Recently, RGB-T tracking has received increasing attention due to its robustness. However, existing RGB-T trackers mainly use cross-attention for modal feature interaction, limiting the utilization of complementary information. In addition, these trackers employ fixed dominant-auxiliary paradigms for feature fusion, ignoring modal quality fluctuations. To address these issues, we propose FMTrack, an effective framework for fully capturing complementary information. FMTrack consists of two key components, a frequency-aware interaction network (FIN) and a multi-expert fusion module (MEFM). To emphasize the valuable information in each modality, FIN utilizes frequency masks to perform high-pass and low-pass filtering on RGB and TIR data. FIN explicitly establishes cross-modal interactions via frequency domain learning, which facilitates the sharing of complementary information. Besides, MEFM extracts diverse features via the differentiated expert network and then adjusts feature combinations according to modal reliability, achieving deep understanding and flexible fusion of multimodal data. With FIN and MEFM, FMTrack makes full use of the advantageous information of each modality to highlight target representations, thus improving performance in complex scenes. Extensive experiments on four popular RGBT tracking datasets (LasHeR, VTUAV, RGBT234, and RGBT210) show that our FMTrack achieves leading performance. The code is available at https://github.com/xyl-507/FMTrack.

The introduction of natural language for vision-language (VL) tracking has been proven to improve performance. However, natural language remains under-explored in existing aerial trackers. Moreover, existing VL trackers ignore the misalignment of language with dynamic target states, which is prominent in complex UAV scenarios. In this work, we present AVLTrack, a flexible framework for aerial vision-language tracking. It consists of three key components, a dynamic sparse learning (DSL) module, an efficient Transformer backbone, and a multi-level language perception (MLP) strategy. First, DSL sparsely connects language and images via dynamic sparse attention, providing accurate multi-modal prompts. To adapt to target state variations, the sparsity in DSL is dynamically adjusted based on semantic information, flexibly highlighting target-specific tokens. Next, the Transformer backbone follows highly parallelized one-stream architectures, allowing efficient multi-modal feature extraction and interaction. Finally, MLP enables the iterative interaction of language and visual information, aiming to utilize language priori to guide the generation of discriminative visual features. Moreover, we construct the DTB70-NLP dataset to facilitate UAV vision-language tracking. Extensive experiments on WebUAV-3M and DTB70-NLP demonstrate the leading performance of AVLTrack compared to existing outstanding trackers while maintaining a high running speed of 80.5 FPS. The dataset and codes are available athttps://github.com/xyl-507/AVLTrack.

Waveform decoupling is usually considered a more technical challenge for fully exploiting the potential benefits provided by multiple-input–multiple-output (MIMO) synthetic aperture radar (SAR) structure. Spotlighted as a promising solution to this challenge, the well-known orthogonal-waveform beamforming scheme has become increasingly popular. However, in some cases, the performance of digital beamforming (DBF) involved in this scheme may be significantly degraded due to the nonlinear array configuration under stringent space constraints. Up until now, relatively little research on robust DBF on receive in elevation has been presented for a nonlinear array configuration. To alleviate this, we here propose a least absolute shrinkage and selection operator (LASSO) regression-based DBF technique for the improved segmented phase coding (SPC) decoupling scheme. First, a generalized steering vector formation model based on a 3-D geometric vector is provided for the subsequent DBF operation. Given that, due to the nonlinear array configuration, the calculated steering vector exhibits space-varying characteristics within the azimuth pulse extension of the illumination beam, steering vector constraints for the desired signal and interferences are subsequently designed to allow for such variation within the azimuth footprint. Furthermore, the beamforming problem is addressed by generalized LASSO regression to approach the goal that providing a distortionless response and deep nulls for each desired signal and interference component within the azimuth pulse extension. Finally, we assess the feasibility and performance of the proposed LASSO regression-based DBF technique for waveform decoupling with the use of numerical simulations.

The spaceborne multistatic synthetic aperture radar (SAR) system offers a flexible baseline that provides more observation angles and higher interferometry accuracy. However, any phase deviation among the independent oscillators in the spaceborne multistatic SAR system can cause a residual modulation of the echoes. Therefore, accurate phase synchronization is crucial for the system. The pulsed alternate synchronization scheme accurately extracts phase errors between different platforms, as verified in the TanDEM-X mission. Furthermore, an advanced noninterrupted pulsed alternate scheme uses the time interval of transmitting sequence to realize phase synchronization without interrupting the normal operation of the radar, which is verified in the LuTan-1 mission. However, with the increasing number of spaceborne multistatic SAR platforms, the time interval of the system may not be sufficient to support noninterrupted phase synchronization. To this end, this article proposes a novel phase synchronization method to improve the efficiency of phase synchronization for spaceborne multistatic SAR systems. First, a model for phase synchronization is built based on the pulsed alternate synchronization scheme, and the constraint between phase synchronization accuracy and waveform properties is analyzed in detail. Second, a quasi-orthogonal waveform optimization method, which can realize the rapid generation of phase synchronization waveform with better correlation properties, is introduced to improve the accuracy of phase synchronization. Third, to further reduce cross correlation energy between waveforms, we introduce generalized short-term shift-orthogonal (STSO) waveforms for phase synchronization. This waveform can achieve local orthogonality with a known baseline, improving the accuracy of phase synchronization greatly. Finally, the proposed method is verified through detailed simulations and ground experiments.

