Optimal Geometry and Motion Coordination for Multisensor Target Tracking with Bearings-Only Measurements.

This paper focuses on the optimal geometry and motion coordination problem of mobile bearings-only sensors for improving target tracking performance. A general optimal sensor-target geometry is derived with uniform sensor-target distance using D-optimality for arbitrary n (n≥2) bearings-only sensors. The optimal geometry is characterized by the partition cases dividing n into the sum of integers no less than two. Then, a motion coordination method is developed to steer the sensors to reach the circular radius orbit (CRO) around the target with a minimum sensor-target distance and move with a circular formation. The sensors are first driven to approach the target directly when outside the CRO. When the sensor reaches the CRO, they are then allocated to different subsets according to the partition cases through matching the optimal geometry. The sensor motion is optimized under constraints to achieve the matched optimal geometry by minimizing the sum of the distance traveled by the sensors. Finally, two illustrative examples are used to demonstrate the effectiveness of the proposed approach.

Cooperative Global Robust Practical Output Regulation of Nonlinear Lower Triangular Multiagent Systems via Event-Triggered Control.

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.

Event-Based Practical Output Regulation for a Class of Multiagent Nonlinear Systems.

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.

Pixel Binning for High Dynamic Range Color Image Sensor Using Square Sampling Lattice.

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.

A Novel Multiobject Tracking Approach in the Presence of Collision and Division.

This paper aims to develop a general framework for accurately tracking and quantitatively characterizing multiple cells (objects) when collision and division between cells arise. Through introducing three types of interaction events among cells, namely, independence, collision, and division, the corresponding dynamic models are defined and an augmented interacting multiple model particle filter tracking algorithm is first proposed for spatially adjacent cells with varying size. In addition, to reduce the ambiguity of correspondence between frames, both the estimated cell dynamic parameters and cell size are further utilized to identify cells of interest. The experiments have been conducted on two real cell image sequences characterized with cells collision, division, or number variation, and the resulting dynamic parameters such as instant velocity, turn rate were obtained and analyzed.