Search Results (50)

In high-frequency surface wave radar (HFSWR) systems, the retrieval of the directional wave spectrum has remained challenging, especially in the case of echoes from long ranges with a low signal-to-noise ratio (SNR). Therefore, a quadratic programming algorithm based on the regularization technique is proposed with an empirical criterion for estimating the optimal regularization parameter, which minimizes the effect of noise to obtain more accurate inversion results. The reliability of the inversion method is preliminarily verified using simulated Doppler spectra under different wind speeds, wind directions, and SNRs. The directional wave spectra inverted from a radar network with two multiple-input multiple-output (MIMO) systems are basically consistent with those from the ERA5 data, while there is a limitation for the very concentrated directional distribution due to the truncated second order in the Fourier series. Further, in the field experiment during a storm that lasted three days, the wave parameters are calculated from the inverted directional spectra and compared with the ERA5 data. The results are shown to be in reasonable agreement at four typical locations in the core detection area. In addition, reasonable performance is also obtained under the condition of low SNRs, which further verifies the effectiveness of the proposed inversion algorithm. Full article

This paper addresses the problem of target localization in a distributed waveform diverse array radar system, exploiting the technique of sparse reconstruction. At the configuration stage, the distributed radar system consists of two individual Frequency Diverse Array Multiple-Input Multiple-Output (FDA-MIMO) radars and one single Element-Pulse Coding MIMO (EPC-MIMO) radar. To obtain the angle and incremental range (i.e., the range offset between the sampling point and actual position within the range bin) of the targets in each local radar, two sparse reconstruction-based algorithms, including the grid-based Iterative Adaptive Approach (IAA) and gridless Atomic Norm Minimization (ANM) algorithms, are implemented. Furthermore, multiple sets of local statistics are fused at the fusion center, where a Weighted Least Squares (WLS) method is performed to localize targets. At the analysis stage, the estimation performance of the proposed methods, encompassing both IAA and ANM algorithms, is evaluated in contrast to the Cramér–Rao Bound (CRB). Numerical results and parametric studies are provided to demonstrate the effectiveness of the proposed sparse reconstruction methods for target localization in the distributed waveform diverse array system. Full article

Due to its fast processing time and robustness against harsh environmental conditions, the frequency modulated continuous waveform (FMCW) multiple-input multiple-output (MIMO) radar is widely used for target localization. For high-accuracy localization, the two-dimensional multiple signal classification (2D MUSIC) algorithm can be applied to signals received by a single FMCW MIMO radar, achieving high-resolution positioning performance. To further enhance estimation accuracy, received signals or MUSIC spectra from multiple FMCW MIMO radars are often collected at a data fusion center and processed coherently. However, this approach increases data communication overhead and implementation complexity. To address these challenges, we propose an efficient high-resolution target localization algorithm. In the proposed method, the target position estimates from multiple FMCW MIMO radars are collected and combined using a weighted averaging approach to determine the target’s position within a unified coordinate system at the data fusion center. We first analyze the achievable resolution in the unified coordinate system, considering the impact of local parameter estimation errors. Based on this analysis, weights are assigned according to the achievable resolution within the unified coordinate framework. Notably, due to the typically limited number of antennas in FMCW MIMO radars, the azimuth angle resolution tends to be relatively lower than the range resolution. As a result, the achievable resolution in the unified coordinate system depends on the placement of each FMCW MIMO radar. The performance of the proposed scheme is validated using both synthetic simulation data and experimentally measured data, demonstrating its effectiveness in real-world scenarios. Full article

Continuous and robust trajectory tracking of unmanned aerial vehicles (UAVs) plays a crucial role in urban air transportation systems. Accordingly, this article presents an end-to-end spatiotemporal correlation integration (ESCI)-based UAV tracking framework by leveraging a high-resolution cascade multiple input multiple output (MIMO) radar. On this account, a novel joint anti-interference detection and tracking system for weak extended targets is presented in this paper; the proposed method handles them jointly by integrating a continuous detection process into tracking. It not only eliminates the threshold decision-making process to avoid the loss of weak target information, but also significantly reduces the interference from other co-channel radars and strong clutters by exploring the spatiotemporal correlations within a sequence of radar frames, thereby improving the detectability of weak targets. In addition, to accommodate the time-varying number and extended size of radar reflections, with the ellipse spatial probability distribution model, the extended UAV with multiple scattering sources can be treated as an entity to track, and the complex measurement-to-object association procedure can be avoided. Finally, with Texas Instruments AWR2243 (TI AWR2243) we can utilize a cascade frequency-modulated continuous wave–multiple input multiple output (FMCW-MIMO) radar platform. The results show that the proposed method can obtain outstanding anti-interference performance for extended UAV tracking compared with state-of-the-art methods. Full article

