Pub Date : 2025-10-28DOI: 10.1109/LCSYS.2025.3626272
Mohammad Jeddi;Mohammad Hossein Khademi;Ali Khaki-Sedigh
Data-driven control methodologies design controllers directly from data without explicit system models. Among these methods, the Virtual Reference Feedback Tuning (VRFT) method uses offline data collected from an unknown system. However, challenges arise in selecting an appropriate reference model and in handling non-minimum phase (NMP) transmission zeros. This letter extends an optimal reference-model selection algorithm from single-input single-output (SISO) to multivariable systems and introduces a one-shot method for identifying and incorporating NMP transmission zeros. An appropriate cost function is formulated for multivariable systems, and evolutionary optimization techniques are applied for reference-model selection. Simulation results validate the effectiveness of the proposed approach, contributing to improved data-driven control design for multivariable systems.
{"title":"Data-Driven Control Based on Virtual Reference Feedback Tuning With Optimal Reference Model for Non-Minimum Phase Multivariable Systems","authors":"Mohammad Jeddi;Mohammad Hossein Khademi;Ali Khaki-Sedigh","doi":"10.1109/LCSYS.2025.3626272","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3626272","url":null,"abstract":"Data-driven control methodologies design controllers directly from data without explicit system models. Among these methods, the Virtual Reference Feedback Tuning (VRFT) method uses offline data collected from an unknown system. However, challenges arise in selecting an appropriate reference model and in handling non-minimum phase (NMP) transmission zeros. This letter extends an optimal reference-model selection algorithm from single-input single-output (SISO) to multivariable systems and introduces a one-shot method for identifying and incorporating NMP transmission zeros. An appropriate cost function is formulated for multivariable systems, and evolutionary optimization techniques are applied for reference-model selection. Simulation results validate the effectiveness of the proposed approach, contributing to improved data-driven control design for multivariable systems.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2459-2464"},"PeriodicalIF":2.0,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-27DOI: 10.1109/LCSYS.2025.3625957
Charis Stamouli;Leonardo F. Toso;Anastasios Tsiamis;George J. Pappas;James Anderson
We analyze the performance of policy gradient in multitask linear quadratic regulation (LQR), where the system and cost parameters differ across tasks. The main goal of multitask LQR is to find a controller with satisfactory performance on every task. Prior analyses on relevant contexts fail to capture closed-loop task similarities, resulting in conservative performance guarantees. To account for such similarities, we propose bisimulation-based measures of task heterogeneity. Our measures employ new bisimulation functions to bound the cost gradient distance between a pair of tasks in closed loop with a common stabilizing controller. Employing these measures, we derive suboptimality bounds for both the multitask optimal controller and the asymptotic policy gradient controller with respect to each of the tasks. We further provide conditions under which the policy gradient iterates remain stabilizing for every system. For multiple random sets of certain tasks, we observe that our bisimulation-based measures improve upon baseline measures of task heterogeneity dramatically.
