Maneuvering Target Research Articles

Dynamic Self‐Triggered Parallel Control of Guidance Intercept Systems With Constrained Inputs via Adaptive Dynamic Programming

ABSTRACTThis paper introduces an anti‐saturation optimization algorithm based on dynamic self‐triggered adaptive dynamic programming (ADP) for bounded acceleration guidance interception. By establishing a nonlinear input‐constrained guidance intercept and control system, the smooth bounded function is used to constrain the system input to ensure that the system operates in a controllable range. Subsequently, the appropriate performance function that can accurately reflect the system is designed. In alignment with the advantages of parallel control and ADP, a group of parallel systems are constructed by modeling the derivation of control input. A self‐learning control framework is explored, facilitating virtual‐actual interaction and mutual reinforcement between multiple controllers, optimizing the management of the interception system. Furthermore, event‐triggered control (ETC) policies are devised, which can be updated only when needed by setting trigger conditions, thus saving data resources. The stability proof of the closed‐loop system is given to ensure that the system can keep stable operation when the trigger condition is satisfied. On this basis, a dynamic self‐triggered control (DSTC) with soft computing is put forward, enabling trigger instant calculation without real‐time system monitoring and further extending the trigger interval. Simulation results evidence that the devised guidance scheme can intercept the maneuvering target at a reduced expenditure of communication resources.

Cognitive Radar Waveform Selection for Low-Altitude Maneuvering-Target Tracking: A Robust Information-Aided Fusion Method

In this paper, we introduce an innovative interacting multiple-criterion selection (IMCS) idea to design the optimal radar waveform, aimingto reduce tracking error and enhance tracking performance. This method integrates the multiple-hypothesis tracking (MHT) and Rao–Blackwellized particle filter (RBPF) algorithms to tackle maneuvering First-Person-View (FPV) drones in a three-dimensional low-altitude cluttered environment. A complex hybrid model, combining linear and nonlinear states, is constructed to describe the high maneuverability of the target. Based on the interacting multiple model (IMM) framework, our proposed IMCS method employs several waveform selection criteria as models and determines the optimal criterion with the highest probability to select waveform parameters. The simulation results indicate that the MHT–RBPF algorithm, using the IMCS method for adaptive parameter selection, exhibits high accuracy and robustness in tracking a low-altitude maneuvering target, resulting in lower root mean square error (RMSE) compared with fixed- or single-waveform selection mechanisms.

An optimal parameterized Newton-type structure-preserving doubling algorithm for impact angle guidance-based 3D pursuer/target interception engagement

The proposed strategy, finite-time state-dependent Riccati equation (FT-SDRE)-based impact angle guidance, is generally employed to solve the 3D pursuer/target interception model with fixed lateral accelerations. This article expands its application to a general scenario where the lateral acceleration of a target may change. To achieve this, we approximate the accelerations of the azimuth and elevation angles of the target in the inertial frame via second-order finite difference schemes and develop a high-performance FT-SDRE algorithm with structure-preserving doubling algorithms (SDAs). As a result, the update frequency of the controller can be increased, and better guidance of the pursuer can be obtained to address the high maneuverability of the target during the entire interception procedure. At every state of the FT-SDRE, a modified Newton–Lyapunov method is employed to solve the continuous algebraic Riccati equation (CARE), and a new simplified SDA with adaptive optimal parameter selection is proposed for solving the associated Lyapunov equation. Our numerical results demonstrate that the FT-SDRE algorithm accelerated by our proposed methods is approximately three times faster than the FT-SDRE algorithm, in which the MATLAB functions icare and lyap are used to solve the CARE and the Lyapunov equation, respectively, throughout the entire interception procedure. In other words, the control frequency can be increased threefold. In our benchmark cases where the target maneuvers with nonlinear lateral acceleration, the target can be intercepted earlier via the proposed FT-SDRE algorithm.

