This article addresses the multi-target tracking problem for a dense but resolvable group, and designs a method that uses the velocity estimation of the group to correct the initialization of trajectories and optimize the trajectories identification for the sub-targets within the group. First, an extended target tracking algorithm based on the elliptical random hypersurface model (RHM) is adopted to obtain the overall motion state of the group. Second, the overall velocity estimation, i.e., the collective feature of the group, is used as a prior pseudo measurement information to assist in generating the newborn target state more accurately. Next, an adaptive Generalized Labeled MultiBernoulli (GLMB) algorithm is used to estimate the motion states of the dense targets within the group, and the sub-target motion states are modified by integrating the overall velocity estimation of the group. The simulation results verify that the velocity correction algorithm proposed in this paper can significantly improve the tracking performance of the dense group targets, and provide a theoretical guidance for the engineering applications in the group target tracking.

In this article, the problem of cooperative global robust practical output regulation is examined for uncertain nonlinear multiagent systems in a lower triangular form via event-triggered control. The problem is dealt with in three steps. At the first step, a decentralized internal model is constructed based on the lower triangular form such that the regulation issue is translated to a stabilization one. Next, a nonlinear decentralized state-feedback controller is designed to achieve input-to-state stabilization with the sampling error as the input. In the third step, a simple event-triggering mechanism is embedded in the controller to achieve an event-based controller redesign. An illustrative example is presented to verify the theoretical results.

This paper investigates the distributed state estimation for a class of linear time‐varying systems with intermittent observations in sensor networks. Unlike the existing studies in distributed state estimation, this work considers the scenario where the cross‐covariances between different sensors are unavailable and the measurements for state estimation encounter intermittent observations and/or random losses. For this practical scenario, a new sequential covariance intersection‐based Kalman consensus filer (SCIKCF) is then developed. We show that, with the proposed SCIKCF, each sensor can achieve consensus estimates regardless of the order of fusion. Furthermore, the stability of the SCIKCF as well as the boundedness of the estimation error and the corresponding error covariances are analysed. Finally, three examples are performed to verify the effectiveness of the proposed SCIKCF.

This paper explores a cooperative practical output regulation problem for a class of heterogeneous multiagent nonlinear systems by event-based output feedback. Specifically, we shall restrict our attention to the situation of sampled-data-based local measurements. As usual, due to agents heterogeneity, we first convert the problem into a stabilization one for the so-called augmented system, composed of the agent systems and suitably designed continuous-time internal models. Then, we show that this stabilization can be solved by measurement feedback. It finally allows us to establish a valid event-based protocol, leading to a global practical stability property and meanwhile guaranteeing Zeno-free condition.

We propose a wavelet-based Gaussian scale mixture (GSM) demosaicking method. The wavelet coefficients of the proposed method corresponding to the luminance and chrominance components are reconstructed using Bayesian minimum mean square error estimation. The proposed wavelet-GSM prior exploits the correlation of neighboring wavelets coefficients to improve upon a previously proposed posterior sparsity directed demosaicking method. As a result, our proposed demosaicking method suppresses the zippering artifacts more effectively than the state of the arts.

We propose a new pixel binning scheme for color image sensors. We minimized distortion caused by binning by requiring that the superpixels lie on a square sampling lattice. The proposed binning schemes achieve the equivalent of 4.42 times signal strength improvement with the image resolution loss of 5 times, higher in noise performance and in resolution than the existing binning schemes. As a result, the proposed binning has considerably less artifacts and better noise performance compared with the existing binning schemes. In addition, we provide an extension to the proposed binning scheme for performing single-shot high dynamic range image acquisition.

This study addresses the problem of tracking extended target with intermittent observations. Based on practical applications, two Bernoulli distributed random variables are employed to describe the intermittent phenomenon of the positional measurements and the measurements of target extent, respectively. First, a machine vision algorithm is developed to solve the target shape parameters. Then, four sub‐filters are designed according to the received observations and the achieved target shape parameters. The output of the proposed tracking filer can be obtained by the weighted‐confidence fusion of the sub‐filters. Finally, the machine vision algorithm is evaluated by the virtual target images created in OpenGL (Open Graphics Library) and the real images of a moving ship. The performance of the designed tracking filter is compared with the traditional tracking filter. The experiment results show the effectiveness of the machine vision approach; also the Monte‐Carlo runs demonstrate that the provided tracking filter outperforms the traditional one with respect to accuracy.

In this paper, we propose the pseudo multi-hop distributed consensus algorithm under directed communication topologies. The property and convergence rate of the pseudo multi-hop distributed consensus algorithm under directed communication topologies are analyzed, and the convergence conditions for the pseudo multi-hop distributed consensus algorithm is given. In particular, the convergence rate is determined by the spectral radius of the matrix depend on the communication topology. Finally, simulation results are provided to verify these analytical results.

In order to improve the accuracy of distributed consensus under quantized communication, in the paper, we firstly propose a modification to the probabilistically quantized distributed (PQDA) algorithm, and we update the state of every node by its state and the probabilistically quantized information of the adjacency nodes. Moreover, based on the modification, we propose a second-order modified probabilistically quantized algorithm. We analyze the performance and the mean square errors of the second-order modified probabilistic quantization distributed consensus, by analysis and simulation, the results show the second-order distributed consensus with modified probabilistic quantization fails to reach a consensus, but the mean square error is far smaller than that of the PQDA algorithm, and the curve chart of the new algorithm is similar to that of the distributed average consensus algorithm without distortion.

In order to improve the accuracy and the convergence rate of distributed consensus under quantized communication, in the paper, based on one-bit adaptive quantization scheme, we propose the second-order distributed consensus to update the state of every node by the present quantized values and the previous quantized values of the adjacency nodes. We analyze the convergence performance. The second-order distributed consensus with one-bit adaptive quantization achieves a consensus in a mean square sense, and the consensus is equal to the average of the initial states. Simultaneously, Simulations are done about the second-order distributed consensus based on one-bit adaptive quantization. Results show that the second-order distributed consensus algorithm based on one-bit adaptive quantization can reach an average consensus, and its convergence rate is higher than that of the first-order adaptive quantized distributed consensus algorithm, moreover, the mean square errors are smaller within the finite steps.