{"title":"Policy Gradient Bounds in Multitask LQR","authors":"Charis Stamouli;Leonardo F. Toso;Anastasios Tsiamis;George J. Pappas;James Anderson","doi":"10.1109/LCSYS.2025.3625957","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3625957","url":null,"abstract":"We analyze the performance of policy gradient in multitask linear quadratic regulation (LQR), where the system and cost parameters differ across tasks. The main goal of multitask LQR is to find a controller with satisfactory performance on every task. Prior analyses on relevant contexts fail to capture closed-loop task similarities, resulting in conservative performance guarantees. To account for such similarities, we propose bisimulation-based measures of task heterogeneity. Our measures employ new bisimulation functions to bound the cost gradient distance between a pair of tasks in closed loop with a common stabilizing controller. Employing these measures, we derive suboptimality bounds for both the multitask optimal controller and the asymptotic policy gradient controller with respect to each of the tasks. We further provide conditions under which the policy gradient iterates remain stabilizing for every system. For multiple random sets of certain tasks, we observe that our bisimulation-based measures improve upon baseline measures of task heterogeneity dramatically.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2495-2500"},"PeriodicalIF":2.0,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-27DOI: 10.1109/LCSYS.2025.3626269
Mengxiang Zeng;Peng Lin
Most of the existing studies on position constraints integrate the constraint conditions into the agent dynamics. In contrast, the active constraints considered in this letter are implemented through control algorithms, ensuring that the agents’ states remain within their constraint sets without altering the system dynamics. Therefore, this letter explores the active-constrained consensus problem with nonconvex velocity and convex position constraints. The position constraints are fundamentally different from the velocity constraints, because the velocity constraint sets contain the origin, whereas the position constraint sets may not. The key difficulty is how to deal with the coupling between these two fundamentally different constraints. By performing a series of model transformations and utilizing the convexity of the system, it is demonstrated that the active-constrained consensus can be attained. Finally, simulation examples show the validity of the conclusions.
{"title":"Distributed Active-Constrained Consensus of Second-Order Multi-Agent Systems","authors":"Mengxiang Zeng;Peng Lin","doi":"10.1109/LCSYS.2025.3626269","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3626269","url":null,"abstract":"Most of the existing studies on position constraints integrate the constraint conditions into the agent dynamics. In contrast, the active constraints considered in this letter are implemented through control algorithms, ensuring that the agents’ states remain within their constraint sets without altering the system dynamics. Therefore, this letter explores the active-constrained consensus problem with nonconvex velocity and convex position constraints. The position constraints are fundamentally different from the velocity constraints, because the velocity constraint sets contain the origin, whereas the position constraint sets may not. The key difficulty is how to deal with the coupling between these two fundamentally different constraints. By performing a series of model transformations and utilizing the convexity of the system, it is demonstrated that the active-constrained consensus can be attained. Finally, simulation examples show the validity of the conclusions.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2471-2476"},"PeriodicalIF":2.0,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-24DOI: 10.1109/LCSYS.2025.3625473
Xiaofeng Zong;Xuping Hou;Fuke Wu;Xuerong Mao
Motivated by the proportional integral (PI) control theory of deterministic systems, this letter aims to establish the PI-type control theory for stochastic systems, including the standard PI control and the proportional fragment-integral (PFI) control. For the standard PI control of stochastic functional systems (SFSs), the mean square and almost sure stabilization criteria are investigated, and then the explicit design of the proportional gain and the integral gain is obtained. It is revealed that the proportional gain matrix can be firstly designed and then the negative definite integral gain matrix can be determined based on the design of the proportional gain matrix. For the PFI control, the joint design of the proportional gain and the fragment-integral gain is proposed. It is shown that the fragment integral can work positively for the stabilization. Finally, the numerical methods are also proposed to approximate the two-type closed-loop SFSs and the simulation examples are given to confirm the theoretical results.
