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Time series of SAR imagery combined with reference ground data can be suitable for producing forest inventories. Copernicus Sentinel-1 imagery is particularly interesting for forest mapping because of its free availability to data users; however, temporal dependencies within SAR time series that can potentially improve mapping accuracy are rarely explored. In this study, we introduce a novel semi-supervised Long Short-Term Memory (LSTM) model, CrsHelix-LSTM, and demonstrate its utility for predicting forest tree height using time series of Sentinel-1 images. The model brings three important modifications to the conventional LSTM model. Firstly, it uses a Helix-Elapse (HE) projection to capture the relationship between forest temporal patterns and Sentinel-1 time series, when time intervals between datatakes are irregular. A skip-link based LSTM block is introduced and a novel backbone network, Helix-LSTM, is proposed to retrieve temporal features at different receptive scales. Finally, a novel semisupervised strategy, Cross-Pseudo Regression, is employed to achieve better model performance when reference training data are limited. CrsHelix-LSTM model is demonstrated over a representative boreal forest site located in Central Finland. A time series of 96 Sentinel-1 images are used in the study. The developed model is compared with basic LSTM model, attention-based bidirectional LSTM and several other established regression approaches used in forest variable mapping, demonstrating consistent improvement of forest height prediction accuracy. At best, the achieved accuracy of forest height mapping was 28.3% relative root mean squared error (rRMSE) for pixel-level predictions and 18.0% rRMSE on stand level. We expect that the developed model can also be used for modeling relationships between other forest variables and satellite image time series. [ABSTRACT FROM AUTHOR]

Existing deep learning-based change detection methods in the field of polarimetric synthetic aperture radar (POLSAR) usually directly deal with intensity images. Methods can be easily transferred from optical image processing to synthetic aperture radar (SAR) image processing. However, the polarization information, which is critical in POLSAR data, has been discarded under normal situations. This paper introduces a novel joint change detection network based on similarity learning for coregistered complex-valued SAR data. A pseudo-siamese network takes both amplitude information and polarization information of POLSAR data as the input. The fusion of low-level, middle-level and high-level features enables the network to keep high-resolution and have strong representation ability during training procedures. Our novel sub-networks, which we term C2-Net and Intensity-Net, deal with the covariance matrix of complex SAR data and amplitude SAR data, respectively. The Intensity-Net works as a typical classification network and detects targets directly. The C2-Net attempts to find the relationship between two SAR data patches. An improved cosine similarity function is used to measure the similarity between two generated feature vectors in C2-Net. Output probability vectors of the two sub-networks are combined for final change detection. The two sub-networks are trained jointly and simultaneously. Control experiments show that proposed improvements are working. Experimental results on dual-polarization complex-valued SAR data from Sentinel-1 demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In practical radar systems, changes in the target aspect toward the radar will result in glint noise disturbances in the measurement data. The glint noise has heavy-tailed characteristics and cannot be perfectly modeled by the Gaussian distribution widely used in conventional tracking algorithms. In this article, we investigate the challenging problem of tracking a time-varying number of maneuvering targets in the context of glint noise with unknown statistics. By assuming a set of models for the possible motion modes of each single-target hypothesis and leveraging the multivariate Laplace distribution to model measurement noise, we propose a robust interacting multi-model multi-Bernoulli mixture filter based on the variational Bayesian method. Within this filter, the unknown noise statistics are adaptively learned while filtering and the predictive likelihood is approximately calculated by means of the variational lower bound. The effectiveness and superiority of our proposed filter is verified via computer simulations. [ABSTRACT FROM AUTHOR]

This article deals with the imaging problem of bistatic forward-looking synthetic aperture radar with a stationary transmitter (one-stationary BFSAR). In one-stationary BFSAR, due to the large forward-looking (or squint) angle, it causes more serious range cell migration and two-dimensional spatial variation. To solve this problem, an imaging algorithm based on squint minimization and modified nonlinear chirp scaling (NLCS) method is proposed. The squint minimization method can decrease the coupling between range and azimuth and correct the linear range cell migration. The modified NLCS can eliminate the variation of azimuth reference function more accurately than traditional NLCS algorithm, which improves the focus quality of marginal targets. Finally, simulation results confirm the efficiency of our proposed algorithm. [ABSTRACT FROM AUTHOR]

