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To address the challenges in tracking complex maneuvering extended targets, an effective maneuvering extended target tracking method was proposed for irregularly shaped star-convex using a Transformer network. Initially, the alpha-shape algorithm was used to model the variations in the star-convex shape. In addition, a recursive approach was proposed to estimate the irregular shape of an extended target by detailed derivation in the Bayesian filtering framework. This approach accurately estimated the shape of a static star convex extended target. Moreover, through the structural redesign of the target state transition matrix and the real-time estimation of the maneuvering extended target’s state transition matrix using a transformer network, the accurate tracking of complex maneuvering targets was achieved. Furthermore, the real-time tracking of star convex maneuvering extended targets was achieved by fusing the estimated shape contours with motion states. This study focused on constructing certain complex maneuvering extended target tracking scenarios to assess the performance of the proposed method and the comprehensive estimation capabilities of the algorithm considering both shapes and motion states using multiple performance indicators.

An airborne bistatic radar working in downward-looking mode confronts two major challenges for low-altitude target detection. One is range cell migration (RCM) and Doppler migration (DM) resulting from the relative motion of the radar and target. The other is the non-stationarity characteristic of clutter due to the radar configuration. To solve these problems, this paper proposes a joint implementation method based on sub-aperture processing to achieve clutter suppression and coherent maneuvering target detection. Specifically, clutter Doppler compensation and sliding window processing are carried out to realize sub-aperture space–time processing, removing the clutter non-stationarity resulting from the bistatic geometric configuration. Thus, the output matrix of clutter suppression in the sub-aperture could be obtained. Then, the elements with the same phase of this matrix are superimposed and rearranged to achieve the reconstructed 2-D range-pluse echo matrix. Next, the aperture division with respect to slow time is conducted and the RCM correction based on modified location rotation transform (MLRT) and coherent integration (CI) are realized within each sub-aperture. Finally, the matched filtering process (MFP) is applied to compensate for the RCM/DM among different sub-apertures to coherently integrate the maneuvering target energy of all sub-apertures. The simulation and measured data processing results prove the validity of the proposed method. [ABSTRACT FROM AUTHOR]

Targets faced by inverse synthetic aperture radar (ISAR) are often non-cooperative, with target maneuvering being the main manifestation of this non-cooperation. Maneuvers cause ISAR imaging results to be severely defocused, which can create huge difficulties in target identification. In addition, as the ISAR bandwidth continues to increase, the impact of migration through resolution cells (MTRC) on imaging results becomes more significant. Target non-cooperation may also result in sparse aperture, leading to the failure of traditional ISAR imaging algorithms. Therefore, this paper proposes an algorithm to realize MTRC correction and sparse aperture ISAR imaging for maneuvering targets simultaneously named whale optimization algorithm–fast iterative shrinkage thresholding algorithm (WOA-FISTA). In this algorithm, FISTA is used to perform MTRC correction and sparse aperture ISAR imaging efficiently and WOA is adopted to estimate the rotational parameter to eliminate the effects of maneuvering on imaging results. Experimental results based on simulation and measured datasets prove that the proposed algorithm implements sparse aperture ISAR imaging and MTRC correction for maneuvering targets simultaneously. The proposed algorithm achieves better results than traditional algorithms under different signal-to-noise ratio conditions. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a fixed‐time cooperative guidance law for leader‐following missiles, comprising one leader missile with the target seeker and several seeker‐less follower missiles. The aim is to achieve a simultaneous attack on a maneuvering target at desired impact angles. First, a guidance law with impact angle control for the leader missile against a maneuvering target is proposed based on nonsingular fast terminal sliding mode (NFTSM) control algorithm. Then, the design of cooperative guidance law for the follower missiles is composed of two parts: along the follower‐to‐leader line of sight (LOS) direction, the guidance command derived from bi‐homogeneous property is designed to ensure that the follower‐leader ranges keep proportional consensus with the range‐to‐go of the leader missile, thus avoiding the estimation of time‐to‐go (tgo$$ {t}_{go} $$); in the normal follower‐to‐leader LOS direction, considering the relative impact angle constraints which is determined by the leader LOS angle, the guidance command is proposed based on predefined‐time sliding mode control method. What's more, a distributed fixed‐time observer is designed for the follower missiles to compensate for unobtainable leader missile information. The fixed‐time stability of the proposed methods is demonstrated using the Lyapunov theory and bi‐homogeneous property. Finally, simulation results confirm the effectiveness and superiority of the proposed fixed‐time cooperative guidance law with leader‐following strategy. [ABSTRACT FROM AUTHOR]