Object tracking is a basic task of the uncrewed aerial vehicle (UAV)-based intelligent visual perception system. The presence of similar targets and complex backgrounds in the airborne perspective poses significant challenges to aerial trackers. However, existing target-aware or distractor-aware trackers fail to capture discriminative cues from both target and background information in a balanced manner, resulting in limited improvement. To address these issues, this paper proposes a global agent-based Target-Distractor Aware Tracker (TDAT) to enhance the discrimination of the target. TDAT comprises two effective modules: a global agent generator and an interactor. First, the generator aggregates the target and background regions into representative agents and then performs self-attention on these agents to explicitly model the global relationships between the target and backgrounds. Next, the interactor realizes the bidirectional information interaction between global agents and local regions via self-attention. Based on the global dependencies encoded in global agents, the interactor extracts target-oriented features and enhances the understanding of the target. TDAT embedded with target-distractor awareness effectively widens the gap between target and background distractors. Experimental results on multiple UAV benchmarks show that TDAT achieves outstanding performance with a speed of 34.5 frames/s. The code is available at https://github.com/xyl-507/TDAT

Real-time echo restoration from signals containing range ambiguities is a technique challenge for high pulse repetition frequency synthetic aperture radar (SAR). In this letter, to ensure real-time processing, a novel azimuth phase coding scheme is utilized to realize the processing of raw data in groups, which is conceived for a conventional SAR system. Meanwhile, an advanced fast algorithm is proposed to further decrease the computational complexity of the restoration processes. The proposed scheme is validated by the simulated point-like and distributed targets SAR data. The quantitative analysis results show that the ambiguous signal can be at least suppressed by 40 dB and the image average range ambiguity separation error is less than -60 dB. Finally, compared with some conventional methods in computational complexity and memory cost, the results illustrate that the processing efficiency has been significantly improved and the memory resource occupation has been significantly reduced. Particularly, the proposed fast algorithm improves the computational efficiency by about 80 times.

The convolution modulation jamming method based on noise template has received widespread attention and application due to its advantages such as having the ability to obtain synthetic aperture radar (SAR) system processing gain. However, there are few studies on the evaluation analysis and optimization design of noise templates. In this letter, an optimal noise template generation method based on weighted combination is proposed and the multi-scale structural similarity (MS-SSIM) index is used to evaluate the jamming effect of the obtained optimal noise template. The experimental results show that the optimal combination noise template can achieve better jamming effect than traditional noise templates under the same jamming-to-signal ratio (JSR), which verifies the effectiveness of the proposed method.

Real-world graphs are often dynamic and evolve over time. It is crucial for storing and querying a graph's evolution in graph databases. However, existing works either suffer from high storage overhead or lack efficient temporal query support, or both. In this paper, we propose AeonG, a new graph database with built-in temporal support. AeonG is based on a novel temporal graph model. To fit this model, we design a storage engine and a query engine. Our storage engine is hybrid, with one current storage to manage the most recent versions of graph objects, and another historical storage to manage the previous versions of graph objects. This separation makes the performance degradation of querying the most recent graph object versions as slight as possible. To reduce the historical storage overhead, we propose a novel anchor+delta strategy, in which we periodically create a complete version (namely anchor) of a graph object, and maintain every change (namely delta) between two adjacent anchors of the same object. To boost temporal query processing, we propose an anchor-based version retrieval technique in the query engine to skip unnecessary historical version traversals. Extensive experiments are conducted on both real and synthetic datasets. The results show that AeonG achieves up to 5.73× lower storage consumption and 2.57× lower temporal query latency against state-of-the-art approaches, while introducing only 9.74% performance degradation for supporting temporal features.

Aerial tracking has received growing attention due to its broad practical applications. However, single-view aerial trackers are still limited by challenges such as severe appearance variations and occlusions. Existing multi-view trackers utilize cross-drone information to address these issues but struggle to overcome heterogenous differences. In this paper, we propose a novel Transformer-based consistent representation mining (CRM) module to capture invariant target information and suppress the heterogenous differences in cross-drone information. First, CRM divides the heterogenous input into regions and measures semantic relevance by modeling the relations between these regions. Then reliable target regions are roughly localized by selecting the top k most relevant regions. Next, the global perception is performed on these reliable regions via multi-head sparse self-attention, further enhancing the understanding of the target and suppressing background regions. In particular, CRM, as a plug-and-play module, can be flexibly embedded into different tracking frameworks (CRM-Siam and CRM-DiMP). Besides, the multi-view correction strategy is designed to ensure timely correction of multi-view information and full utilization of its own information. Extensive experiments on the multi-drone dataset, MDOT, demonstrate that CRM-assisted trackers effectively improve the accuracy and robustness of the multi-drone tracking system, outperforming other outstanding trackers. The code and models are available athttps://github.com/xyl-507/CRM.

The separation of multiple transmit waveforms with time and frequency synchronization constitutes a considerable challenge for multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) systems. It is well-known that aliased signal returns may be separable by digital beamforming (DBF) on receive in elevation. However, the current orthogonal-waveform beamforming schemes significantly increase the hardware complexity. Moreover, the direction of arrival (DOA) mismatch issue caused by topographical variations significantly increases the complexity of the DBF process. To alleviate these issues, we here introduce a multiple-subpulse separation and weighting synthesis (MSS-WS) echo separation framework, which is formed using segmented phase coding (SPC) waveforms. The proposed MSS-WS scheme can halve the number of interferences from far arrival angles, allowing for a reduction of the system complexity. In addition, constrained/generalized least absolute shrinkage and selection operator (LASSO) regression is exploited to form the beamformer with relatively high robustness in terms of dealing with the presence of topographical variations. The so-called LASSO-based dynamic beam response (LASSO-DBR) technique introduced here contains two parts: the source localization and the beamforming based on the designed constraint matrices. In this respect, the proposed LASSO-DBR beamformer can produce a distortionless response to the desired signal and still yield wide nulls for the unwanted interferences. Using numerical simulations, we illustrate the feasibility and performance of the proposed MSS-WS framework using the LASSO-DBR beamforming technique.