An iterative adaptive approach (IAA) based algorithm is presented to realise coherent integration for the random pulse repetition interval radar. The proposed method firstly removes the effect of Doppler ambiguity via phase compensation, and then accomplishes target focusing via conducting the azimuth IAA and range inverse fast Fourier transform. Theoretical analysis and numerical experiments have verified the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In a synthetic aperture radar (SAR) system, prolonging the integration time can improve the detection performance of the ground moving target. However, the complex motions of the target will induce severe range migration and Doppler frequency migration (DFM), which cause target energy defocused and deteriorate the imaging quality. In order to deal with the problem, a novel ground moving target focusing method is proposed. In this paper, the discrete polynomial-phase transform (DPT) is performed to reduce the phase order of the range compressed signal. Then, the residual range walk is removed by the constructed compensation function. Lastly, Lv's distribution (LVD) is used to motion parameter estimation and DFM correction. The proposed method is capable of focusing the targets both near and apart from the scene center and is computationally efficient. Several simulation results validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This letter proposes a novel structured compressive sensing (CS) imaging method in random stepped frequency (RSF) synthetic aperture radar (SAR) for better imaging performance in the presence of strong additive noise. In this method, the multiple measurement vectors (MMV) model is established in range-Doppler domain with range migration taken into consideration and a modified sparse recovery algorithm is derived to synthesize the one-dimension range profile. Traditional cross-range focusing algorithm is then performed to obtain a well-focused image. The proposed method makes full use of the coherent information among different bursts, which may further illuminate the influence of additive noise and decrease the number of transmitted pulses needed in one burst. The simulated results demonstrate the validity of the proposed method. [ABSTRACT FROM PUBLISHER]

A d -Approval election consists of a set C of candidates and a set V of votes, where each vote v can be presented as a set of d candidates. For a vote v ∈ V , the d -Approval voting protocol assigns one point to each candidate in v . The candidate getting the most points from all votes wins the election. An important aspect of studying election systems is the strategic behavior such as control and bribery problems. The control by deleting votes problem decides whether for a given election ( C , V ) , a specific candidate c , and an integer k , it is possible to delete at most k votes such that c wins the resulting election. In the control by adding votes setting, one has two sets V and U of votes and asks for a subset U ′ ⊆ U such that | U ′ | ≤ k and c becomes the winner in V ∪ U ′ . The bribery problem has the same input as the vote deleting control problem and asks for changing at most k votes to make c win. All three problems have been shown NP-hard. We initialize the study of the parameterized complexity of these problems and present a collection of tractability and intractability results. In particular, we derive a polynomial-size problem kernel for the standard parameterization of the control by deleting votes problem, the seemingly first non-trivial problem kernel for the control problem of elections. [ABSTRACT FROM AUTHOR]

Abstract: A high-resolution estimation of velocity and range profile (HREVRP) method is studied to yield both high-resolution radial velocity and range profile of slowly moving target for pulse linear frequency modulation (LFM) ultra-wideband (UWB) radar. Two-dimensional projection discrete Fourier transform (2-DPDFT) is utilized to estimate the velocity. The range profile is then obtained by compensating its motion and spread with this velocity estimate. In addition, a range of the velocities is derived based on velocity ambiguity and corresponding phase restriction. The proposed method does not require any initial velocity estimation and focusing matrices construction. Besides, it has superior performance of velocity estimation compared with conventional techniques, especially at low signal-to-noise ratio (SNR) levels. The simulations validate the effectiveness of the method. [Copyright &y& Elsevier]