Adaptive interacting multiple model particle filter based on a dual-pattern bat algorithm for maneuvering target tracking

The interacting multiple model particle filter (IMM-PF) is a filtering method commonly used for nonlinear non-Gaussian radar systems. Its transfer probabilities are usually fixed and dependent on prior information. When tracking maneuvering targets, the IMM-PF may cause excessive tracking errors due to the model switching lag. In addition, the particle impoverishment caused by the resampling of the particle filter seriously affects the filtering accuracy. Therefore, the IMM-PF has difficulty meeting the accuracy and speed requirements of modern high-performance radar target tracking systems. To address these problems, an adaptive interacting multiple model particle filter based on the dual-pattern bat algorithm (ADIMM-DPBA-PF) is proposed for tracking maneuvering targets under nonlinear and non-Gaussian conditions. First, the adaptive model switching mechanism is established to adjust the transition probabilities using the model probability posterior information at consecutive times, improving the model switching efficiency. Second, a particle filter based on the dual-pattern bat algorithm (DPBA-PF) is proposed. The filter exploits global search and local search strategies to optimize the particles and intelligently move the particles to the high likelihood region. Finally, a particle filter based on the dual-pattern bat algorithm is used to form the adaptive interacting multiple model method. The experimental results show that the proposed algorithm has better comprehensive performance than the IMM-PF.

The Application of a Modified Adaptive CS Model in Tracking and Predicting Maneuvering Targets on the Airport Surface

The Application of a Modified Adaptive CS Model in Tracking and Predicting Maneuvering Targets on the Airport Surface

Distributed cooperative guidance strategy based on virtual negotiation and rolling optimization

The problem of multi-UAV cooperative guidance under target maneuvering conditions is studied, and a distributed cooperative guidance strategy based on virtual negotiation mechanism and rolling horizon optimization is proposed. Firstly, in order to improve the time coordination and guidance control effect at the same time, local coordination variables and objective functions are designed under the rolling horizon optimization framework, and a multi-constrained cooperative guidance optimization model in a limited time domain is constructed; secondly, in order to enhance the robustness of the guidance system to maneuvering targets, a disturbance observer based on nonlinear autoregressive neural network is designed; thirdly, for the control input coupling between UAVs, a virtual negotiation mechanism is proposed to achieve synchronous decision-making and eliminate internal "differences", and the root mean square propagation algorithm of Nesterov momentum is used to design the guidance command generation strategy; finally, digital simulation and semi-physical simulation experiments are carried out respectively. The results show that the proposed guidance strategy can cope with various interferences and improve the cooperative guidance effect through online prediction and optimization, and is expected to be further applied and tested in actual tasks.

EKF-based parameter estimation method for radar maneuvering target with unknown time information

Moving target detection (MTD) is a research hotspot in radar signal processing. Generally, the time information of non-cooperative moving targets entering and leaving a radar coverage area is unknown, which would lead to severe performance loss for target parameter estimation, detection, and imaging. Unlike our previous research work, this paper addresses the motion parameters estimation and refocusing problem for a radar maneuvering target with unknown entry and departure time. A computationally efficient method that utilizes extended Kalman filtering (EKF) for phase tracking is proposed to estimate the entry and departure times. The proposed method first performs range cell migration correction (RCMC) on the pulse compression echo signal. Then, the maneuvering target signal is modeled as a polynomial phase signal (PPS) and utilizes the EKF to construct a binary state-space equation for polynomial phase tracking. Finally, by comparing the phase tracking results of the noise cell and the signal cell, one can derive estimates for the entry/departure time and motion parameters. Compared with existing methods, the proposed method avoids multi-dimension searching on the parameter space, so it has a prominent advantage in computational complexity. Moreover, the core of the proposed method lies in tracking the polynomial phase, which is not constrained by the order of target motion, and has wider applicability in practice. Both simulated and public radar data are used to validate the effectiveness of the proposed method.

Reentry vehicle fixed-time terminal guidance and attitude control with impact angle constraints

The motivation of this paper is to handle the terminal guidance and attitude control problem for the unpowered reentry vehicle in the diving phase, which is crucial for interception to the target. The most important objectives of this study are to intercept the maneuvering target with the miss distance and impact angle error as small as possible. First, the second-order derivatives of the aerodynamic angles are derived with the fin deflections as control input, which is objective to avoid the tracking of reentry vehicle body angle rate command in the traditional methods. Second, a novel fixed-time time-varying parameter nonsingular terminal sliding mode guidance law is proposed to improve the convergence speed of line-of-sight angle rate, which can avoid control input singularity without switching to the polynomial form when the error approaches the origin. Thirdly, a new global time-varying sliding mode is developed to design the attitude controller, with the predefined fixed convergence time and analytical solution. The system states are proved to be fixed-time convergence based on the Lyapunov stability theory. Six-degree-of-freedom flight simulations are conducted to validate the superiority of the proposed algorithm in terms of miss distance, impact angle error, intercepting time and control input energy.