Multi-input and multi-output (MIMO) radar has drawn much attention in synthetic aperture radar (SAR) due to the possession of more degrees of freedom (DOFs). However, there are some repeated equivalent phase centers (EPCs) caused by the same wave path have no contribution to the improvement of DOFs. To this end, a novel interpulse phase coding and multi-carrier (IPCMC) transmission scheme is investigated to restored repeated EPCs. Furthermore, an advanced range ambiguity separation method is proposed based on the increased efficient EPCs. Finally, distributed targets simulation experiments are performed and the experiment results illustrate that the range ambiguity suppression performance is significantly improved due to the restored EPCs.

To address the echo separation issue involved in multiple-input multiple-output (MIMO) synthetic aperture radar (SAR), the elevation beamforming solution has become increasingly popular and been widely investigated. However, the current elevation digital beamforming (DBF) schemes usually require high hardware complexity, which is not allowed for practical MIMO-SAR systems. To alleviate this problem and achieve a low-cost MIMO-SAR system, we here detail an improved two-stage echo separation scheme, i.e., subband match filtering and DBF (SMF-DBF). First, the subband match filtering enables the number of interference components to be halved. Afterwards, the remained interferences from far arrival angles will be suppressed by DBF techniques. The two-stage processing can considerably simplify the array configuration and reduce the system complexity. Numerical simulations have demonstrated the feasibility and potential of the proposed method for channel-limited MIMO-SAR systems.

The echo separation problem in the application of multiple-input multiple-output (MIMO) concept on synthetic aperture radar (SAR) systems is considered as a more technical challenge. It has been shown that the separation of aliased signal returns can be achieved by the state-of-the-art elevation beamforming techniques, e.g., the well-known short-term shift-orthogonal (STSO) scheme. However, the directions of arrival (DOAs) of the signal segments are usually inaccurate due to the unknown topography variation, especially for airborne MIMO-SAR systems. The DOA mismatch can seriously deteriorate the digital beamforming (DBF) performance of STSO scheme. To this end, a covariance-matrix-reconstruction-based (CMRB) robust beamforming technique is introduced in our paper to alleviate the effect of DOA mismatch on echo separation. Extensive simulations that in comparison with the current DBF methods have been carried out to prove the effectiveness and prospect of the proposed CMRB DBF technique for airborne MIMO-SAR systems.

Waveform design plays a critical role in ruling the performance of a pulse compression radar system, and keeps being a hotpot for several decades. Unfortunately, some coded waveforms being widely employed in recent years nearly have their limitations. The idealistic phase code waveform has a high spectral sidelobe brought by the instantaneous phase change. The polyphase-coded FM (PCFM) waveform can provide a continuous phase function, but the frequency template error (FTE) metric is indispensable for it to control the spectrum, thereby inducing high design complexity. The nonlinear frequency modulated (NLFM) waveform has a controlled spectral content, while its coding structure is non-parameterized. To this end, we develop a novel parameterized frequency modulated (PFM) waveform and a constant envelope. Simulation and real experimental results verify the superior performance of the proposed waveform in term of autocorrelation sidelobes and energy ratio within bandwidth compared to the phase code and PCFM waveforms.

Multi-input and multi-output (MIMO) radar is an advanced radar system, which grows into a promising candidate for the future synthetic aperture radar (SAR) because the MIMO radar can provide more degrees of freedom (DOFs). Monostatic MIMO SARs (where transceiver channels share the same antenna array) will produce lots of repeated equivalent phase centers (EPCs) due to the same wave path, and these repeated EPCs actually have no improvement to the DOFs of the radar system, resulting in a tremendous waste of the radar resources. To this end, this paper devises a novel radar framework, which is referred to as MIMO SAR with interpulse phase coding and multi-carrier (IPCMC). The IPCMC scheme employs multi-carrier and well-designed phase codes in transmitting channels, which has the advantage of increasing the DOFs in elevation. Concretely, by introducing the carrier information into the transmission-receiving spatial frequency domain, non-overlapped spatial frequency difference curves can be obtained to further increase the DOFs in elevation. Furthermore, an advanced range ambiguity separation method based on the proposed IPCMC MIMO SAR is presented. Compared with SAR systems with different numbers of DOFs, the proposed IPCMC MIMO scheme can precisely separate more ambiguous regions, due to the increased DOFs. Finally, detailed simulation experiments are carried out to verify the efficacy of the proposed IPCMC MIMO SAR.

Barrage jamming technique with controllable jamming coverage against synthetic aperture radar (SAR) systems is of great importance in electronic countermeasures. However, it is still a difficulty for the jammer to accurately impose controllable two-dimensional (2-D) local jamming on the regions of interest (ROIs). In this respect, a new parameterized and large-dynamic-range precise controllable (PLDR-PC) jamming method has been proposed in this paper to assist in solving such problems. Based on the SAR imaging properties of linear frequency modulation (LFM) case, the range and azimuth modulation factors have been well designed to generate large dynamic controllable coverage of jamming signals with high 2-D processing gain. In such a context, the PLDR-PC technique can provide the optimal power allocation and considerably reduce the jamming power while still ensuring the satisfactory performance. The proposed PLDR-PC technique can improve the jamming efficiency and considerably reduce the exposure probability of the jammer. Moreover, to improve the barrage jamming performance, the parameter estimation error model is also established to determine the simple yet valid jamming strategy in practical implementations. Finally, extensive numerical simulations in comparison with the current jamming methods have been carried out to demonstrate the effectiveness and prospect of the PLDR-PC technique against airborne/spaceborne SAR systems.