In synthetic aperture radar (SAR) signal processing, compared with the raw data of level-0, level-1 SAR images are more readily accessible and available in larger quantities. However, an amount of level-1 images are affected by radio frequency interference (RFI), which typically originates from Linear Frequency Modulation (LFM) signals emitted by ground-based radars. Existing research on interference suppression in level-1 data has primarily focused on two methods: transforming SAR images into simulated echo data for interference suppression, or focusing interference in the frequency domain and applying notching filters to reduce interference energy. However, these methods overlook the effective utilization of the interference parameters or are confined to suppressing only one type of LFM interference at a time. In certain SAR images, multiple types of LFM interference manifest bright radiation artifacts that exhibit varying lengths along the range direction while remaining constant in the azimuth direction. It is necessary to suppress multiple LFM interference on SAR images when original echo data are unavailable. This article proposes a joint sparse recovery algorithm for interference suppression in the SAR image domain. In the SAR image domain, two-dimensional LFM interference typically exhibits differences in parameters such as frequency modulation rate and pulse width in the range direction, while maintaining consistency in the azimuth direction. Based on this observation, this article constructs a series of focusing operators for LFM interference in SAR images. These operators enable the sparse representation of dispersed LFM interference. Subsequently, an optimization model is developed that can effectively suppress multi-LFM interference and reduce image loss with the assistance of a regularization term in the image domain. Simulation experiments conducted in various scenarios validate the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

For pulse doppler (PD) radar, two tensor structure-based methods are proposed to improve the clutter suppression performance. One is tensor singular value decomposition (TSVD) and the other is tensor robust principal component analysis (TRPCA). Two algorithms both stack the range-Doppler matrices of multiple coherent processing intervals (CPI) into a tensor structure, which helps make the most of the temporal information across CPIs. TSVD utilizes the strong temporal correlation of ground clutter data to estimate the clutter subspace. Meanwhile, TRPCA exactly separates both low-rank clutter component and sparse target components from the time series range-Doppler tensor structure. Experimental results indicate that compared with conventional matrix-based schemes such as singular value decomposition (SVD) or singular value decomposition (RPCA), the proposed methods have significant advantages in keeping the target echo while suppressing the clutter. In addition, the computational complexity of each algorithm is analysed in detail. [ABSTRACT FROM AUTHOR]

A new method for three‐dimensional (3D) imaging via a narrowband bistatic multiple‐input–multiple‐output (MIMO) radar with two uniform rectangular arrays is presented. After matched filtering and phase compensation, the method utilises the 2D cross‐range images measured by the transmit and receive arrays to reconstruct the 3D image of the target. Compared with the 3D wideband monostatic MIMO imaging, lower hardware cost can be achieved since the narrowband waveforms are employed. In addition, the range alignment in wideband imaging can be avoided for the poor range resolution of the narrowband waveforms. [ABSTRACT FROM AUTHOR]

Two‐dimensional (2D) gold multiple signal classification (gold‐MUSIC) is proposed in order to reduce the complexity of MUSIC and make real‐time processing possible for joint range and angle estimation by multi‐carrier frequency multiple‐input multiple‐output radar. 2D gold‐MUSIC evaluates the MUSIC spectrum at coarse intervals, then determines peaks by narrowing the 2D search area iteratively based on a simultaneous gold‐section of range and angle to find accurate localisation of the peaks. The proposed method eliminates huge computation burden caused by a grid‐by‐grid spectral search of classical MUSIC and obtains remarkable resolution in range and angle with no dependence on array configurations. Numerical studies demonstrate the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a non-iterative blind calibration algorithm for gain-phase errors is proposed. A mixing matrix is first obtained from the received observation data through blind signal separation. The mixing matrix is the product of the gain-phase error matrix and the ideal array manifold matrix. Then, a spatial spectrum is constructed by using the estimated mixed matrix. The direction corresponding to the maximum point of the spectral function is proved to be the azimuth of a certain source. Therefore, after the direction-of-arrival (DOA) is obtained by a one-dimensional spectrum search, the active calibration method can be used to estimate the gain-phase errors. The proposed algorithm is not limited to the calibration for uniform linear array (ULA), but also applicable to a non-uniform linear array. Moreover, the estimation performance of the algorithm will not be affected by the magnitude of the gain errors. Some simulations are given to verify the effectiveness and performance of the algorithm. [ABSTRACT FROM AUTHOR]