Terminal Phase Guidance Law Against Maneuvering Targets: Adaptive State-Dependent Differential Riccati Equation Approach

This paper provides a methodology to address the terminal guidance issue for a highly maneuverable target by formulating the problem as a nonlinear autonomous system with matched term uncertainty. This method involves the utilization of the State-Dependent Differential Riccati Equation (SDDRE) technique to develop an optimal adaptive strategy. Since the SDDRE is vulnerable to variations in the model, the adaptive framework has been devised to address the issue of system uncertainty. The utilization of Laguerre Functions (LFs) is employed to characterize the target acceleration due to its arbitrary shape, while the adaptation law computes the coefficients associated with this representation. The effectiveness of the proposed guidance law has been demonstrated in numerical simulation for various target maneuvers. In addition, the effect of the type and number of basis functions on the estimation had been studied. Ultimately, a sensitivity analysis of design parameters is presented and a comparison is provided between the effectiveness of the proposed guidance law and some of the methods that have been proposed in the literature.

Memory-extraction-based DRL cooperative guidance against the maneuvering target protected by interceptors

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.

Maritime ISAR detection and tracking algorithm for multiple maneuvering extended vessels in heavy-tailed clutter using SK-MM-Sub-RMM-MB-TBD filter

Multiple extended objects tracking (EOT) tasks play an important role in computer vision and engineering applications of artificial intelligence. In particular, nonlinear marine object imaging and tracking has received an increasing amount of attention due to its enormous application potential in the field of marine engineering, Autonomous Underwater Vehicles, and Remotely Operated Vehicles. Under high sea state, ship-EOTs perform complex maneuvering movements due to strong disturbances such as sea winds and sea waves. In this paper, we exploit emergent maneuvering EOTs (M-EOTs) methodologies in heavy-tailed clutter. We propose a M-EOT procedure in real-time scenario based on the popular multi-Bernoulli (MB)-TBD filter in maritime inverse synthetic aperture radar (ISAR) systems, and in particular, we describe the extended ship target state through the random matrices model (RMM). In RMM, scatter centers are distributed symmetrically around the M-EOT's centroid. However, in ship M-EOT scenario, the distribution over the whole object is not symmetrical, but distributed and skewed in some portions while a target maneuvers. To solve this problem, a novel robustness observation model is represented by using skewed (SK) non-symmetrically normal distribution and multiple model (MM) MB-TBD with more than one ellipse. Simulation and experimental results illustrate that the proposed SK-MM-Sub-RMM-MB-TBD filter outperforms the existing filters for M-EOTs.

Information fusion-based three-dimensional two-stage optimal cooperative predictive guidance law

A three-dimensional (3D) two-stage optimal cooperative predictive guidance law based on information fusion theory is proposed to intercept a highly maneuverable target. This guidance law integrates an Adaptive Predictive Horizon Nonlinear Model Predictive Control (ANMPC) method and a Distributed Anti-Saturation Extended State Observer (DASESO) algorithm. The combined approach aims to minimize the three-dimensional nonlinear zero-effort-miss. By considering zero-effort-miss, the guidance process can be divided into two stages: rapid convergence to maintain state and fine-tuning. The two-stage method allows missile fuel and computational resources to be allocated more targetedly. In the guidance process, a two-stage switching distance is established based on the initial conditions of the missiles and the target. The DASESO algorithm cooperatively processes information from each missile to accurately estimate the target's maneuvering acceleration. Subsequently, an ANMPC method is employed to generate the optimal guidance command. Additionally, the convexity of the optimization problem has been analyzed to ensure its feasibility in practical applications. Through numerical simulation and comparative analysis, it has been proven that this method can effectively intercept and optimize fuel use, even when dealing with highly maneuverable targets with maneuverability similar to that of the missiles.