The problem of echo separation using digital beamforming (DBF) on receive for multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) is of notable importance to allow for practical systems. Regrettably, current DBF-MIMO-SAR schemes, such as the short-term shift-orthogonal (STSO) scheme, are computationally cumbersome, increasing the required hardware complexity. To alleviate this problem, we here propose an improved echo separation solution for realizing a low-cost MIMO-SAR system. We detail a generic waveform design scheme as well as optimized monostatic radar waveforms (e.g., nonlinear frequency modulation (NLFM) signal) showing how these can be directly adopted in the proposed scheme to improve the imaging performance. The proposed scheme enables the number of the interference segments generated by unmatched waveforms to be halved by the use of the fast time spectrum preprocessing and segment synthesis, dramatically simplifying the array configuration and reduces the system complexity. By exploiting inter-pulse phase coding techniques, the proposed method can provide a reconfigurable waveform transmitting scheme, allowing the system resources in range frequency, elevation space, and Doppler domains to be jointly exploited for the separation of aliased signal returns. The proposed scheme is evaluated using extensive numerical and measured data sets, demonstrating the feasibility and potential of the proposed method for resource-limited spaceborne/airborne MIMO-SAR systems.

Aerial object tracking has recently shown great potential in the field of remote sensing. However, small objects with limited feature information pose a huge challenge to aerial trackers. Despite significant improvements, most trackers still struggle to capture enough discriminative features and to overcome background disturbances. In this work, we propose an efficient aerial tracker (SmallTrack) based on the Siamese network to improve the discrimination of small objects. It consists of two effective modules, namely Wavelet Pooling Layer (WPL) and Graph Enhanced Module (GEM). First, WPL decomposes the input into four subbands via wavelet domain learning, and fully utilizes the high- and low-frequency information in the subbands to preserve the discriminative features of small objects. Second, GEM embeds the pixels on the classification responses as nodes in graph learning through graph neural networks, which naturally mines the similarity between pixels. Based on the pixel-level modulation constructed from graph theory, GEM enhances the understanding of small objects and highlights them in the classification responses. The proposed tracker achieves leading performance on five aerial benchmarks, while maintaining a high running speed of 72.5 frames/s. Besides, real-world tests on an aerial platform have proven the effectiveness of SmallTrack. The code and models are available at https://github.com/xyl-507/SmallTrack.

In this article, a novel method for the moving target detection through multilevel spatial and channelwise attention network based on shallow feature channel module (MSCA-SFCM) is presented, and the circular spotlight (CSL) video synthetic aperture radar ground moving target indication (Video-SAR-GMTI) mode of the Nanjing University of Aeronautics and Astronautics miniature SAR (NUAA MiniSAR) system is introduced. However, due to the lack of moving target samples, MSCA-SFCM cannot be directly applied to the CSL Video-SAR-GMTI mode in the real system. To this end, this article proposes a training sample library construction scheme for moving targets of high verisimilitude. In this scheme, based on the radar system parameters, after the traversal of moving target parameters and SAR imaging, the scattering line characteristic of all possible moving targets under the current system parameters is simulated and then used for MSCA-SFCM network training. Afterward, the properly trained network can be used for moving target detection in real radar data. The effectiveness of the proposed method is verified by the NUAA MiniSAR system.

Abstract Recently, Siamese‐based trackers have drawn amounts of attention in visual tracking field because of their excellent performance. However, visual object tracking on Unmanned Aerial Vehicles platform encounters difficulties under circumstances such as small objects and similar objects interference. Most existing tracking methods for aerial tracking adopt deeper networks or inefficient policies to promote performance, but most trackers can hardly meet real‐time requirements on mobile platforms with limited computing resources. Thus, in this work, an efficient and lightweight siamese tracker (MobileTrack) is proposed for high‐time Unmanned Aerial Vehicles tracking, realising the balance between performance and speed. Firstly, a lightweight convolutional network (D‐MobileNet) is designed to enhance the characterisation ability of small objects. Secondly, an efficient object‐aware module is proposed for local cross‐channel information exchange, enhancing the feature information of the tracking object. Besides, an anchor‐free region proposal network is introduced to predict the object pixel by pixel. Finally, deep and shallow feature information is fully utilised by cascading multiple anchor‐free region proposal networks for accurate locating and robust tracking. Extensive experiments on the three Unmanned Aerial Vehicles benchmarks show that the proposed tracker achieves outstanding performance while keeping a beyond‐real‐time speed.

This paper deals with a novel transmitted scheme for a multi-subcarrier frequency MIMO-SAR system, which aims at suppressing the intrapulse repeater mainlobe-jamming and immensely improving the dynamic range of the receiver. To this end, due to the multi-subcarrier frequency transmission scheme, the mixed signal with true target signal and repeater jamming can be separated by a well-designed spatial-frequency filter. Thus, the range and direction of arrival (DOA) information is accurately estimated without interrupting the normal work of the radar. Furthermore, to further improve the orthogonality of transmitted waveform, the design of a phase-coded LFM waveform pair exhibiting both low cross-correlation energy (CCE) and low peak to sidelobe ratios (PSLRs), is considered. Besides, to handle the resulting nondeterministic polynomial (NP) hard problem, an alternating direction multiplier method (ADMM) based optimization method is employed. Compared with the traditional jamming suppression method, the proposed method improves the degree of freedom (DOF) from the carrier frequency domain, and it has the capability to suppress the intrapulse repeater mainlobe-jamming. Finally, detailed simulation experiments are carried out to verify the practicability and effectiveness of the newly proposed transceiver schemes.