{"title":"Stabilization of Stochastic Functional Systems via PI-Type Controls","authors":"Xiaofeng Zong;Xuping Hou;Fuke Wu;Xuerong Mao","doi":"10.1109/LCSYS.2025.3625473","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3625473","url":null,"abstract":"Motivated by the proportional integral (PI) control theory of deterministic systems, this letter aims to establish the PI-type control theory for stochastic systems, including the standard PI control and the proportional fragment-integral (PFI) control. For the standard PI control of stochastic functional systems (SFSs), the mean square and almost sure stabilization criteria are investigated, and then the explicit design of the proportional gain and the integral gain is obtained. It is revealed that the proportional gain matrix can be firstly designed and then the negative definite integral gain matrix can be determined based on the design of the proportional gain matrix. For the PFI control, the joint design of the proportional gain and the fragment-integral gain is proposed. It is shown that the fragment integral can work positively for the stabilization. Finally, the numerical methods are also proposed to approximate the two-type closed-loop SFSs and the simulation examples are given to confirm the theoretical results.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2429-2434"},"PeriodicalIF":2.0,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-22DOI: 10.1109/LCSYS.2025.3624185
Reid D. Smith;Andrew G. Alleyne
Iterative learning control (ILC) methods which track sets rather than a reference throughout an iteration, namely region-to-region (RTR) ILC and set-to-set (STS) ILC, have assumed that external disturbances are purely repetitive. However, in real-world applications, non-repetitive disturbances will likely be present, both on the state and output channels of the plant. These non-repetitive disturbances lead to challenges in ensuring that the sets for tracking will be achieved throughout each iteration. While STS ILC has been shown to outperform RTR ILC for purely repetitive disturbances, this letter develops a novel STS ILC architecture which incorporates feedback control and tightening of the sets to ensure that the sets are achieved despite the unknown, non-repetitive disturbances. A conservative bound for the necessary tightening of the sets is derived and a simulation-based case study demonstrates the novel STS ILC’s ability to achieve the sets while outperforming alternative ILC approaches.
{"title":"Set-to-Set Iterative Learning Control for Non-Repetitive Disturbances","authors":"Reid D. Smith;Andrew G. Alleyne","doi":"10.1109/LCSYS.2025.3624185","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3624185","url":null,"abstract":"Iterative learning control (ILC) methods which track sets rather than a reference throughout an iteration, namely region-to-region (RTR) ILC and set-to-set (STS) ILC, have assumed that external disturbances are purely repetitive. However, in real-world applications, non-repetitive disturbances will likely be present, both on the state and output channels of the plant. These non-repetitive disturbances lead to challenges in ensuring that the sets for tracking will be achieved throughout each iteration. While STS ILC has been shown to outperform RTR ILC for purely repetitive disturbances, this letter develops a novel STS ILC architecture which incorporates feedback control and tightening of the sets to ensure that the sets are achieved despite the unknown, non-repetitive disturbances. A conservative bound for the necessary tightening of the sets is derived and a simulation-based case study demonstrates the novel STS ILC’s ability to achieve the sets while outperforming alternative ILC approaches.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2441-2446"},"PeriodicalIF":2.0,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11214232","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-22DOI: 10.1109/LCSYS.2025.3624329
Tu Zhang;Guobao Zhang;Amr Alanwar;Yongming Huang
This letter investigates the privacy-preserving consensus for multi-agent systems under affine transformation. To avoid privacy exposure, the true system state is reformulated into a new auxiliary state by adopting an affine transformation. By employing the consensus compensator under the analysis framework of output consensus, the original privacy-preserving consensus of homogeneous multi-agent systems (MASs) is reformulated into the equivalent non-privacy-preserving output consensus of heterogeneous MASs. Resorting to the Lyapunov function method, the design parameters are obtained such that the exact consensus value, instead of mean-square consensus, is achieved over both finite and infinite time domains. A simulation is utilized to validate the supplied method.
{"title":"An Affine Transformation Method to Privacy-Preserving Consensus for Multi-Agent Systems","authors":"Tu Zhang;Guobao Zhang;Amr Alanwar;Yongming Huang","doi":"10.1109/LCSYS.2025.3624329","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3624329","url":null,"abstract":"This letter investigates the privacy-preserving consensus for multi-agent systems under affine transformation. To avoid privacy exposure, the true system state is reformulated into a new auxiliary state by adopting an affine transformation. By employing the consensus compensator under the analysis framework of output consensus, the original privacy-preserving consensus of homogeneous multi-agent systems (MASs) is reformulated into the equivalent non-privacy-preserving output consensus of heterogeneous MASs. Resorting to the Lyapunov function method, the design parameters are obtained such that the exact consensus value, instead of mean-square consensus, is achieved over both finite and infinite time domains. A simulation is utilized to validate the supplied method.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2435-2440"},"PeriodicalIF":2.0,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-20DOI: 10.1109/LCSYS.2025.3623658
Jiwei Wang;Simone Baldi;Wenwu Yu;Xiang Yin
Artificially introducing a delay in the observations of a system can be an effective mechanism to mask the system itself, with the goal to increase its opacity and thus its security. This letter investigates opacity in discrete event systems with delayed observations. We focus on two questions: how to verify opacity under delayed observations, and how to synthesize sensor activation policies that guarantee opacity under such delayed conditions. To address these questions, we first introduce the definition of opacity under delayed observation and develop a corresponding verification method. We then extend such analysis tool into a synthesis tool by proposing an optimization approach for designing sensor activation policies guaranteeing opacity under delayed observations. An example is used to illustrate the analysis and synthesis procedures.
