Compared with the traditional phased array radar, the co-located multiple-input multiple-output(MIMO) radar is able to transmit orthogonal waveforms to form different illuminating modes, providing a larger freedom deg...Compared with the traditional phased array radar, the co-located multiple-input multiple-output(MIMO) radar is able to transmit orthogonal waveforms to form different illuminating modes, providing a larger freedom degree in radar resource management. In order to implement the effective resource management for the co-located MIMO radar in multi-target tracking,this paper proposes a resource management optimization model,where the system resource consumption and the tracking accuracy requirements are considered comprehensively. An adaptive resource management algorithm for the co-located MIMO radar is obtained based on the proposed model, where the sub-array number, sampling period, transmitting energy, beam direction and working mode are adaptively controlled to realize the time-space resource joint allocation. Simulation results demonstrate the superiority of the proposed algorithm. Furthermore, the co-located MIMO radar using the proposed algorithm can satisfy the predetermined tracking accuracy requirements with less comprehensive cost compared with the phased array radar.展开更多
To address the problem of underwater multi-sensor multi-target passive tracking in clutter,a distributed kernel mean embedding-based Gaussian belief propagation(DKME-GaBP)algorithm is proposed.First,a joint posterior ...To address the problem of underwater multi-sensor multi-target passive tracking in clutter,a distributed kernel mean embedding-based Gaussian belief propagation(DKME-GaBP)algorithm is proposed.First,a joint posterior probability density function(PDF)is established and factorized,and it is represented by the corresponding factor graph.Then,the GaBP algorithm is executed on this factor graph to reduce the computational complexity of data association.The factor graph of the GaBP consists of inner and outer loops.The inner loop is responsible for local track estimation and data association.The outer loop fuses information from different sensors.For the inner loop,the kernel mean embedding(KME)with a Gaussian kernel is designed to transform the strong nonlinear problem of local estimation into a linear problem in a high-dimensional reproducing kernel Hilbert space(RKHS).For the outer loop,a multi-sensor distributed fusion method based on KME is proposed to improve fusion accuracy by accounting for the distance among different PDFs in RKHS.The effectiveness and robustness of the DKME-GaBP are validated in the simulations.展开更多
Multi-target tracking is facing the difficulties of modeling uncertain motion and observation noise.Traditional tracking algorithms are limited by specific models and priors that may mismatch a real-world scenario.In ...Multi-target tracking is facing the difficulties of modeling uncertain motion and observation noise.Traditional tracking algorithms are limited by specific models and priors that may mismatch a real-world scenario.In this paper,considering the model-free purpose,we present an online Multi-Target Intelligent Tracking(MTIT)algorithm based on a Deep Long-Short Term Memory(DLSTM)network for complex tracking requirements,named the MTIT-DLSTM algorithm.Firstly,to distinguish trajectories and concatenate the tracking task in a time sequence,we define a target tuple set that is the labeled Random Finite Set(RFS).Then,prediction and update blocks based on the DLSTM network are constructed to predict and estimate the state of targets,respectively.Further,the prediction block can learn the movement trend from the historical state sequence,while the update block can capture the noise characteristic from the historical measurement sequence.Finally,a data association scheme based on Hungarian algorithm and the heuristic track management strategy are employed to assign measurements to targets and adapt births and deaths.Experimental results manifest that,compared with the existing tracking algorithms,our proposed MTIT-DLSTM algorithm can improve effectively the accuracy and robustness in estimating the state of targets appearing at random positions,and be applied to linear and nonlinear multi-target tracking scenarios.展开更多
We propose a distributed labeled multi-Bernoulli(LMB)filter based on an efficient label matching method.Conventional distributed LMB filter fusion has the premise that the labels among local densities have already bee...We propose a distributed labeled multi-Bernoulli(LMB)filter based on an efficient label matching method.Conventional distributed LMB filter fusion has the premise that the labels among local densities have already been matched.However,considering that the label space of each local posterior is independent,such a premise is not practical in many applications.To achieve distributed fusion practically,we propose an efficient label matching method derived from the divergence of arithmetic average(AA)mechanism,and subsequently label-wise LMB filter fusion is performed according to the matching results.Compared with existing label matching methods,this proposed method shows higher performance,especially in low detection probability scenarios.Moreover,to guarantee the consistency and completeness of the fusion outcome,the overall fusion procedure is designed into the following four stages:pre-fusion,label determination,posterior complement,and uniqueness check.The performance of the proposed label matching distributed LMB filter fusion is demonstrated in a challenging nonlinear bearings-only multi-target tracking(MTT)scenario.展开更多
基金supported by the National Natural Science Fundation of China (61671137)。
文摘Compared with the traditional phased array radar, the co-located multiple-input multiple-output(MIMO) radar is able to transmit orthogonal waveforms to form different illuminating modes, providing a larger freedom degree in radar resource management. In order to implement the effective resource management for the co-located MIMO radar in multi-target tracking,this paper proposes a resource management optimization model,where the system resource consumption and the tracking accuracy requirements are considered comprehensively. An adaptive resource management algorithm for the co-located MIMO radar is obtained based on the proposed model, where the sub-array number, sampling period, transmitting energy, beam direction and working mode are adaptively controlled to realize the time-space resource joint allocation. Simulation results demonstrate the superiority of the proposed algorithm. Furthermore, the co-located MIMO radar using the proposed algorithm can satisfy the predetermined tracking accuracy requirements with less comprehensive cost compared with the phased array radar.