Joins in native graph database management systems (GDBMSs) are predefined to the system as edges, which are indexed in adjacency list indices and serve as pointers. This contrasts with and can be more performant than value-based joins in RDBMSs. Existing approaches to integrate predefined joins into RDBMSs adopt a strict separation of graph and relational data and processors, where a graph-specific processor uses left-deep and index nested loop joins (INLJ) for a subset of joins. In this paper we study and experimentally evaluate this technique's performance against an alternative technique that is based on using hash joins that use system-level row IDs (RIDs). In this alternative approach, when a join between two tables is predefined to the system, the RIDs of joining tuples are materialized in extended tables and optionally in RID indices. Instead of using the RID index to perform the join directly, we use it primarily in hash joins to generate filters that can be passed to scans using sideways information passing (sip), ensuring sequential scans. We further compare these two approaches against: (i) the default value-based joins of an RDBMS; and (ii) using materialized views that can avoid evaluating predefined joins completely and instead replace them with scans. We integrated our alternative approach to DuckDB and call the resulting system GRainDB. Our evaluation demonstrates that existing INJL-based approach can be very efficient when entity relations contain very selective filters. However, GRainDB's approach is more robust and is either competitive with or outperforms the INLJ-based approach across a wide range of settings. We further demonstrate that GRainDB far improves the performance of DuckDB, which uses default value-based joins, on relational and graph workloads with large many-to-many joins, making it competitive with a state-of-the-art GDBMS, and incurs no major overheads otherwise.

This article deals with the synthesis and analysis of quasi-orthogonal nonlinear frequency modulation (NLFM) waveforms to mitigate the impairments of ambiguous returns in quadrature-polarimetric (quad-pol) synthetic aperture radars (SARs). To this end, focusing on signals with a continuous piecewise linear instantaneous frequency, the design of a waveform pair exhibiting both a low cross correlation energy (CCE) and low peak to sidelobe ratios (PSLRs), is considered. To handle the resulting nondeterministic polynomial (NP) hard problem, a coordinate descent (CD) method is employed, where, at each step, the marginal minimization is tackled via a MATLAB optimization toolbox. Hence, transmission/reception schemes jointly capitalizing quasi-orthogonal NLFM waveforms and azimuth phase coding (APC) techniques are proposed to suppress ambiguity interference. Moreover, a systematic framework for the evaluation of the resulting azimuth ambiguity-to-signal ratio (AASR) and range ambiguity-to-signal ratio (RASR) is provided. Finally, detailed simulation experiments based on the LuTan (LT-1) parameters are carried out to verify the practicability and effectiveness of the newly proposed transceiver schemes.

Range ambiguity is a technical challenge for current spaceborne synthetic aperture radar (SAR) systems. To this end, a novel range-azimuth joint modulation transmission scheme is proposed, and the corresponding imaging processing and performance analysis are detailed. Compared with the azimuth phase coding (APC) technique, this scheme fully exploits the sampling margins of the range and azimuth dimensions, resulting in the range ambiguities experiencing a double suppression effect. Starting from the range-azimuth joint modulation scheme, to obtain the best ambiguity suppression performance, the design of a nonlinear frequency modulation (NLFM) waveform with a continuous piecewise linear instantaneous frequency is formulated and tackled via a MATLAB optimization toolbox. The detailed simulation results based on LuTan-1 (LT-1) parameters illustrate that the proposed methodologies outperform the APC method and provide considerable ambiguity suppression.

An alternate transmitting mode (ATM) is an important synthetic aperture radar (SAR) imaging mode as it can provide rich waveform design degrees of freedom to improve the system performance, especially to mitigate range ambiguities. However, the azimuth ambiguity issue caused by the differences between the autocorrelation functions of transmitted waveforms is ignored in existing studies. In this article, a deep understanding of the ambiguities in the ATM allows a correct evaluation of the ambiguity-to-signal ratio and the design of quasi-orthogonal nonlinear frequency modulation (NLFM) waveforms optimized for ambiguity suppression. Moreover, a novel azimuth compensation method is developed to remove the azimuth ambiguities caused by waveform diversity. Finally, detailed simulation experiments are carried out to verify the theoretical analysis.

The range ambiguity issue is a technical challenge in the radar community and has been widely discussed over the years. Researchers have given special attention to multiple-input and multiple-output (MIMO) radar to address the range ambiguity because this radar system can employ more equivalent degrees of freedom. Open studies on MIMO radar are generally based on the assumption of orthogonal waveforms, whereas radar performance is seriously limited by distributed targets due to mismatched energy. To this end, this paper deals with a novel MIMO radar transmission scheme called transmitter interpulse phase coding (TIPC) without using orthogonal waveforms. First, a set of well-designed TIPC codes are employed to modulate transmitter subarrays with the same modulated signal. Second, in the case of high pulse repetition frequency (PRF)1, aliased echoes from different transmitted channels are directly separated by a group of simple Doppler filters; for normal PRF2radar systems, a technique called digital beamforming in azimuth is exploited to ensure an effective multiple waveform separation. Third, a decoding processing is performed for a further derivation of the residual TIPC matrix that is related with ambiguity order. Next, the desired and ambiguous echoes are separated by a specifically designed spatial filter that absorbs the residual TIPC matrix. Particularly, the separated signal can be used for some further applications such as increasing the observation swath. Finally, point-like target and distributed targets simulation experiments are performed to verify the feasibility of the proposed TIPC MIMO radar.

