Aiming to improve radar multi-target tracking (MTT) accuracy and association performance in complex scenarios involving dense clutter, missed detections, and maneuvering targets, an improved tracklet generation approach based on the expectation-maximization (EM) framework is proposed in which data association variables and motion model variables are jointly modeled as latent variables. These variables are estimated through iterative updates based on the loopy belief propagation (LBP) algorithm and the interacting multiple model (IMM) filtering and smoothing algorithms to generate high-confidence tracklets. Then, a delayed decision-making strategy based on the multi-hypothesis approach is employed to associate these tracklets into complete target trajectories. The resulting algorithm is named IMM-TrackletMHT. The performance of the IMM-TrackletMHT algorithm is evaluated and compared with several baseline algorithms in simulated scenarios under different clutter rates and detection probabilities. The simulation results demonstrate that the proposed algorithm consistently outperforms the baseline methods in terms of tracking accuracy, exhibits strong robustness to variations in the operating environment, and achieves higher computational efficiency in multi-scan measurement processing, thereby demonstrating the effectiveness and superiority of the proposed tracklet generation approach for maneuvering MTT.

Radar signal pulse train recognition plays a crucial role in modern electronic warfare, serving as the foundation for further analysis of radar signal sources and operating modes. Currently, most studies rely on PRI modulation features as the primary basis for recognition. However, with the continuous development of radar technology, the frequency parameters of radar pulses have become increasingly dynamic and complex; nevertheless, this aspect has not received adequate attention over the years. This paper proposes a signal pulse train recognition method based on a Dual-branch LSTM-Transformer architecture, introducing improvements in both input design and network structure. In terms of architecture, a dual-branch structure is constructed, with each branch employing an LSTM-Transformer network that effectively combines the strengths of both models. For the input, the two branches respectively receive the normalized PRI sequences, frequency sequences, and their corresponding difference sequences. Additionally, a gating mechanism is introduced during the feature fusion stage to adaptively allocate feature weights, enabling more accurate and efficient radar pulse train recognition. Simulation results show that this method achieves a recognition rate exceeding 90% even in high-noise environments, demonstrating strong robustness.

Frequency agile radar (FAR) is widely recognized for its robust antijamming capabilities in complex electromagnetic environments. However, achieving accurate monopulse direction-of-arrival (DOA) estimation with FAR remains challenging, particularly under short dwell times where phase shifter mismatch (PSM) exacerbates the issue. This article proposes a targeted procedure to monopulse DOA estimation for FAR that considers the impact of PSM. Initially, we introduce a modified FAR Radon-Fourier transform algorithm to enhance the extraction of matched filtering samples for high-speed targets, laying the groundwork for subsequent DOA estimation procedures. We then present two distinct methods for monopulse DOA estimation with FAR. The modified monopulse method facilitates rapid DOA estimation at low beam pointing angles, while the maximum likelihood estimation (MLE) method ensures high accuracy across all beam pointing angles. Furthermore, we discuss fast implementation techniques tailored to the MLE method. In addition, leveraging extensive simulation experiments based on real-world electromagnetic data and rigorous theoretical analyses, we explore critical aspects such as the Cram & eacute;r-Rao lower bound (CRLB) for DOA estimation under FAR with PSM and the theoretical computational complexities of our algorithms. Our results demonstrate the superior performance of the proposed methods in diverse scenarios. Particularly noteworthy is the accuracy achieved by the modified monopulse and MLE methods, which closely approach the CRLB at medium to high signal-to-noise ratios.

Airborne distributed coherent aperture radar (ADCAR) benefits from the signal coherent synthesis of multichannel echoes to improve signal gain and detection performance significantly. However, the complex detection environment formed by the geometrical configuration between ADCAR and the region of interest always causes distance-velocity envelope shifts with different properties and relative phase deviations, severely reducing the signal coherent synthesis efficiency. We make contributions toward addressing these limitations. First, the signal model and description of target coherent parameters (TCPs) for ADCAR in the receive-coherent (RC) stage are considered, and a parameter space division using the dimensionality reduction (DR) condition for TCPs is proposed. Then, a multiple transmit-receive pairs joint TCPs estimation method based on maximum likelihood estimation is proposed, and the RC synthesis strategies are performed with the DR and non-DR conditions by the proposed TCPs estimation method. On this basis, two fast implementations based on particle swarm optimization and conjugate gradient are proposed for the RC synthesis and the TCPs output. Finally, the results of simulation experiments and real-measured data indicated that the performance of the proposed methods for signal coherent synthesis, TCPs estimation, and target detection is significantly improved compared with the traditional methods.