{"title":"Enforcing Opacity in Discrete Event Systems via Delayed Observations","authors":"Jiwei Wang;Simone Baldi;Wenwu Yu;Xiang Yin","doi":"10.1109/LCSYS.2025.3623658","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3623658","url":null,"abstract":"Artificially introducing a delay in the observations of a system can be an effective mechanism to mask the system itself, with the goal to increase its opacity and thus its security. This letter investigates opacity in discrete event systems with delayed observations. We focus on two questions: how to verify opacity under delayed observations, and how to synthesize sensor activation policies that guarantee opacity under such delayed conditions. To address these questions, we first introduce the definition of opacity under delayed observation and develop a corresponding verification method. We then extend such analysis tool into a synthesis tool by proposing an optimization approach for designing sensor activation policies guaranteeing opacity under delayed observations. An example is used to illustrate the analysis and synthesis procedures.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2411-2416"},"PeriodicalIF":2.0,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145351999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-20DOI: 10.1109/LCSYS.2025.3623660
Karthik Shenoy;Arun D. Mahindrakar;Umesh Vaidya
This letter focuses on a safety-critical solution to equality-constrained nonlinear programming, where the cost and the constraints vary continuously over time. To address this problem, we propose a continuous-time dynamical system with a quadratic-program (QP)-based feedback. The control barrier equality is treated as the hard constraint in the QP to enforce safety, and the control Lyapunov inequality is treated as the soft constraint for tracking the non-stationary minimizer. We show that the tracking error dynamics is locally uniformly ultimately bounded, and we establish uniform exponential stability in the presence of feedforward prediction. As an application, we solve the time-varying Procrustes problem, which is a time-varying optimization problem on an embedded submanifold of the Euclidean space.
{"title":"Safe Time-Varying Nonlinear Programming With Equality Constraints","authors":"Karthik Shenoy;Arun D. Mahindrakar;Umesh Vaidya","doi":"10.1109/LCSYS.2025.3623660","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3623660","url":null,"abstract":"This letter focuses on a safety-critical solution to equality-constrained nonlinear programming, where the cost and the constraints vary continuously over time. To address this problem, we propose a continuous-time dynamical system with a quadratic-program (QP)-based feedback. The control barrier equality is treated as the hard constraint in the QP to enforce safety, and the control Lyapunov inequality is treated as the soft constraint for tracking the non-stationary minimizer. We show that the tracking error dynamics is locally uniformly ultimately bounded, and we establish uniform exponential stability in the presence of feedforward prediction. As an application, we solve the time-varying Procrustes problem, which is a time-varying optimization problem on an embedded submanifold of the Euclidean space.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2417-2422"},"PeriodicalIF":2.0,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145351972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-20DOI: 10.1109/LCSYS.2025.3623945
Alexis M. H. Teter;Abhishek Halder;Michael D. Schneider;Alexx S. Perloff;Jane Pratt;Conor M. Artman;Maria Demireva
From a stochastic control perspective, the Schrödinger bridge is a density-valued continuous curve parameterized by time that connects a given pair of initial and terminal probability densities via minimum effort controlled Brownian motion. The control-affine Schrödinger bridge extends this idea to a generic control-affine Itô diffusion, possibly with an additive state cost. In this letter, we recast the necessary conditions of optimality for the control-affine Schrödinger bridge problem as a two point boundary value problem for a quantum mechanical Schrödinger PDE with complex potential. This complex-valued potential is a generalization of the real-valued Bohm potential in quantum mechanics. Our derived potential is akin to the optical potential in nuclear physics where the real part of the potential encodes elastic scattering (transmission of wave function), and the imaginary part encodes inelastic scattering (absorption of wave function). The key takeaway is that the process noise that drives the evolution of probability densities induces an absorbing medium in the evolution of wave function. These results make new connections between control theory and non-equilibrium statistical mechanics through the lens of quantum mechanics.