The echo separation issue for multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) is usually regarded as a more technical challenge. Spotlighted as a promising solution to the echo separation, the well-known short-term shift-orthogonal (STSO) beamforming scheme has become increasingly popular. However, for airborne MIMO SAR systems, the digital beamforming (DBF) involved in the STSO scheme usually encounters more issues, e.g., the direction of arrival (DOA) mismatch induced by topography variation. Up to now, relatively less research on robust DBF processing has been conducted for airborne MIMO SAR systems. In this respect, an adaptive DBF technique, based on interference plus noise covariance matrix (IPNCM) reconstruction and desired signal steering vector estimation, has been proposed in this paper. IPNCM reconstruction and steering vector estimation can not only cope with the DOA mismatch problem, but also remove the desired signal component in the training data cells to increase the beamformer convergence rates. Consequently, the proposed approach really improves the array output signal-to-interference-plus-noise ratio (SINR). Moreover, numerous discussions and simulations are carried out to prove the effectiveness of proposed DBF technique under various disturbance environments. Compared with the current DBF techniques, the proposed method provides a bright application prospect for the STSO scheme.

Multiple-input–multiple-output synthetic aperture radar (MIMO-SAR) has extensive application prospects, mainly including the acquisition of multidimensional scattering information, high-resolution and wide-width (HRWS) imaging, and moving target indication (MTI). Its echo separation is the most technical challenge, and so far, the confirmation for orthogonal waveform encoding MIMO-SAR by airborne experiments has not been reported in any literature. Here, an echo separation experiment based on the segmented phase code (SPC) waveforms and an airborne digital beamforming SAR (DBF-SAR) system is demonstrated for the first time. In the experiment, the SPC waveforms are cyclically transmitted within the adjacent pulse repetition intervals (PRIs) to simulate multiple transmitters, and the scattered echoes are received by the 16-channel antennas in elevation at the same time. In the postprocessing, the echo signals of continuous PRIs are added to obtain the mixed echoes, and a detailed echo separation method is adopted. In the method, the mixed echo signals from close arrival angles and far arrival angles are separated by the time shift and weighting, and by the bandpass filtering and DBF technique, respectively. Through the presented method, the mixed echoes of dual-transmit and 16-receive (2T16R) SAR imaging mode are separated and imaged successfully. The experimental results not only validate the echo separation scheme but also indicate that it is very promising in future MIMO-SAR missions.

In recent years, the multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) concept has been widely researched because it can provide more degrees of freedom to dramatically improve SAR system performance. However, the echo separation issue of different transmit antennas is the most challenging issue and it stirs up extensive discussions. In this letter, a novel MIMO-SAR imaging scheme based on the improved orthogonal frequency-division multiplexing (OFDM) waveforms is proposed. The main contributions of this work are that:1)Improved generation method for$M$OFDM waveforms is presented. This method can compensate the extra carrier frequency deviation and compared with the other compensation methods, this method is more general and suitable for$M$OFDM waveforms.2)Based on the proposed OFDM waveforms, a novel and low-cost spaceborne MIMO-SAR imaging scheme is proposed.In this scheme, a simple time-shift weighting process and a bandpass filter bank are employed to separate the echoes from the close arrival angles. Then, the digital beamforming (DBF) on receive in elevation is employed to separate the echoes from far arrival angles. Furthermore, the distributed scene simulation results are presented to verify the practicability of the proposed scheme.

Azimuth ambiguity degrades the quality of synthetic aperture radar (SAR) images and leads to the increase of false alarm rate in ground moving target indication (GMTI). Due to the existing azimuth ambiguity, suppression methods do not remove the first-order ambiguity completely and ignore the ambiguity above first order as well, moving target detection is affected by residual ambiguity. Hence, a novel method to suppress first-order and higher order azimuth ambiguities for the dual-channel SAR/GMTI is proposed in this letter. First, the displaced phase center antenna (DPCA) technique is applied to suppress clutter. Then, estimate the local azimuth ambiguity-to-signal ratio (LAASR) to find out the area affected by ambiguity. Finally, an inpainting algorithm is improved to patch the ambiguity area. The proposed method can remove the ambiguity almost completely. Moreover, the method is verified using the GaoFen-3 SAR dual-channel complex image data, and the result shows that the false alarm of moving target detection is degraded without reducing the detection rate.

In this letter, a novel and low-cost echo separation technique for the multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) is presented, based on the orthogonal frequency-division multiplexing (OFDM) chirp waveforms. The proposed scheme allows the generation of multiple OFDM chirp waveforms on common spectral support. In the new scheme, a series of simple time-domain operations including replica, T-shift, and superposition is applied to eliminate the interference waveform within a limited time. Then, a combination with a bandpass filter instead of a matched filter to focus signal power and digital beamforming (DBF) on receive in elevation enables the suppression of interference signals for a realistic spaceborne SAR scenario, where the swath width exceeds the spatial extension of the transmitted pulse. Furthermore, the distributed scene simulation results are presented to verify the practicability of the proposed scheme.