In the distributed multiple input multiple output (MIMO) system with the integrated radar and communication (IRAC) waveform transmitted, the synthesized transmit beampattern usually suffers from high sidelobes. To decrease the sidelobes of the transmit beampattern and accomplish radar and communication functions simultaneously in the distributed MIMO system, this paper proposes two IRAC waveform design methods. First, to minimize the maximal sidelobe of the transmit beampattern, this paper proposes the IRAC waveform design method with low sidelobes, and the designed IRAC waveform can produce the desired radar waveform in the target direction and communication waveform in the user direction, respectively. However, the designed IRAC waveform may have non-constant modulus, and it will be distorted if the power amplifier works in the saturation region. Then, to make sure the modulus of the designed IRAC waveform is constant, this paper proposes the IRAC waveform design method with constant modulus. In addition to producing the desired waveforms, the designed IRAC waveform has constant modulus. Moreover, the transmit beampattern has low sidelobes. Finally, the simulation results show that the proposed IRAC waveform design methods can simultaneously accomplish radar and communication functions and form the transmit beampattern with low sidelobes. Full article

This work addresses the issue of closely spaced multi-target localization in distributed MIMO radars using bistatic range (BR) and angle of arrival (AOA) measurements. We propose a two-step method, decomposing the problem into measurement association and individual target localization. The measurement association poses a significant challenge, particularly when targets are closely spaced along with the existence of both false alarms and missed alarms. To tackle this challenge, we formulate it as a clustering problem and we propose a novel clustering algorithm. By carefully defining the distance metric and the set of neighboring estimated points (EPs), our method not only produces accurate measurement association, but also provides reliable initial values for the subsequent individual target localization. Single-target localization remains challenging due to the involved nonlinear and nonconvex optimization problems. To address this, we formulate the objective function as a form of the product of certain local functions, and we design a factor graph-based iterative message-passing algorithm. The message-passing algorithm dynamically approximates the complex local functions involved in the problem, delivering excellent performance while maintaining low complexity. Extensive simulation results demonstrate that the proposed method not only achieves highly efficient association but also outperforms state-of-the-art algorithms and exhibits superior consistency with the Cramer–Rao lower bound (CRLB). Full article

Accurate fruit detection is of great importance for yield assessment, timely harvesting, and orchard management strategy optimization in precision agriculture. Traditional optical imaging methods are limited by lighting and meteorological conditions, making it difficult to obtain stable, high-quality data. Therefore, this study utilizes near-field millimeter-wave MIMO-SAR (Multiple Input Multiple Output Synthetic Aperture Radar) technology, which is capable of all-day and all-weather imaging, to perform high-precision detection of apple targets in orchards. This paper first constructs a near-field millimeter-wave MIMO-SAR imaging system and performs multi-angle imaging on real fruit tree samples, obtaining about 150 sets of SAR-optical paired data, covering approximately 2000 accurately annotated apple targets. Addressing challenges such as weak scattering, low texture contrast, and complex backgrounds in SAR images, we propose an innovative detection framework integrating Dynamic Spatial Pyramid Pooling (DSPP), Recursive Feature Fusion Network (RFN), and Context-Aware Feature Enhancement (CAFE) modules. DSPP employs a learnable adaptive mechanism to dynamically adjust multi-scale feature representations, enhancing sensitivity to apple targets of varying sizes and distributions; RFN uses a multi-round iterative feature fusion strategy to gradually refine semantic consistency and stability, improving the robustness of feature representation under weak texture and high noise scenarios; and the CAFE module, based on attention mechanisms, explicitly models global and local associations, fully utilizing the scene context in texture-poor SAR conditions to enhance the discriminability of apple targets. Experimental results show that the proposed method achieves significant improvements in average precision (AP), recall rate, and F1 score on the constructed near-field millimeter-wave SAR apple dataset compared to various classic and mainstream detectors. Ablation studies confirm the synergistic effect of DSPP, RFN, and CAFE. Qualitative analysis demonstrates that the detection framework proposed in this paper can still stably locate apple targets even under conditions of leaf occlusion, complex backgrounds, and weak scattering. This research provides a beneficial reference and technical basis for using SAR data in fruit detection and yield estimation in precision agriculture. Full article

Multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) is a promising scheme for high-resolution wide-swath (HRWS) imaging. After echo separation processing, a MIMO-SAR system can provide many equivalent phase centers (EPCs) in azimuth. However, EPC duplication occurs for traditional monostatic systems with uniform antenna arrays, leading to system resource waste. Moreover, range ambiguity suppression is a necessary process for wide-swath SAR systems. In this paper, a novel MIMO-SAR echo separation and range ambiguity suppression processing framework is proposed for HRWS imaging. A set of transmission delays is introduced to the transmit channels to displace the repetitive EPCs. The transmission delays can also be used to flexibly control the performance of echo separation. A wide-null beamformer is employed to accomplish echo separation and ambiguity suppression simultaneously. The proposed framework is designed for real-time processing and therefore does not require frequency-domain operations. Finally, the proposed framework is verified through point target and distributed scene simulation experiments. Full article

The distributed multiple-input multiple-output (MIMO) radar observes targets from different angles, which can overcome the adverse effects of target glint and avoid the situation where the target’s tangential flight cannot be effectively detected by the radar, thus providing great advantages in target detection. However, distributed MIMO often encounters a scarcity of training samples for target detection. To overcome this difficulty, this paper proposes a Bayesian approach. By modeling the target signal as a subspace signal, where each transmit–receive pair possesses a distinct and unknown covariance matrix governed by an inverse Wishart distribution, three efficient detectors are devised based on the generalized likelihood ratio test (GLRT), Rao, and Wald criteria. Comparative analysis with existing detectors reveals that the proposed Bayesian detectors exhibit superior performance, particularly in scenarios with limited training data. Experimental results demonstrate that the Bayesian GLRT achieves the highest probability of detection (PD), outperforming conventional detectors by requiring a reduction in signal-to-noise ratio (SNR). Furthermore, an increase in the degrees of freedom of the inverse Wishart distribution and the number of receiving antennas enhances detection performance, albeit at the cost of increased hardware requirements. Full article

Multiple-input multiple-output synthetic aperture radar (MIMO-SAR) anti-jamming waveform design relies on accurate prior information about the interference. However, it is difficult to obtain accurate prior knowledge about uncertain intermittent sampling repeater jamming (ISRJ), leading to a severe decline in the detection performance of MIMO-SAR systems. Therefore, this article studies the robust joint design problem of MIMO radar transmit waveform and filter against uncertain ISRJ. We characterize two categories of uncertain interference, including sample length uncertainty and sample-time uncertainty, modeled as Gaussian distribution in different range bins. Based on the uncertain interference model, we formulate the maximizing SINR as a figure of merit, which is a non-convex quadratic optimization problem under specific waveform constraints. Based on the alternating direction method of multipliers (ADMM) framework, a novel joint design algorithm of waveform and filter is proposed. In order to improve the convergence performance of ADMM, the difference in convex functions (DC) programming is applied to the ADMM iterations framework to solve the problem of waveform energy inequality constraint. Finally, numerical results demonstrate the effectiveness and robustness of the proposed method, compared to the existing methods that utilize deterministic interference models in the uncertain ISRJ environment. Moreover, the spaceborne SAR real scene imaging simulations are conducted to evaluate the anti-ISRJ performance. Full article

Utilizing spatial diversity, the distributed multiple-input multiple-output (MIMO) radar has the potential advantage of improving system detection performance. In this paper, the incoherent detection performance of distributed multiple-input multiple-output (MIMO) radars is investigated for Rice fluctuating targets. To calculate the incoherent detection probability, the moment generating function (MGF) of the Rice variable is expanded as the infinite series form. By inverting the product of MGFs of multiple independent Rice variables, new closed-form expressions for the probability density function (PDF) of the sum of independent and weighted squares of Rice variables are proposed. The proposed PDF expression for the sum of independent, non-identically distributed (i.n.i.d.) Rice variables involves an infinite series in terms of the confluent Lauricella function. Specially, the PDF for the sum of independent identically distributed (i.i.d.) Rice is expressed as the confluent hypergeometric function-based infinite series. In addition, the uniform convergence of the proposed PDF expression is also validated. Using this proposed expression, the closed-form and approximate expressions of the incoherent detection probability of MIMO radar are derived, respectively. Numerically evaluated results are illustrated and compared with Monte Carlo (MC) simulations to validate the accuracy of the derivations. Full article