{"title":"Control-Affine Schrödinger Bridge and Generalized Bohm Potential","authors":"Alexis M. H. Teter;Abhishek Halder;Michael D. Schneider;Alexx S. Perloff;Jane Pratt;Conor M. Artman;Maria Demireva","doi":"10.1109/LCSYS.2025.3623945","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3623945","url":null,"abstract":"From a stochastic control perspective, the Schrödinger bridge is a density-valued continuous curve parameterized by time that connects a given pair of initial and terminal probability densities via minimum effort controlled Brownian motion. The control-affine Schrödinger bridge extends this idea to a generic control-affine Itô diffusion, possibly with an additive state cost. In this letter, we recast the necessary conditions of optimality for the control-affine Schrödinger bridge problem as a two point boundary value problem for a quantum mechanical Schrödinger PDE with complex potential. This complex-valued potential is a generalization of the real-valued Bohm potential in quantum mechanics. Our derived potential is akin to the optical potential in nuclear physics where the real part of the potential encodes elastic scattering (transmission of wave function), and the imaginary part encodes inelastic scattering (absorption of wave function). The key takeaway is that the process noise that drives the evolution of probability densities induces an absorbing medium in the evolution of wave function. These results make new connections between control theory and non-equilibrium statistical mechanics through the lens of quantum mechanics.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2453-2458"},"PeriodicalIF":2.0,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-20DOI: 10.1109/LCSYS.2025.3623948
Jianwu Tao;Shiru Guo;Wenchao Ji
This letter focuses on estimating the position and velocity of a moving source in 3 dimension (3D) space, using hybrid angle of arrival (AOA) and time difference of arrival (TDOA) measurements observed by a single moving receiver. In order to characterize the motion of the relative range between source and receiver, the components of the relative velocity and acceleration are firstly represented in a spherical coordinate frame. Then, the relations between these components and AOA measurements are modeled together with the relations between the source localization and TDOA measurements. This unified model can be reformulated as a constrained least squares (LS) problem and a closed-form solution can be obtained by using the squared range difference LS (SRD-LS). Finally, the Cramer-Rao Bound (CRB) is derived for unified model. Numerical simulations show the validity of the proposed method.
{"title":"Air-Based Passive Localization for a Moving Source Using Hybrid AOA–TDOA","authors":"Jianwu Tao;Shiru Guo;Wenchao Ji","doi":"10.1109/LCSYS.2025.3623948","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3623948","url":null,"abstract":"This letter focuses on estimating the position and velocity of a moving source in 3 dimension (3D) space, using hybrid angle of arrival (AOA) and time difference of arrival (TDOA) measurements observed by a single moving receiver. In order to characterize the motion of the relative range between source and receiver, the components of the relative velocity and acceleration are firstly represented in a spherical coordinate frame. Then, the relations between these components and AOA measurements are modeled together with the relations between the source localization and TDOA measurements. This unified model can be reformulated as a constrained least squares (LS) problem and a closed-form solution can be obtained by using the squared range difference LS (SRD-LS). Finally, the Cramer-Rao Bound (CRB) is derived for unified model. Numerical simulations show the validity of the proposed method.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2405-2410"},"PeriodicalIF":2.0,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145351902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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