• For the maneuvering target detection, the proposed method can remove range migration (RM) and Doppler frequency migration (DFM) in both fixed and random pulse repetition interval (PRI) radar systems. • The proposed method has a high search efficiency thanks to the adoption of sparse Fourier transform (SFT). • As a linear algorithm, the proposed method has a good performance in the scenario of multiple weak targets detection. The range migration (RM) and Doppler frequency migration (DFM) are the two major problems faced by radar maneuvering target detection. Unlike the existing algorithms which focus on the correction of RM and DFM in the fixed pulse repetition interval (PRI) system, this paper proposes a novel method which can further realize coherent integration in the random PRI system. The proposed method firstly conducts the multi-dimensional search for the maneuvering target's trajectory, then compensates DFM and realizes coherent integration via the nonuniform polynomial sparse Fourier transform (NUPSFT). As a linear algorithm, the proposed method can detect multiple weak targets without generating any cross term. Moreover, a high processing efficiency can be obtained thanks to the application of sparse Fourier transform (SFT). Both theoretical analysis and simulation results have validated the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

For parallel bistatic forward-looking synthetic aperture radar (SAR) imaging, the instantaneous slant range is a double-square-root expression due to the separate transmitter-receiver system form. The hyperbolic approximation provides a feasible solution to convert the dual square-root expression into a single-square-root expression. However, some high-order terms of the range Taylor expansion have not been considered during the slant range approximation procedure in existing methods, and therefore, inaccurate phase compensation occurs. To obtain a more accurate compensation result, an improved hyperbolic approximation range form with high-order terms is proposed. Then, a modified omega-K algorithm based on the new slant range form is adopted for parallel bistatic forward-looking SAR imaging. Several simulation results validate the effectiveness of the proposed imaging algorithm. [ABSTRACT FROM AUTHOR]

In recent years, super-resolution imaging techniques have been intensely introduced to enhance the azimuth resolution of real aperture scanning radar (RASR). However, there is a paucity of research on the subject of sea surface imaging with small incident angles for complex scenarios. This research endeavors to explore super-resolution imaging for sea surface monitoring, with a specific emphasis on grounded or shipborne platforms. To tackle the inescapable interference of sea clutter, it was segregated from the imaging objects and was modeled alongside I/Q channel noise within the maximum likelihood framework, thus mitigating clutter's impact. Simultaneously, for characterizing the non-stationary regions of the monitoring scene, we harnessed the Markov random field (MRF) model for its two-dimensional (2D) spatial representational capacity, augmented by a quadratic term to bolster outlier resilience. Subsequently, the maximum a posteriori (MAP) criterion was employed to unite the ML function with the statistical model regarding imaging scene. This hybrid model forms the core of our super-resolution methodology. Finally, a fast iterative threshold shrinkage method was applied to solve this objective function, yielding stable estimates of the monitored scene. Through the validation of simulation and real data experiments, the superiority of the proposed approach in recovering the monitoring scenes and clutter suppression has been verified. [ABSTRACT FROM AUTHOR]

This letter considers the coherent integration and detection for hypersonic target in the first‐order approximate (FOA) echo model. The proposed algorithm first accomplishes the modified pulse compression utilising the designed reference signal, then conducts the Keystone transform (KT) based coherent integration method to detect the target. Compared with the existing methods which model the received signal without considering the target's intrapulse motion, the proposed method has a lower modeling error and can maintain good noise robustness for weak hypersonic target detection. Experimental results are given to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Dealing with range ambiguity is a hot issue in SAR signal processing. One limitation of current algorithms is their poor robustness to noise and the energy of ambiguous targets, which can lead to performance degradation in complex environments. This letter proposes a new algorithm for range ambiguity suppression in SAR. The algorithm utilizes the orthogonality of the transmitted signal to separate the echo signals of the main region and the ambiguous area, and subsequently applies the alternating direction method of multipliers (ADMM) algorithm to perform joint sparse recovery on both areas. Experimental results show that the proposed method can effectively suppress range ambiguity, and it has a good ability to maintain the weak targets and the details of the main region's image. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm to enhance the range‐Doppler imaging performance for noise radar. Traditional matched filtering based method suffers from high range and Doppler sidelobes, which makes the weak targets overwhelmed by the sidelobes of strong targets or clutters in the range‐Doppler map. Sparse recovery based methods have been widely used to suppress such sidelobes, but most of them assume a repetitive transmit waveform, which cannot be applied to noise radar applications. In this letter, the range‐Doppler imaging problem for noise radar is formulated as a sparse recovery model and solved by a 2D generalized smoothed‐l0 algorithm. The proposed method can deal with the random waveforms varying among different pulses in noise radar. The robustness of the proposed method in strong noise and clutters is validated by simulation results. [ABSTRACT FROM AUTHOR]