This paper introduces a resource management approach for distributed multiple-input multiple-output (MIMO) radar systems equipped with phased array antennas. The approach focuses and adjusts narrow beams from all transmit and receive nodes to generate a regularly shaped observation area for reliable detection. Based on this, a structured early warning framework can be built by evenly arranging sufficient observation areas to cover the surveillance region and periodically scanning these areas for continuous monitoring. Observation area generation, a core technique for this framework, involves the joint optimization of beamforming weights for both transmit and receive nodes, as well as the beam dwell time. Our optimization strategy is designed to achieve two key objectives: minimizing beam dwell time to ensure rapid alerts for defense systems, and minimizing node transmit power to extend operational time while reducing the risk of intercept. To address the problem of observation area generation, we propose a two-stage method. The first stage uses the signal-to-clutter-plus-noise ratio (SCNR) as a new criterion to determine the transmit and receive beamforming weights. The second stage employs a power factor as an additional variable to scale the transmit beamforming weights under various beam dwell times, constructing a Pareto solution set for the problem. Numerical simulations validate the effectiveness of our method, demonstrating improved detection capabilities compared to monostatic phased array radar systems. Full article

In order to reduce the hardware cost and data acquisition time in near-field scenarios, such as airport security imaging systems, this paper discusses the layout of a multiple-input multiple-output (MIMO) radar array. In view of the existing multi-input multiple-output imaging algorithm, the reconstructed image artifacts and aliasing problems caused by sparse sampling are discussed. In this paper, a multi-station radar array and a corresponding sparse MIMO imaging algorithm based on combined sparse sub-channels are proposed. By studying the wave–number spectrum of backscattered MIMO synthetic aperture radar (SAR) data, the nonlinear relationship between the wave number spectrum and reconstructed image is established. By selecting a complex gain vector, multiple channels are coherently combined effectively, thus eliminating aliasing and artifacts in the reconstructed image. At the same time, the algorithm can be used for the MIMO–SAR configuration of arbitrarily distributed transmitting and receiving arrays. A new multi-station millimeter wave imaging system is designed by using a frequency-modulated continuous wave (FMCW) chip and sliding rail platform as a planar SAR. The combination of the hardware system provides reconfiguration, convenience and economy for the combination of millimeter wave imaging systems in multiple scenes. Full article

The proliferation of electronic countermeasure (ECM) technology has presented military radar with unprecedented challenges as it remains the primary method of battlefield situational awareness. In this paper, a joint antenna scheduling and power allocation (JASPA) scheme is put forward for multi-target tracking (MTT) in the distributed multiple-input multiple-output (D-MIMO) radar. Aiming at radar resource scheduling in the presence of range deception jamming (RDJ), the false target discriminator is designed based on the Cramer–Rao lower bound (CRLB) in terms of the spoofing range, and the predicted conditional CRLB (PC-CRLB) plays a role in evaluating tracking accuracy. The JASPA scheme integrates the quality of service (QoS) principle to develop an optimization model based on false target discrimination, with the objective of enhancing both the discrimination probability of false targets and the tracking accuracy of real targets concurrently. Since the optimal variables can be separated in constraints, a four-step optimization cycle (FSOC)-based algorithm is developed to solve the multidimensional non-convex problem. Numerical simulation results are provided to illustrate the effectiveness of the proposed JASPA scheme in dealing with MTT in the RDJ environment. Full article

The multiple-input multiple-output synthetic aperture radar (MIMO SAR) system has developed rapidly since its discovery. At the same time, the low-disturbance and high-gain requirements of the MIMO system are continuing to increase. Through the application of digital beamforming (DBF) techniques, the multidimensional waveform encoding (MWE) technique can play a key role in MIMO systems, which can greatly improve the system’s performance, especially the multi-mission capability of radar. Intrapulse beamsteering in elevation is a typical form of multi-dimensional waveform encoding which can greatly improve the transmitting efficiency and multi-mission performance of radar. However, because of the high sensitivity of the DBF technique to height, there is significant deterioration in performance in the presence of terrain undulations. The OFDM (Orthogonal Frequency Division Multiplexing) technique is widely used in communication. Due to the similarity of radar and communication systems and the great waveform diversity of OFDM signals, the OFDM radar has recently begun to emerge as a new radar system, simultaneously, the orthogonality of OFDM signals is in the time and frequency domains, and is not affected by terrain undulation. So, this paper proposes a novel radar mode combining intrapulse beamsteering in elevation and OFDM-Chirp signals, that is, the combination of “beam orthogonality” and “waveform orthogonality”, which can greatly improve the performance and fault tolerance to interference signals. In this manuscript, the system working mode and signal processing flow are introduced in detail, and simulations for both point targets and distributed targets are carried out to verify the feasibility of the proposed mode. Simultaneously, a comparison experiment is carried out, which shows the high level of fault tolerance to terrain undulation and the high potential of the proposed radar mode in Earth observation. Full article