The echo separation issue associated with different transmit antennas is the most technical challenge in realizing the multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) system. In this article, a novel MIMO-SAR imaging scheme based on an advanced radar waveform, namely, segmented-phase-code (SPC) waveform, is proposed. Compared with the state-of-the-art short-term shift-orthogonal (STSO) waveform beamforming schemes, this scheme relieves the short-term shift-orthogonality condition of transmitted waveforms without losing the imaging performance, which extends the optional waveform space. In this scheme, the separation of the echoes from close arrival angles is ensured by a simple time-shift weighting processing. Furthermore, a range bandpass filter bank and the digital beamforming (DBF) technique are employed to ensure that the echoes from far arrival angles are separable. Finally, detailed simulation experiments are performed to verify the feasibility of the proposed scheme, and in-depth discussions of different waveforms and MIMO-SAR imaging schemes are presented.

For synthetic aperture radars (SARs), an alternate transmitting mode based on waveform diversity is widely discussed for suppressing range-ambiguity in many letters, because it is easy to implement and there is no need to improve the pulse repetition frequency (PRF). These studies mainly focus on the discussion of pseudo-orthogonal waveform design, such as up-down chirp waveforms and orthogonal-frequency-division-multiplexing (OFDM) waveforms; however, the effect on imaging caused by waveform diversity is ignored. This letter, for the first time, provides a demonstrative derivation of imaging for the alternate transmitting mode, which will deepen the understanding of this mode and be helpful for the future research. In this letter, we point out that transmitting different waveforms will introduce a phase-amplitude periodic modulation in the azimuth domain; furthermore, it will cause the aliasing of the azimuth spectrum. In addition, the simulation experiment is performed for verifying the correctness of the theoretical analysis.

Azimuth ambiguity greatly affects the image quality and application of synthetic aperture radar (SAR). Several azimuth ambiguity suppression methods have been proposed; however, these methods cannot remove the ambiguity completely and only the first-order ambiguity has been considered. Hence, in this letter, a novel method based on Wiener filtering and local azimuth ambiguity-to-signal ratio (AASR) estimation for N-order azimuth ambiguity suppression is proposed. The Wiener filtering is used to attenuate the ambiguity energy. Then the original image and the filtered image are combined to estimate local AASR, which is used to identify the ambiguity pixel. Finally, the ambiguity can be removed via interpolation. Through this method, the N-order ambiguity energy can also be suppressed to a lower level, and simultaneously, the consistency, resolution, and signal-to-noise ratio of an SAR image are maintained. Furthermore, in order to verify the practicability of the proposed method, it has been tested on the GaoFen-3 image and TerraSAR-X image.

It is well known that the nonlinear frequency modulation (NLFM) chirp waveform can shape advantageously the power spectrum density (PSD) such that the autocorrelation function exhibits reduced sidelobes as the window function. However, differences between the PSD and window function due to the Gibbs effects caused by the Fresnel integral would lead to deteriorative performance of the NLFM chirp waveform. In particular, the correlation function of NLFM waveform with Chebyshev PSD is seriously inconsistent with the lowest sidelobe level of the Chebyshev window function possesses. To overcome the inconsistency, therefore, in this letter, a novel NLFM waveform with modified Chebyshev window PSD is proposed, which combines the Chebyshev with edge distortion compensation, allowing, in theory, low sidelobe level as Chebyshev window. Using theoretical analysis also confirmed by simulation, this letter shows that the novel NLFM waveform possesses low sidelobes without high computational complexity in design.

LuTan-1 (LT-1), i.e., TwinSAR-L, mission is an innovative spaceborne bistatic synthetic aperture radar (SAR) mission that is based on two satellites operating at L-band with flexible formation flying, which is planned to launch in 2020. The primary objective of LT-1 is to generate a highly accurate global digital elevation model (DEM). Beyond that, LT-1 will serve for the demonstration of some state-of-the-art technologies in radar field and some applications, such as biomass inversion, disaster forecasting, and climate and environmental monitoring, and phase synchronization is a technical challenge in realizing the highly accurate topography and deformation measurements. The pulsed alternate synchronization scheme, which is proposed to solve the synchronization problem of TerraSAR-X add-on for Digital Elevation Measurements (TanDEM)-X, is an efficient and accurate method. However, it interrupts the normal work of the TanDEM-X, accordingly flowing a series of problems. For LT-1, a novel synchronization scheme, in which the phase synchronization signal is exchanged by virtue of a time slot between radar signals, is applied. Thus, the working efficiency and synchronization accuracy can further be improved. Furthermore, the performance prediction and phase synchronization experiment for this synchronization scheme is presented, which verifies the feasibility of the proposed scheme. Finally, in order to guarantee the transmission quality of the synchronization signal, the illumination combinations and gain variation of the synchronization antenna in an orbital period are detailed.

LuTan-1 [(LT-1), i.e., TwinSAR-L] mission is an innovative spaceborne bistatic synthetic-aperture radar (BiSAR) mission focusing mainly on differential interferometry, which will be launched in 2020. This article introduces some important aspects of the LT-1 mission for the first time, including the formation configuration, imaging mode, application scenarios, and the efficient baselines between the master satellite and the slave satellite. To realize the high accurate topography and deformation measurements, a wide swath BiSAR focusing algorithm with phase reserving ability should be developed. This article proposes a modified bistatic range-Doppler algorithm based on 2-D principle of stationary phase spectrum, which reduces the phase error introduced by the root term expansion and has an excellent focus performance and a good phase reserving ability. Finally, the spaceborne bistatic simulation experiments using orbital parameters and imaging mode of the LT-1 mission, including point targets and scene targets, are utilized to illustrate the validity and accuracy of the proposed algorithm.

