Sensing Matrix Optimization for Multi-Target Localization Using Compressed Sensing in Wireless Sensor Network

In the multi-target localization based on Compressed Sensing(CS),the sensing matrix's characteristic is significant to the localization accuracy.To improve the CS-based localization approach's performance,we propose a sensing matrix optimization method in this paper,which considers the optimization under the guidance of the t%-averaged mutual coherence.First,we study sensing matrix optimization and model it as a constrained combinatorial optimization problem.Second,the t%-averaged mutual coherence is adopted as the optimality index to evaluate the quality of different sensing matrixes,where the threshold t is derived through the K-means clustering.With the settled optimality index,a hybrid metaheuristic algorithm named Genetic Algorithm-Tabu Local Search(GA-TLS)is proposed to address the combinatorial optimization problem to obtain the final optimized sensing matrix.Extensive simulation results reveal that the CS localization approaches using different recovery algorithms benefit from the proposed sensing matrix optimization method,with much less localization error compared to the traditional sensing matrix optimization methods.

Multi-target Collaborative Combat Decision-Making by Improved Particle Swarm Optimizer

A decision-making problem of missile-target assignment with a novel particle swarm optimization algorithm is proposed when it comes to a multiple target collaborative combat situation.The threat function is established to describe air combat situation.Optimization function is used to find an optimal missile-target assignment.An improved particle swarm optimization algorithm is utilized to figure out the optimization function with less parameters,which is based on the adaptive random learning approach.According to the coordinated attack tactics,there are some adjustments to the assignment.Simulation example results show that it is an effective algorithm to handle with the decision-making problem of the missile-target assignment(MTA)in air combat.

A new algorithm of bearings-only multi-target tracking of bistatic system

Much research mainly focuses on the batch processing method （e.g. maximum likelihood method） when bearings-only multiple targets tracking of bistatic sonar system is considered. In this paper, the idea of recursive processing method is presented and employed, and corresponding data association algorithms, i.e. a multi-objective ant-colony-based optimization algorithm and an easy fast assignment algorithm are developed to solve the measurements-to-measurements and measurements-to-tracks data association problems of bistatic sonar system, respectively. Monte-Carlo simulations are induced to evaluate the effectiveness of the proposed methods.

Sequence optimization for multiple asteroids rendezvous via cluster analysis and probability-based beam search

It is of great significance to visit multiple asteroids in a space mission.In this paper,the multiple asteroids mission optimization is implemented using cluster analysis and probability-based beam search.Clustering is performed to select the first asteroid to visit.Four cluster algorithms are investigated and affinity propagation is selected.Then four beam search algorithms that are deterministic beam search and three probability-based beam search variants,probabilistic beam search,ant-colony beam search,and evolving beam search,are applied to search for the rendezvous sequence.Deterministic beam search as a heuristic tree search algorithm is widely applied in multitarget sequence optimization,but it has an obvious drawback of the conflict between the number of pruned nodes and the possibility of finding optimal solutions,which can be improved by probability-based beam search.Among three probability-based beam search,the ant-colony beam search has a learning mechanism,and evolving beam search is constructed based on ant-colony beam search and has an evolutionary mechanism.Results show that the introduction of randomness can improve beam search,and beam search variants with the learning and evolutionary mechanism have an excellent performance.

A conceptual methodology for simultaneous optimization of water and heat with non-isothermal mixing

A new conceptual methodology is proposed to simultaneously integrate water allocation and energy networks with non-isothermal mixing. This method employs a simultaneous model and includes two design steps. In the first step, the water allocation network （WAN）, which could achieve the targets of saving water and energy, is obtained by taking account the temperature factor into the design procedure. The optimized targets of both freshwater and energy are reached at this step which ensures this approach is a simultaneous one. In the second step, based on the obtained WAN, the whole water allocation and heat exchange network （WAHEN） is combined with the non-isothermal mixing to reduce the number of heat exchangers. The thus obtained WAHEN can achieve three optimization targets （minimization of water, energy and the number of heat exchangers）. Furthermore, the effectivity of our method has been demonstrated by solving two literature examples.

Design of Decentralized Multi-input Multi-output Repetitive Control Systems

This paper presents the design of decentralized repetitive control （RC） for multi-input multi-output （MIMO） systems. An optimization method is used to obtain a RC compensator that ensures system stability and good tracking performance. The designed compensator is in the form of a stable, low order, and causal filter, in which the compensator can be implemented separately without being merged with the RC internal model. This will reduce complexity in the implementation. Simulation results and comparison study are given to demonstrate the effectiveness of the proposed design. The novelty of design is also verified in experiments on a 2 degrees of freedom （DOF） robot.

A benchmark system to investigate the non-minimum phase behaviour of multi-input multi-output systems

In this paper,the problem of designing a multi-input multi-output(MIMO)systemfor studying the non-minimum phase(NMP)behaviour is considered.For this purpose,a NMP MIMO circuit is proposed and studied under different conditions.The main reason for designing this circuit is the lack of a simple and flexible benchmark for examining different control methods.Due to the simple structure and capability of showing different NMP characteristics,our proposed system is a suitable choice to study the behaviour of these systems.Also,our proposed system can be extended by series and parallel connections to generate more complicated benchmarks.The other advantages of this system are the large number of tunable parameters,adjustable interaction,variable number of poles and zeros,and inexpensive cost.Moreover,this benchmark can be used as a tool for hardware simulation.Finally,an optimal H∞decoupling control is applied to this benchmark to verify its effectiveness.