Accurate target angle estimation is one of the challenges for wideband radars due to the fact that target occupies multiple range bins, resulting in lower energy or signal to noise ratio in a single range bin. This paper proposes a processing technique for enhanced accuracy of target angle estimates for wideband monopulse radars. Firstly, to accumulate the energy of the received echo signals from different scatterers on a target, the phase difference between different scatterers on a target is estimated using the minimum entropy phase estimation method combining with the correlation between adjacent pulses. Then, the monopulse ratio is obtained by using the signals from the accumulated sum and difference channels. The target angle is estimated by weighting the accumulated echo energy for accuracy enhancement. Experimental results based on both numerical simulation and measured data are presented to validate the effectiveness of the proposed technique.

Airborne distributed coherent aperture radar (ADCAR) obtain target coherent parameters (TCPs) and improve target detection performance significantly in the receive-coherent (RC) stage by multiple physically isolated airborne radars with orthogonal waveforms. However, the heterogeneous clutter with unknown power arising from imperfect orthogonal waveforms, and the random phase errors (RPE) introduced by phase synchronization between nodes, all of which cause severe degradation of detection performance. In this paper, we propose a RC signal model of ADCAR in cluttered environments under the RPE modeling by the von Mises distribution. Then, a two-step RC generalized likelihood ratio test (GLRT) detector with a simplified expectation maximization (EM) algorithm (2S-RVEM-G) is proposed, which iteratively solves for the unknown parameters under the alternative hypotheses by the EM algorithm to achieve target detection of ADCAR under heterogeneous clutter and RPE. Then, a detection strategy without prior information is proposed to enhance the proposed algorithm’s feasibility further. Finally, experimental results indicate that the target detection performance of 2S-RVEM-G is better than the traditional detectors, and the proposed strategy also ensures decent performances without prior information, proving the proposed method’s effectiveness.

Exact estimation of space object attitude parameters is a great challenge. The effectiveness of conventional attitude estimation approaches based on target sizes suffers a significant reduction when occlusion exists. This paper proposes an innovative approach to estimate the attitude parameters for space objects based on inverse synthetic aperture radar (ISAR) image sequences. The formulation for nonlinear size constraints (NSC) is developed by accounting for the characteristics of object size variation in ISAR image sequences. The multi-start framework for global optimization and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) based quasi-Newton iterative method are combined with and used for more accurate estimation of space object's attitude parameters. Furthermore, the Cramer-Rao lower bound (CRLB) of attitude parameter estimates is derived. Comparative experiments demonstrate the effectiveness and robustness of the proposed method.

Extended object tracking, which can obtain rich information about the object's shape, size, and orientation, has received widespread attention. The modeling method based on support functions (SFs) or extended Gaussian images (EGIs) needs the down-range measurement and cross-range measurement of the object to estimate the information about the object's extent. Down-range and cross-range refer to the projection lengths of the object along and perpendicular to the radar line of sight (LOS), respectively. The down-range measurement can be generated from the high range resolution profile (HRRP). However, in some radar applications, limited by the resolution capability in cross-range, the cross-range measurements are unavailable. To track extended objects only using down-range measurements, this article analyzes the observability of the extended object in detail. The analysis results indicate that the observability of the extended object is affected by the dimensions of range extent measurement, and the required dimensions of the range extent measurement for extended object tracking vary under different motion modes. According to the analysis results, when the object performs coordinate turn motion, extended object tracking can be achieved using only down-range measurements. The results of simulation experiments confirm this conclusion. The analysis results presented in this article clarify the measurement conditions required for extended object tracking and expand the application scope of tracking algorithms based on the SF and EGI modeling.