It is well known that the nonlinear frequency modulation (NLFM) waveform with the advantage that it can shape the power spectral density (PSD) to provide a radar matched filter output with lower sidelobe without the loss of signal-to-noise ratio (SNR) when compared with the linear frequency modulation (LFM) waveform. But NLFM waveform would also broaden the main lobe and reduce the range resolution. In this paper, we report a novel waveform optimization framework. Through this framework, an advanced nonlinear frequency modulation (NLFM) waveform with lower sidelobes and a smaller main lobe is constructed. In addition, we apply it in a real synthetic aperture radar (SAR) system with a bandwidth of 100 MHz at 9.6 GHz carrier frequency and the imaging results validate the proposed NLFM waveform.

The phase synchronization is one of the key issues to be addressed for the bistatic synthetic aperture radar system. In this paper, an advanced non-interrupted phase synchronization scheme is proposed. Both satellites are equipped with four synchronization antennas for a mutual exchange of synchronization pulses, which are transmitted rightly after the radar signal transmitting and before the echo receiving. Therefore, it can not interrupt the normal SAR data acquisition, which can further improve synchronization accuracy and avoid the data missing effect. The ground validation system for TwinSAR-L synchronization module is described in detail. The results are also evaluated to demonstrate feasibility of the proposed scheme.

Range ambiguity suppression is a technical challenge for current synthetic aperture radar systems. A potential solution is to orthogonally modulate the transmitting pulses; however, these orthogonal waveforms (e.g., up-down chirp waveforms) actually cannot reduce the cross correlation energy (CCE). Nonlinear frequency modulation (NLFM) waveform can change the time-frequency relationship to adjust the energy distribution within the bandwidth to reduce the CCE. In this letter, a novel orthogonal NLFM waveform optimization framework is proposed. Through this framework, advanced NLFM waveforms with low sidelobe and CCE are constructed. Furthermore, point and distributed scene simulation results are presented to verify the practicability of the proposed waveforms. In addition, the system scheme, waveform design, and range ambiguity suppression performance are detailed.

Generally, synthetic aperture radar (SAR) system transmits linear frequency modulation (LFM) signal to obtain the high-resolution image and weighted windowing is usually employed to suppress sidelobes. However, it will cause a 1-2-dB signal-to-noise ratio (SNR) loss. Nonlinear frequency modulation (NLFM) signal, which can construct the signal's power spectral density (PSD) to reduce sidelobes without loss of SNR, is a promising candidate. However, the real-time generation of precise NLFM signal is still a technical challenge. In this letter, a high-precision NLFM signal generator with the ability of predistortion compensation is developed, and this signal generator will be employed in LuTan-1 (LT-1, i.e., TwinSAR-L) mission which is an innovative spaceborne bistatic SAR mission and planned to launch in 2020. In addition, a two-step error compensation method is developed to compensate the system error. Finally, the ground experiment is performed to validate the designed signal generator.

With the development of high-resolution radar satellite for global comprehensive environmental monitoring, day-and-night and all-weather surveillance has become an active and growing research field. However, in all cases, these applications require radar to have a high-efficiency radar module (e.g., T/R module), and high system transmitting power. These requirements may put an important limitation on the performance of a radar satellite with a high-power configuration. In this paper, we report a novel waveform optimization framework. Through this framework, an advanced nonlinear frequency modulation (NLFM) waveform with lower sidelobes and a smaller main lobe, which can significantly relieve the restriction of very limited satellite power, is constructed. In addition, we apply it in a real synthetic aperture radar (SAR) system with a bandwidth of 100 MHz at 9.6-GHz carrier frequency and the whole process of the NLFM waveform for radar imaging is discussed in detail, including the system architecture and configuration, a system error compensation method, and a modified chirp scaling algorithm (CSA). The imaging results demonstrate the excellent performance of the advanced NLFM waveform. Moreover, we observe that the SAR system with the advanced waveform has a higher signal-to-noise ratio (SNR) of 1.29 dB compared with the conventional linear frequency modulation (LFM) waveform. The improvement of 1.29-dB SNR means that the real radar system can reduce transmitting power with a ratio of 25%. This effect is likely to be a potential feature of NLFM waveform, which can reduce the transmitting power requirement, especially for radar satellite.

Popular column stores such as ORC and Parquet have been widely used in many Hadoop-oriented data analysis systems. With the effective column skipping and data compression functionalities provided by column stores, wide tables with hundreds or even thousands of columns are applied by many big data analysis applications to avoid the expensive distributed joins. We found that the performance of such systems can be further improved by optimizing the physical data layout to fit certain workloads and system settings. However, it is nontrivial to perform such optimization manually. In this demo, we present a data layout optimization tool called Rainbow, which leverages workload-driven layout optimization algorithms to adjust data layouts adaptively without intervening the previous data blocks that have been stored. We also provide a Web UI for users to interact with the layout optimization process. Furthermore, Rainbow is open sourced with an accompanying benchmark for performance evaluation of wide tables.

Real time analysis of fine granularity of log data can help people gain personalized insights on business. For example, real time analysis of e-commerce log data will help us learn recent changes of browsing and shopping behavior of specific customers, which enables us to provide personalized recommendations. To accomplish such analysis, log data should have been loaded quickly into data warehouse without loss. This paper proposes a no loss staging and fast loading solution for log data. Based on open sourced tools such as Kafka, HDFS, and Spark, we have designed and implemented an entity fiber based log data partitioning and staging method, as well as a parallel loading algorithm. Our scheme achieves a data staging performance of around 390,000 records/s, and a data loading performance of around 160,000 records/s.