainty sets, instead of using a single large set, to cover the whole large uncertainty region. Two efficient algorithms, iterative semidefinite programming-based robust adaptive beamforming (ISDP-RAB) and iterative linearization-based robust beamforming (IL-RAB), are developed to solve the nonconvex problem. Simulation results indicate that the proposed method offers a significant performance improvement in case of large steering vector mismatch. [ABSTRACT FROM AUTHOR]