IMM Filtering Algorithms for a Highly Maneuvering Fighter Aircraft: An Overview

The trajectory estimation of a highly maneuvering target is a challenging problem and has practical applications. The interacting multiple model (IMM) filter is a well-established filtering algorithm for the trajectory estimation of maneuvering targets. In this study, we present an overview of IMM filtering algorithms for tracking a highly-maneuverable fighter aircraft using an air moving target indicator (AMTI) radar on another aircraft. This problem is a nonlinear filtering problem due to nonlinearities in the dynamic and measurement models. We first describe single-model nonlinear filtering algorithms: the extended Kalman filter (EKF), unscented Kalman filter (UKF), and cubature Kalman filter (CKF). Then, we summarize the IMM-based EKF (IMM-EKF), IMM-based UKF (IMM-UKF), and IMM-based CKF (CKF). In order to compare the state estimation accuracies of the IMM-based filters, we present a derivation of the posterior Cramér-Rao lower bound (PCRLB). We consider fighter aircraft traveling with accelerations 3g, 4g, 5g, and 6g and present numerical results for state estimation accuracy and computational cost under various operating conditions. Our results show that under normal operating conditions, the three IMM-based filters have nearly the same accuracy. This is due to the accuracy of the measurements of the AMTI radar and the high data rate.

Generalized interacting multiple model Kalman filtering algorithm for maneuvering target tracking under non-Gaussian noises

The traditional interacting multiple model Kalman filtering algorithm (IMM-KF) can deal with the maneuvering target problem under Gaussian noise by soft switching among possible motion models. In practice, its performance is likely to degrade when handling non-Gaussian noise. We introduce the Gaussian mixture model (GMM) into the IMM-KF, and the GMM is utilized to model the non-Gaussian measurement noise as a mixture of multiple Gaussian probability densities with a certain probability. Then, a GIMM framework is proposed that enables accurate switching and fusion among multiple possible motion and noise models. And combined with Kalman filtering (KF), a GIMM-KF algorithm is proposed that enables accurate state estimation of maneuvering targets under non-Gaussian noise conditions. Subsequently, the provided simulations and experiments validate that the GIMM-KF algorithm outperforms existing methods in terms of accuracy, stability and robustness.

Prescribed-Time Maneuvering Target Closing for Multiple Fixed-Wing UAVs

Prescribed-Time Maneuvering Target Closing for Multiple Fixed-Wing UAVs

ISAR Imaging Analysis of Complex Aerial Targets Based on Deep Learning

Traditional range–instantaneous Doppler (RID) methods for maneuvering target imaging are hindered by issues related to low resolution and inadequate noise suppression. To address this, we propose a novel ISAR imaging method enhanced by deep learning, which incorporates the fundamental architecture of CapsNet along with two additional convolutional layers. Pre-training is conducted through the deep learning network to establish the mapping function for reference. Subsequently, the trained network is integrated into the electromagnetic simulation software, Feko 2019, utilizing a combination of geometric forms such as corner reflectors and Luneberg spheres for analysis. The results indicate that the derived ISAR imaging effectively identifies the ISAR program associated with complex aerial targets. A thorough analysis of the imaging results further corroborates the effectiveness and superiority of this approach. Both simulation and empirical data demonstrate that this method significantly enhances imaging resolution and noise suppression.

A DMPC-based three-dimensional cooperative guidance scheme with impact time and impact angle constraints

In this paper, a distributed model predictive control (DMPC)-based three-dimensional (3D) cooperative guidance scheme is proposed for multiple interceptors with controllable thrust and constrained acceleration, to simultaneously attack a maneuvering target at desired terminal angles. The cooperative guidance scheme consists of a fractional power function-based extended state observer (FPESO) for disturbance estimation, and a DMPC controller to generate the control actions. By introducing the assumed state trajectory, the DMPC controller (of each interceptor) can simultaneously employ the zebra optimization algorithm (ZOA) to solve the constrained optimization problem, and generate the real-time guidance commands with no need of any global information. For restricting the uncertain deviation between the assumed state trajectory and the actual one, a compatibility constraint is designed, and the terminal ingredients are developed for coupling and decoupling stage costs, respectively. By applying the above designed constraints and the terminal ingredients, the recursive feasibility of the resulting optimization problem and the closed-loop stability of multi-interceptor system are both ensured. Through numerical simulations, it is verified that the proposed cooperative guidance scheme can achieve cooperative interception with impact time and angle constraints in various scenarios, and has advantageous performance.