Copernicus Sentinel-1 images are widely used for forest mapping and predicting forest growing stock volume (GSV) due to their accessibility. However, certain important aspects related to the use of Sentinel-1 time series have not been thoroughly explored in the literature. These include the impact of image time series length on prediction accuracy, the optimal feature selection approaches, and the best prediction methods. In this study, we conduct an in-depth exploration of the potential of long time series of Sentinel-1 SAR data to predict forest GSV and evaluate the temporal dynamics of the predictions using extensive reference data. Our boreal coniferous forests study site is located near the Hyytiälä forest station in central Finland and covers an area of 2500 km2 with nearly 17,000 stands. We considered several prediction approaches and fine-tuned them to predict GSV in various evaluation scenarios. Our analyses used 96 Sentinel-1 images acquired over three years. Different approaches for aggregating SAR images and choosing feature (predictor) variables were evaluated. Our results demonstrate a considerable decrease in the root mean squared errors (RMSEs) of GSV predictions as the number of images increases. While prediction accuracy using individual Sentinel-1 images varied from 85 to 91 m3/ha RMSE, prediction accuracy with combined images decreased to 75.6 m3/ha. Feature extraction and dimension reduction techniques facilitated the achievement of near-optimal prediction accuracy using only 8–10 images. Examined methods included radiometric contrast, mutual information, improved k-Nearest Neighbors, random forests selection, Lasso, and Wrapper approaches. Lasso was the most optimal, with RMSE reaching 77.1 m3/ha. Finally, we found that using assemblages of eight consecutive images resulted in the greatest accuracy in predicting GSV when initial acquisitions started between September and January. [ABSTRACT FROM AUTHOR]

To solve multitarget tracking (MTT) problems with coloured measurement noise, this study proposes a Poisson multi‐Bernoulli mixture filter with coloured measurement noise (PMBM‐CMN) and a robust PMBM‐CMN filter. By using the measurement differencing method and state augmentation approach, the proposed PMBM‐CMN filter transforms a state estimation problem with coloured measurement noise into a problem with white measurement noise. However, covariances of the true process and measurement noise in the proposed PMBM‐CMN filter are time‐varying and unknown, which may degrade the filtering performance. Therefore, a robust PMBM‐CMN filter is proposed for estimating the augmented state, including the kinematic state, the predicted state covariance, and the white measurement noise covariance. For linear Gaussian systems, the augmented state is modelled as a Gaussian inverse Wishart inverse Wishart (GIWIW) distribution. The variational Bayesian method is also employed to guarantee the conjugacy of the GIWIW density. Simulation results demonstrate the ability of the PMBM‐CMN filter to solve MTT problems with coloured measurement noise and show that the robust PMBM‐CMN filter based on the GIWIW model (GIWIW‐PMBM‐CMN) has the best overall performance in comparison with existing state‐of‐the‐art filters. [ABSTRACT FROM AUTHOR]