基金supported by the National Natural Science Foundation of China(Nos.62371173,U22A2044,and U22A2047)the Stable Supporting Fund of Acoustic Science and Technology Laboratory(NO.JCKYS2024604SSJS009)。
文摘To address the problem of underwater multi-sensor multi-target passive tracking in clutter,a distributed kernel mean embedding-based Gaussian belief propagation(DKME-GaBP)algorithm is proposed.First,a joint posterior probability density function(PDF)is established and factorized,and it is represented by the corresponding factor graph.Then,the GaBP algorithm is executed on this factor graph to reduce the computational complexity of data association.The factor graph of the GaBP consists of inner and outer loops.The inner loop is responsible for local track estimation and data association.The outer loop fuses information from different sensors.For the inner loop,the kernel mean embedding(KME)with a Gaussian kernel is designed to transform the strong nonlinear problem of local estimation into a linear problem in a high-dimensional reproducing kernel Hilbert space(RKHS).For the outer loop,a multi-sensor distributed fusion method based on KME is proposed to improve fusion accuracy by accounting for the distance among different PDFs in RKHS.The effectiveness and robustness of the DKME-GaBP are validated in the simulations.
基金supported by the National Natural Science Foundation of China(No.62276204)Open Foundation of Science and Technology on Electronic Information Control Laboratory,Natural Science Basic Research Program of Shanxi,China(Nos.2022JM-340 and 2023-JC-QN-0710)China Postdoctoral Science Foundation(Nos.2020T130494 and 2018M633470).
文摘Multi-target tracking is facing the difficulties of modeling uncertain motion and observation noise.Traditional tracking algorithms are limited by specific models and priors that may mismatch a real-world scenario.In this paper,considering the model-free purpose,we present an online Multi-Target Intelligent Tracking(MTIT)algorithm based on a Deep Long-Short Term Memory(DLSTM)network for complex tracking requirements,named the MTIT-DLSTM algorithm.Firstly,to distinguish trajectories and concatenate the tracking task in a time sequence,we define a target tuple set that is the labeled Random Finite Set(RFS).Then,prediction and update blocks based on the DLSTM network are constructed to predict and estimate the state of targets,respectively.Further,the prediction block can learn the movement trend from the historical state sequence,while the update block can capture the noise characteristic from the historical measurement sequence.Finally,a data association scheme based on Hungarian algorithm and the heuristic track management strategy are employed to assign measurements to targets and adapt births and deaths.Experimental results manifest that,compared with the existing tracking algorithms,our proposed MTIT-DLSTM algorithm can improve effectively the accuracy and robustness in estimating the state of targets appearing at random positions,and be applied to linear and nonlinear multi-target tracking scenarios.
文摘We propose a distributed labeled multi-Bernoulli(LMB)filter based on an efficient label matching method.Conventional distributed LMB filter fusion has the premise that the labels among local densities have already been matched.However,considering that the label space of each local posterior is independent,such a premise is not practical in many applications.To achieve distributed fusion practically,we propose an efficient label matching method derived from the divergence of arithmetic average(AA)mechanism,and subsequently label-wise LMB filter fusion is performed according to the matching results.Compared with existing label matching methods,this proposed method shows higher performance,especially in low detection probability scenarios.Moreover,to guarantee the consistency and completeness of the fusion outcome,the overall fusion procedure is designed into the following four stages:pre-fusion,label determination,posterior complement,and uniqueness check.The performance of the proposed label matching distributed LMB filter fusion is demonstrated in a challenging nonlinear bearings-only multi-target tracking(MTT)scenario.