Radar anti-jamming is essential for the secure operation of radar systems in hostile environments, and accurate recognition of radar jamming types is critical. With increasing environmental noise and more diverse jamming patterns, recognizing radar jamming becomes increasingly challenging. To address this problem, this paper presents a diffusion model and Dempster-Shafer (D-S) embedded Swin Transformer integrated framework (DS-Diff-SwinT). This approach involves generating a time-frequency distribution map from jamming signals using the Choi-Williams distribution (CWD) and subsequently utilizing the swin transformer to capture multi-scale features within different receptive fields. Finally, a D-S fusion algorithm is embedded within the model to integrate recognition confidence across these fields, yielding the final recognition outcome. Experimental results on eight radar jamming categories demonstrate that this method outperforms traditional approaches in recognition performance.

In the domain of high-speed target processing using Agile Frequency Radar (AFR), a coherent accumulation algorithm based on the Agile Frequency Radon-Fourier Transform (AFRFT) has been developed. However, when the synthesized bandwidth is large and there is a lack of prior target information, the real-time performance of this algorithm can significantly degrade. Additionally, multiple scattering centers under wide-band conditions can hinder the detection of extended targets. To address these challenges, this paper proposes a Reorder and Segmentation AFRFT (RS-AFRFT) algorithm. This algorithm first sorts all pulses according to their frequencies, then segments the rearranged pulses to widen the search steps. Finally, it completes the inter-pulse accumulation by combining intra-segment AFRFT processing with inter-segment fusion processing. Theoretical analysis and simulations based on real electromagnetic data confirm the superior performance of the proposed algorithm. Compared to the traditional AFRFT algorithm, the RS-AFRFT method not only effectively reduces computational complexity but also improves target detection performance by integrating multiple scattering centers of extended targets.

Phase comparison monopulse technology is a widely used method for obtaining subbeam-level accuracy in angle estimates by processing the complex amplitude ratio of sum-anddifference beams. However, with increases in bandwidth and observation time, the interaction among multiple closely-spaced targets can significantly reduce the accuracy of angle estimates. To address these challenges, this paper proposes a direction-of-arrival (DOA) estimator named 'Classify-Track' for radar operating in long-time coherent integration (LTCI) mode. Initially, we utilize time-varying radial delay features (TRDF) to match unresolved scattering centers across both fast and slow time. Subsequently, we employ the timevarying angle tracking function (TATF) to estimate time-varying angles by searching over slow time and integrating multiple scattering centers. This method, diverging from traditional monopulse techniques, is based on the target motion model and effectively utilizes the angle difference of extended targets at various slow times. Additionally, to reduce computational complexity, we also present a rank-reduced version of TATF (RR-TATF) and provide guidelines for selecting specific hyperparameters within the method. Finally, multiple simulation experiments were conducted using real electromagnetic data from unmanned aerial vehicles (UAVs). The results of these simulations confirm the superior performance of our estimator in various signal-to-noise ratio scenarios.

AbstractThe interrupted‐sampling repeater jamming (ISRJ) is an effective kind of intro‐pulse coherent jamming based on digital radio frequency memory. It appears as a group of false targets that are difficult to distinguish on the range profile after pulse compression, which seriously affects the target identification and tracking. According to the coherence between ISRJ and real target echo and the waveform discontinuity caused by intermittent interception, a new method is proposed for ISRJ parameter estimation and jamming suppression based on block sparse recovery. To ensure the effectiveness of sparse recovery, the transmitted signal is divided into several random phase‐encoded sub‐pulses. Firstly, the pulse‐number and time‐delay of the received echo are estimated by block orthogonal matching pursuit. Then, the jamming slices are identified based on the sampling duty ratio and the Doppler frequency is estimated through matching. Finally, according to the parameter estimation results, the jamming slices are reconstructed to eliminate ISRJ. Numerical experiments in two typical scenarios have shown that this method can effectively suppress ISRJ. Especially in the scenario of high jamming duty ratio, this method exhibits good anti‐jamming performance.

