Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userOptimal Deployment of Multistatic Radar System Using Multi-Objective Particle Swarm Optimization
85
0
0.0
(
0
)
Added by
Tianxian Zhang
Publication date
2016
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
We consider an optimization deployment problem of multistatic radar system (MSRS). Through the antenna placing and the transmitted power allocating, we optimally deploy the MSRS for two goals: 1) the first one is to improve the coverage ratio of surveillance region; 2) the second goal is to get a even distribution of signal energy in surveillance region. In two typical working modes of MSRS, we formulate the optimization problem by introducing two objective functions according to the two mentioned goals, respectively. Addressing on two main challenges of applying multi-objective particle swarm optimization (MOPSO) in solving the proposed optimization problem, we propose a deployment algorithm based on multiobjective particle swarm optimization with non-dominated relative crowding distance (MOPSO-NRCD). For the challenge of value difference, we propose a novel selection method with a non-dominated relative crowding distance. For the challenge of particle allocation, a multi-swarm structure of MOPSO is also introduced. Finally, simulation results are given out to prove the advantages and validity of the proposed deployment algorithm. It is shown that with same number of employed particles, the proposed MOPSO-NRCD algorithm can achieve better optimization performance than that of traditional multiobjective particle swarm optimization with crowding distance (MOPSO-CD).
rate research
Read More
Application of the multi-objective particle swarm optimisation (MOPSO) algorithm to design of water distribution systems is described. An earlier MOPSO algorithm is augmented with (a) local search, (b) a modified strategy for assigning the leader, and (c) a modified mutation scheme. For one of the benchmark problems described in the literature, the effect of each of the above features on the algorithm performance is demonstrated. The augmented MOPSO algorithm (called MOPSO+) is applied to five benchmark problems, and in each case, it finds non-dominated solutions not reported earlier. In addition, for the purpose of comparing Pareto fronts (sets of non-dominated solutions) obtained by different algorithms, a new criterion is suggested, and its usefulness is pointed out with an example. Finally, some suggestions regarding future research directions are made.
Joint communication and sensing allows the utilization of common spectral resources for communication and localization, reducing the cost of deployment. By using fifth generation (5G) New Radio (NR) (i.e., the 3rd Generation Partnership Project Radio Access Network for 5G) reference signals, conventionally used for communication, this paper shows sub-meter precision localization is possible at millimeter wave frequencies. We derive the geometric dilution of precision of a bistatic radar configuration, a theoretical metric that characterizes how the target location estimation error varies as a function of the bistatic geometry and measurement errors. We develop a 5G NR compliant software test bench to characterize the measurement errors when estimating the time difference of arrival and angle of arrival with 5G NR waveforms. The test bench is further utilized to demonstrate the accuracy of target localization and velocity estimation in several indoor and outdoor bistatic and multistatic configurations and to show that on average, the bistatic configuration can achieve a location accuracy of 10.0 cm over a bistatic range of 25 m, which can be further improved by deploying a multistatic radar configuration.
In recent years, with the development of microarray technique, discovery of useful knowledge from microarray data has become very important. Biclustering is a very useful data mining technique for discovering genes which have similar behavior. In microarray data, several objectives have to be optimized simultaneously and often these objectives are in conflict with each other. A Multi Objective model is capable of solving such problems. Our method proposes a Hybrid algorithm which is based on the Multi Objective Particle Swarm Optimization for discovering biclusters in gene expression data. In our method, we will consider a low level of overlapping amongst the biclusters and try to cover all elements of the gene expression matrix. Experimental results in the bench mark database show a significant improvement in both overlap among biclusters and coverage of elements in the gene expression matrix.
In this paper, considering multiple interference regions simultaneously, an optimal antenna deployment problem for distributed Multi-Input Multi-Output (MIMO) radar is investigated. The optimal antenna deployment problem is solved by proposing an antenna deployment method based on Multi-Objective Particle Swarm Optimization (MOPSO). Firstly, we construct a multi-objective optimization problem for MIMO radar antenna deployment by choosing the interference power densities of different regions as objective functions. Then, to obtain the optimal deployment result without wasting time and computational resources, an iteration convergence criterion based on interval distance is proposed. The iteration convergence criterion can be used to stop the MOPSO optimization process efficiently when the optimal antenna deployment algorithm reaches the desired convergence level. Finally, numerical results are provided to verify the validity of the proposed algorithm.
This paper investigates a full-duplex orthogonal-frequency-division multiple access (OFDMA) based multiple unmanned aerial vehicles (UAVs)-enabled wireless-powered Internet-of-Things (IoT) networks. In this paper, a swarm of UAVs is first deployed in three dimensions (3D) to simultaneously charge all devices, i.e., a downlink (DL) charging period, and then flies to new locations within this area to collect information from scheduled devices in several epochs via OFDMA due to potential limited number of channels available in Narrow Band IoT, i.e., an uplink (UL) communication period. To maximize the UL throughput of IoT devices, we jointly optimizes the UL-and-DL 3D deployment of the UAV swarm, including the device-UAV association, the scheduling order, and the UL-DL time allocation. In particular, the DL energy harvesting (EH) threshold of devices and the UL signal decoding threshold of UAVs are taken into consideration when studying the problem. Besides, both line-of-sight (LoS) and non-line-of-sight (NLoS) channel models are studied depending on the position of sensors and UAVs. The influence of the potential limited channels issue in NB-IoT is also considered by studying the IoT scheduling policy. Two scheduling policies, a near-first (NF) policy and a far-first (FF) policy, are studied. It is shown that the NF scheme outperforms FF scheme in terms of sum throughput maximization; whereas FF scheme outperforms NF scheme in terms of system fairness.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