Since the start of the Sentinel-1 (S-1) mission, numerous cases of severe image degradation caused by radio frequency interference (RFI) have been reported, which puts forward an urgent need for RFI identification and mitigation. In this article, an automatic RFI identification method is proposed based on a Siamese-type deep convolutional neural network (Siam-CNN-RIM). The Siam-CNN-RIM can be served as a preprocessing step before RFI mitigation to identify whether an S-1 image is RFI-contaminated or not. Different from traditional RFI identification networks which only use a single image as input, an additional image in the repeat-pass time series is also fed into the input of Siam-CNN-RIM as a reference. Both the input images correspond to the same illuminated area and pass through the same convolutional layer followed by an energy function such that the different features caused by RFI can be extracted and the background terrain features can be ignored. This is beneficial for distinguishing the real RFI signatures and the similar terrain signatures that may cause false positives and thus improving the RFI identification performance. Experimental results show that the proposed method is robust in different scenarios and can achieve more than 97% RFI identification accuracy, even for the open-set task where the test scenarios are not included in the training set. [ABSTRACT FROM AUTHOR]

This letter deals with the range-Doppler image reconstruction problem in co-located multiple input multiple output (MIMO) noise radar, where the waveform diversity is applied not only among different transmit channels but also among different pulse repetition intervals (PRI). The transmit channel diversity and pulse diversity lead to high range-Doppler sidelobes, which would cause the weak target to be overwhelmed by strong target or clutters. In this letter, we propose a sparse recovery-based approach to deal with the range-Doppler image reconstruction problem in collocated MIMO noise radar, by exploiting the 2D-block sparsity of range-Doppler profiles to enhance the range-Doppler image reconstruction performance and its robustness to clutter. Simulation results show that the range-Doppler image reconstruction performance of the proposed method outperforms traditional methods, especially when noise and strong clutter exist. [ABSTRACT FROM AUTHOR]

Radio frequency interference (RFI) suppression is a hot topic in synthetic aperture radar (SAR) imaging. Mathematically, the RFI suppression problem can be considered as an underdetermined signal separation problem to extract the signal of interest (SOI) from the RFI contaminated raw data. The regularization-based method can exploit both the prior knowledge of RFI and SOI and, therefore, has the advantage of solving the underdetermined problem and preserving the information of SOI. Current regularization methods make use of the RFI prior well by exploiting low-rank representation (LRR) or sparse representation (SR), but the prior knowledge of SOI has not been sufficiently studied and used. In some literature, the sparsity of the raw data or range profile was exploited to formulate the regularization term, which we found to be inadequate in describing the SOI property. In this article, we explore the features of SAR images and propose an RFI suppression model with a combination of multiple image domain regularizations to preserve different types of targets. An efficient solution to the optimization problem is proposed based on the alternating direction multiplier method (ADMM). The proposed method can accurately recover both the sparse strong targets, and the nonsparse regions in the illuminated area and its performance is validated by measured data. [ABSTRACT FROM AUTHOR]

Abstract: A joint direction of departures (DODs) and direction of arrivals (DOAs) estimation for bistatic MIMO radar via both ESPRIT and SVD of cross-correlation matrix of the received data from two transmit subarrays is presented. The proposed method, with the influence of spatial colored noise eliminated, is effective for three- or more-transmitter configured system. The DOAs and DODs of targets can be solved in closed form and paired automatically. Moreover, the maximum number of targets that can be identified by using this method is also analyzed. Simulation results are presented to verify the effectiveness of the method. [Copyright &y& Elsevier]

The performance of synthetic aperture radar is vulnerable to radio frequency interference (RFI). In many situations, the RFI has a low-rank property, since the frequency bands occupied by RFI usually remain stable during a short slow time period. Therefore, low-rank representation (LRR)-based methods can be applied to separate RFI and signal of interest (SOI), by minimizing the rank of RFI components with a regularization constraint to protect SOI. However, traditional methods use the sparsity of the raw data or range profile to formulate the regularization term, which fails to describe the properties of SOI accurately. In addition to the sparse property of range profiles, this article explores the common patterns hidden in the range profiles and proposes two new LRR-based RFI suppression optimization models with a well-designed regularization term to describe such common sparsity to protect the SOI. Four methods are proposed to solve the optimization problems based on the alternating direction multiplier (ADM) method, which provides tradeoff between efficiency and accuracy. Compared with traditional LRR-based RFI suppression methods, the proposed methods make a more precise description of the features of SOI, therefore can better protect the information of SOI during the RFI suppression process and improves the imaging quality. The superior performance of the proposed method is validated by measured data in both sparse and nonsparse scenes. [ABSTRACT FROM AUTHOR]