Interrupted-sampling repeater jamming (ISRJ) is a type of intra-pulse coherent jamming that poses a significant threat to radar detection and tracking of targets. This paper proposes an ISRJ suppression method based on frequency agile waveform and sparse recovery, starting from the temporal discontinuity and modulation characteristics of ISRJ. This method is particularly suitable for scenarios with high jamming duty ratio (JDR) and high jammer sampling duty ratio (SDR). By dividing the transmitted waveform into sub-pulses with different carrier frequencies and applying a two-round block sparse algorithm, the method accurately recovers three parameters of ISRJ, achieving effective jamming identification, reconstruction, and cancellation. Additionally, a target detection technique based on robust sparse recovery is proposed, significantly improving the stability and accuracy of target detection. Comparative experimental results conducted in three scenarios confirm the effectiveness and superiority of this method under high JDR and SDR conditions.

The dechirp beamformer (DEBF), designed for dechirp systems without relying on delay lines, offers significant advantages such as ease of engineering implementation and excellent wideband wide-angle scanning performance. Consequently, it has found widespread use in imaging radars based on linear frequency modulation (LFM) waveforms. However, when applied to hyperbolic frequency modulation (HFM) waveforms, this beamformer experiences a notable reduction in beam gain at large scanning angles due to phase and frequency setting errors, which limits the performance of the imaging system. To address this issue, this paper proposes a subarray-level HFM-DEBF based on higher-order Taylor expansion. By approximating the complex nonlinear nature of HFM signals as a sum of multiple linear polynomials using higher-order Taylor expansion, phase and frequency compensation values can be accurately set according to these approximate polynomials, ensuring high beam gain across all pointing directions. Finally, several simulation experiments were conducted, validating the superior performance of the proposed beamformer.

Mainlobe dense false target jamming has brought great challenges to current radar detection due to its multi-dimensional flexible modulation capability. To this end, many effective solutions have been proposed in recent years. However, the performance of most existing methods will be largely affected in strong noise environment. In actual scenarios, strong noise environments are unavoidable. To solve this problem, this paper proposes a mainlobe dense false target jamming identification method for strong noise environments based on two-dimensional (2D) sparse recovery. First, the angle and time-delay parameters of the pulse are extracted by 2D sparse recovery. Then, the jamming identification is completed using the space-time joint feature difference within a single pulse repetition interval (PRI). On this basis, we also provide a jamming reconstruction and cancellation scheme for strong jamming environments. Compared with existing methods, the proposed method achieves accurate identification and suppression of jamming within a single PRI by effectively utilizing the spatial information of the source. It exhibits excellent robustness to strong noise and jamming environments. Additionally, it has the advantage of significantly reducing computational costs, thereby increasing overall efficiency and practicality. Numerical simulation experiment results verify the effectiveness of the proposed method.

Incorporating Doppler information into a hybrid active-passive radar system can effectively enhance target tracking performance. However, in practical tracking scenarios, using a low pulse repetition frequency can lead to Doppler ambiguity. Ambiguous Doppler measurements not only fail to improve target tracking accuracy but also significantly degrade overall tracking performance. To utilize accurate target Doppler information, this paper proposes a tracking method with Doppler ambiguity resolution based on a Gaussian mixture probability hypothesis density (GM-PHD) filter for penetration target tracking in a hybrid active-passive radar system. This method achieves joint processing of multi-target tracking and Doppler ambiguity resolution. the method utilizes radar’s position measurements for filtering to obtain an estimated value of the target’s radial velocity. It then calculates potential target radial velocity values based on ambiguous Doppler measurements and selects the radial velocity value with the maximum likelihood as the Doppler measurement without ambiguity. After Doppler measurements are unambiguous, the target states are updated again. Simulation experiments demonstrate that the GM-PHD filter with Doppler ambiguity resolution can ensure radar system tracking performance.

System-in-Package(SIP) technology plays an important role in developing highly integrated RF microsystem. Conventionally, silicon bridge was mainly built for connection of SoCs to transmit signals. In this paper, a silicon bridge structure with functions of package and mixed signals transmission for RF microsystem is presented. By this means, chips used for RF microsystem can be arranged with optimized footprint. Multi-layer Through-Silicon-Vias(TSV) interposers are stacked as silicon bridge for package and RF/DC mixed signals transmission. Metamaterials is also introduced in silicon bridge for reducing Electromagnetic Interference(EMI) of RF Microsystem. As mentioned above, the proposed silicon bridge structure can be used for compact and highly reliable RF system modules.