Underwater maneuvering target motion analysis using delayed azimuth angles

Accurate position information of an underwater target is the premise of successfully executing underwater missions, and this paper focuses on underwater target motion analysis (TMA) using delayed azimuth angles. Considering that the underwater sound speed (around 1500 m/s) is much smaller than the speed of light (3 × 108 m/s), the underwater TMA needs to adopt the non-stop model considering the effect of the target movement during signal propagation on state estimation, instead of the traditional go-stop model ignoring the target movement during signal propagation. The existing non-stop model is based on a constant-velocity target, and the model mismatch will make the estimated results biased when it is applied to a maneuvering target. In this paper, we focus on the constant-acceleration (CA) target and propose two methods to solve this problem. One is to extend the non-stop model to the case of a constant-acceleration target. We build the corresponding least squares (LS) estimator and derive the expression of the root mean square error (RMSE) of the LS solution. The other is to directly offset the estimation bias of the CA go-stop model and a bias-compensated algorithm is proposed to improve the performance. Simulations prove that the proposed methods can result in a smaller RMSE than the traditional CA go-stop model. Their RMSEs decrease with the number of measurements and observers and increase with random angle errors. When the number of measurements is small, the RMSE of the established CA non-stop model is smaller than that of the bias-compensated algorithm. As the number of measurements increases, their estimation precisions become gradually equal. The running time of the proposed methods increases with the number of measurements and observers. When the number of observers is small, the bias-compensated algorithm has the advantage of needing less running time. In addition, the running time of the bias-compensated algorithm does not approximately change with the random angle errors, while that of the CA non-stop model increases with the random angle errors.

Maneuvering extended target tracking method based on transformer network

This research introduces a new approach to address the challenges related to inaccuracies in shape estimation and motion state tracking during the tracking of maneuvering extended targets. The method combines a novel neural network model with a cubature Kalman filter. Initially, the target’s shape variation is represented using a second-order autoregressive model, and Bayesian filtering is employed for target contour estimation in static environments. Subsequently, the kinematic state tracking of maneuvering targets is enhanced by modifying a neural network model based on Transformer architecture. Through the effective integration of these techniques, precise tracking of maneuvering extended targets is achieved, facilitating accurate estimation of irregular contour information while tracking the targets’ motion state in real-time. The efficacy of the proposed approach is further confirmed through various simulation scenarios, underscoring its suitability for tracking maneuvering extended targets.

Three-dimensional adaptive dynamic surface guidance law for missile with terminal angle and field-of-view constraints

In this paper, an adaptive dynamic surface (DSC) guidance law for missile is designed to intercept the maneuvering target with field-of-view (FOV) and terminal angle constraints in three-dimensional(3D) space, and the missile autopilot dynamics is considered. Firstly, the time-varying transformation function related to line of sight (LOS) is used to replace the FOV constraints, transforming the process-constrained control problem into the output-constrained control problem. Meanwhile, the 3D coupled relative kinematics model considering missile autopilot dynamics and maneuvering target acceleration is established. Secondly, a novel time-varying asymmetric barrier Lyapunov function (TABLF) with dead-zone characteristics is introduced to the adaptive dynamic surface guidance law design process to improve the robustness of parameter debugging. Thirdly, with the help of a nonlinear adaptive filter, the ‘explosion of complexity’ problem can be avoided effectively, which is caused by analytic computation of virtual signal derivatives. Furthermore, aiming at the problem of autopilot dynamic errors, target acceleration disturbances, and unmeasurable parameters in the model, a novel adaptive law is used to evaluate online. Then, the stability of the closed-loop system is rigorously proven using Lyapunov criteria. Ultimately, Numerical simulations with various constraints and comparison studies have been considered to show the feasibility and effectiveness of the proposed missile guidance law.