In the scene of ground moving target detection, the target echo information is often corrupted by strong clutter. In this letter, a joint clutter suppression algorithm is proposed based on robust principal component analysis and total variation denoising for pulse Doppler radar. The novel method named total variation‐ robust principal component analysis replaces the l1‐norm in robust principal component analysis algorithm with a total variation seminorm. Total variation robust principal component analysis retains the clutter suppression ability of robust principal component analysis, and effectively separates the target from the noise in range‐Doppler domain. [ABSTRACT FROM AUTHOR]

Range ambiguity suppression is a significant problem in traditional pulse‐Doppler radar systems, especially when the pulse repetition frequency is set high to avoid Doppler ambiguity. One possible way to deal with such problem is to apply the noise radar system, by transmitting pulse‐agile waveforms and using a group of matched filters as the receiver to accumulate the power of unambiguous echoes while dispersing the range‐ambiguous ones. However, when strong targets or heavy clutters exist, the dispersed ambiguous power is still high, which leads to high range‐Doppler sidelobes and overwhelms the weak targets. This paper explores a new way to deal with the range‐ambiguity suppression problem in noise radar. The range‐ambiguity suppression as a sparse recovery problem is formulated, and then both the unambiguous and ambiguous targets (and/or clutters) can be simultaneously reconstructed. The proposed method can enhance the range‐ambiguity suppression performance and synthetic an ambiguity‐free range‐Doppler map with a low sidelobe level, which ensures the weak target recovery performance even in a cluttered environment. [ABSTRACT FROM AUTHOR]

This paper proposes an implementation of non-continuous piecewise nonlinear frequency modulation (N-PNLFM) signals in MIMO radar which are composed of a sequence of N-PNLFM subpulses. The N-PNLFM subpulse can be divided into three segments, the first and third segment are composed of linear frequency modulation (LFM) waveforms. The LFM waveforms in each N-PNLFM subpulse will be orthogonal in one sequence of our new N-PNLFM signal. A non-linear frequency modulation (NLFM) waveform is in the central component of each N-PNLFM subpulse, the bandwidths of each NLFM waveforms are distributed randomly. Each subpulse can be controlled by different variable parameters in both auto-correlation and cross-correlation functions. In order to suppress the high sidelobes in the new N-PNLFM signal and increase the diversity of signals, each subpulse duration is also distributed randomly. Our proposed PNLFM signals are optimized by applying a particle swarm optimization (PSO) algorithm. Comparing with other PNLFM signals, numerous simulations illustrate that our implementation achieves better performance. [ABSTRACT FROM AUTHOR]

High sidelobes exist in normal multiple-input multiple-output (MIMO) radar waveforms, which utilize linear frequency modulation (LFM) or non-linear frequency modulation (NLFM) waveforms with the same subcarrier durations. Since the waveforms have the same expression, and each segment of the subcarriers can be only controlled by a few parameters to make them distinctive. This will cause comparatively high auto-correlation sidelobes in each zone of subcarriers. In order to suppress the sidelobes, new piecewise NLFM waveforms have been proposed in this paper. These novel MIMO radar waveforms are composed of three different kinds of subcarriers with totally different mathematical expressions. This is verified by the fact that the cross-correlation sidelobes between different subcarriers are much lower than the auto-correlation of each subcarrier. In addition, the number of MIMO radar waveforms has been improved significantly. As different subcarriers have their own controllable parameters, a genetic algorithm will be applied to generate the novel MIMO radar waveforms. Numerical results demonstrate the effectiveness that the auto-correlation sidelobes and the cross-correlation sidelobes of our proposed waveforms are reduced. [ABSTRACT FROM AUTHOR]

Multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) waveform diversity design has drawn extensive attention since it has a great potential in remote sensing. The orthogonal frequency-division multiplexing (OFDM) waveform designing becomes one of the most significant tasks in MIMO SAR system. In this article, a novel piecewise nonlinear frequency modulation (NLFM) waveform is proposed. The OFDM waveform is composed of the piecewise NLFM waveforms as its subpulses, and each subpulse has a different subcarrier beginning frequency. The subpulse is centre symmetrical and can be divided into three segments. The first and third segments of the subpulse are linear frequency modulation waveforms, and the second segment of the subpulse is an NLFM waveform. A few parameters are used to control the duration and bandwidth of each segment in the subpulse. Genetic algorithm is used for the OFDM waveforms to optimize the sidelobes of autocorrelation and cross-correlation function. Compared with other OFDM waveform design methods, the proposed method is more flexible and has higher degrees of freedom and thus can yield waveforms with a better autocorrelation and cross-correlation properties. The performances of ambiguity function and cross ambiguity function are analysed to have low time delay and Doppler correlation peaks. Simulation results demonstrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

In general, ionosphere phase contamination can expand the Bragg peak of sea clutter and the echo signal from a target in sky‐wave over‐the‐horizon radar (OTHR). By exploiting the subspace algorithm, an effective approach of ionosphere phase decontamination is proposed to solve the above‐mentioned problems. In this new scheme, the stronger one of two spreading Bragg peaks is extracted by an adaptive sliding‐window bandpass filter, and the instantaneous phase of the spreading Bragg peak is obtained by the subspace algorithm. Both the theoretical analysis and the experimental results of the real OTHR data have confirmed the feasibility of this new method. [ABSTRACT FROM AUTHOR]

Wide‐band interference (WBI) can severely degrade synthetic aperture radar image quality. This Letter proposes a new WBI suppression technique using time‐varying filtering method and sparse recovery. In this method, a time‐varying filter is firstly set up in the short‐time Fourier transform domain as a pre‐processing step to suppress the major power of WBI, and then, an adaptive dictionary‐based sparse recovery algorithm is developed to recover the useful target echo loss resulting from time‐varying filtering. Finally, traditional SAR imaging algorithms can be performed to obtain a well‐focused image. Simulation results demonstrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: A new multi-SVD based subspace estimation algorithm is proposed to improve the direction of departure (DOD) and direction of arrival (DOA) estimation accuracy for bistatic multiple-input multiple-output (MIMO) radar. First, the matched filter output is transformed to a 3-order tensor. Then the signal subspace is estimated by multi-SVD of the matrix unfoldings of this tensor or its covariance tensor. Since the multidimensional structure is utilized, the estimated signal subspace of our method has better accuracy than that of the traditional SVD/EVD method. Combined with the classical subspace based methods, such as MUSIC and ESPRIT, the proposed approach improves the angle estimation performance compared with the existing ones. Simulation results confirm the effectiveness of our method. [Copyright &y& Elsevier]

Super-resolution technology is considered as an efficient approach to promote the image quality of forward-looking imaging radar. However, super-resolution technology is inherently an ill-conditioned issue, whose solution is quite susceptible to noise. Bayesian method can efficiently alleviate this issue through utilizing prior knowledge of the imaging process, in which the scene prior information plays a pretty significant role in ensuring the imaging accuracy. In this paper, we proposed a novel Bayesian super-resolution method on the basis of Markov random field (MRF) model. Compared with the traditional super-resolution method which is focused on one-dimensional (1-D) echo processing, the MRF model adopted in this study strives to exploit the two-dimensional (2-D) prior information of the scene. By using the MRF model, the 2-D spatial structural characteristics of the imaging scene can be well described and utilized by the nth-order neighborhood system. Then, the imaging objective function can be constructed through the maximum a posterior (MAP) framework. Finally, an accelerated iterative threshold/shrinkage method is utilized to cope with the objective function. Validation experiments using both synthetic echo and measured data are designed, and results demonstrate that the new MAP-MRF method exceeds other benchmarking approaches in terms of artifacts suppression and contour recovery. [ABSTRACT FROM AUTHOR]