An airborne bistatic radar working in downward-looking mode confronts two major challenges for low-altitude target detection. One is range cell migration (RCM) and Doppler migration (DM) resulting from the relative motion of the radar and target. The other is the non-stationarity characteristic of clutter due to the radar configuration. To solve these problems, this paper proposes a joint implementation method based on sub-aperture processing to achieve clutter suppression and coherent maneuvering target detection. Specifically, clutter Doppler compensation and sliding window processing are carried out to realize sub-aperture space–time processing, removing the clutter non-stationarity resulting from the bistatic geometric configuration. Thus, the output matrix of clutter suppression in the sub-aperture could be obtained. Then, the elements with the same phase of this matrix are superimposed and rearranged to achieve the reconstructed 2-D range-pluse echo matrix. Next, the aperture division with respect to slow time is conducted and the RCM correction based on modified location rotation transform (MLRT) and coherent integration (CI) are realized within each sub-aperture. Finally, the matched filtering process (MFP) is applied to compensate for the RCM/DM among different sub-apertures to coherently integrate the maneuvering target energy of all sub-apertures. The simulation and measured data processing results prove the validity of the proposed method.

Targets faced by inverse synthetic aperture radar (ISAR) are often non-cooperative, with target maneuvering being the main manifestation of this non-cooperation. Maneuvers cause ISAR imaging results to be severely defocused, which can create huge difficulties in target identification. In addition, as the ISAR bandwidth continues to increase, the impact of migration through resolution cells (MTRC) on imaging results becomes more significant. Target non-cooperation may also result in sparse aperture, leading to the failure of traditional ISAR imaging algorithms. Therefore, this paper proposes an algorithm to realize MTRC correction and sparse aperture ISAR imaging for maneuvering targets simultaneously named whale optimization algorithm–fast iterative shrinkage thresholding algorithm (WOA-FISTA). In this algorithm, FISTA is used to perform MTRC correction and sparse aperture ISAR imaging efficiently and WOA is adopted to estimate the rotational parameter to eliminate the effects of maneuvering on imaging results. Experimental results based on simulation and measured datasets prove that the proposed algorithm implements sparse aperture ISAR imaging and MTRC correction for maneuvering targets simultaneously. The proposed algorithm achieves better results than traditional algorithms under different signal-to-noise ratio conditions.

• A novel DRL-based decision approach for simultaneously conducting the coordinated attack of the maneuvering target and the evasion of interceptors. • A MOMDP containing multi-step history states is constructed and further transformed into a first-order MDP with memory extraction, which integrates the spatio-temporal coupling properties among missiles, interceptors, and targets while supporting efficient sequential decision-making. • Domain-knowledge-assisted pre-training and reward functions are constructed to speed up the convergence of the training process. This paper presents an open and interesting issue for missiles, i.e., achieving collaborative parameters constrained cooperative guidance, despite the interference of pursing interceptors (INTs) and the maneuvering target, by the fact that the target-missile-interceptor (TMI) engagement induces their complex and time-varying relationships. The Memory-Extraction-based Soft-Actor-Critic (ME-SAC) approach is proposed, which enhances the collaborative performance of missiles by implicitly extracting coupling motion characteristics among TMI from historical state, achieving the joint optimization of situation awareness and strategy. Firstly, the cooperative guidance task is formulated as a multi-order Markov decision process (MOMDP) to better represent the dynamic evolution of engagement, and a memory-extraction process is introduced to alleviate the curse of dimensionality. Secondly, a memory-decision-oriented maximum entropy framework combined with memory update modules is designed for enhancing strategy search ability. Then, a domain-knowledge-based pre-training is implemented to improve convergence speed. Finally, in simulation evaluation with various scenarios, the proposed ME-SAC shows more promising than the typical DRL-based and model-based algorithms in task success rate, learning efficiency, and adaptability. [ABSTRACT FROM AUTHOR]

The motion complexity of maneuvering target causes the estimation uncertainty of target motion model, resulting in state estimation error. Especially for strong maneuvering target, the drastic change of target motion models makes the tracking methods hard to adapt and provide accurate state estimation promptly. To solve the state estimation problem of strong maneuvering targets, we propose a new transformer maneuvering target tracking model based on deep learning, named TrMTT model. The TrMTT model uses a new residual mapping between the observation trajectory and the real trajectory to estimate the target states, and is composed of the encoder and decoder branches while the two have the same input of observation trajectory. The encoder extracts the self-attention information for the input at each layer while the decoder implements cross-attention extraction and fusion between features in different layers, thus providing more correlation information between states for learning the transition law of rapidly changing states. Moreover, we propose an input module before the encoder–decoder structure to code the state features of the observation trajectory, and apply two kinds of normalization layers in the input module and the encoder–decoder structure, to project the input into a feature space which facilitates extracting the correlation information between states. Simulation results show that the proposed TrMTT model is superior in performance for maneuvering target tracking compared with other existing approaches. • A new residual mapping is built for giving real states and accelerating training. • The proposed TrMTT model inserts correlation between states into state features. • An input module is proposed to code the state features of observation trajectory. • The TrMTT model can notably reduce the estimation delay. [ABSTRACT FROM AUTHOR]