Implementation and Analysis of Pattern Propagation Factor Based Radar Model for Path Planning

Woo, Sang-Hyo; Shin, Jong-Jin; Kim, Jin-Gyu

doi:10.1007/s10846-018-0973-7

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…Radar engineering theory is not confined to Shannon theorem whose working frequency, bandwidth, and resolution are improving with the multifunctional architecture [142]. The implementation and analysis of the path planning and wavelength are also applied [143,144].…”

Section: Future Research Trends and Challengesmentioning

confidence: 99%

Processing Technology Based on Radar Signal Design and Classification

Zhang

Zhan

2020

International Journal of Aerospace Engineering

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It is well known that the application of radar is becoming more and more popular with the development of the signal technology progress. This paper lists the current radar signal research, the technical progress achieved, and the existing limitations. According to radar signal respective characteristics, the design and classification of the radar signal are introduced to reflect signal’s differences and advantages. The multidisciplinary processing technology of the radar signal is classified and compared in details referring to adaptive radar signal process, pulse signal management, digital filtering signal mode, and Doppler method. The transmission process of radar signal is summarized, including the transmission steps of radar signal, the factors affecting radar signal transmission, and radar information screening. The design method of radar signal and the corresponding signal characteristics are compared in terms of performance improvement. Radar signal classification method and related influencing factors are also contrasted and narrated. Radar signal processing technology is described in detail including multidisciplinary technology synthesis. Adaptive radar signal process, pulse compression management, and digital filtering Doppler method are very effective technical means, which has its own unique advantages. At last, the future research trends and challenges of technologies of the radar signals are proposed. The conclusions obtained are beneficial to promote the further promotion applications both in theory and practice. The study work of this paper will be useful for choosing more reasonable radar signal processing technology methods.

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Section: Future Research Trends and Challengesmentioning

confidence: 99%

Processing Technology Based on Radar Signal Design and Classification

Zhang

Zhan

2020

International Journal of Aerospace Engineering

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“…Terrain masking which is important for the survivability of UAV is considered in References [6,7]. Nonisotropic RCS is taken into account in References [8][9][10][11]. Numerous path planning algorithms have been proposed.…”

Section: Introductionmentioning

confidence: 99%

UAV Path Planning under Dynamic Threats Using an Improved PSO Algorithm

Shin

Bang

2020

International Journal of Aerospace Engineering

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This paper presents the method to solve the problem of path planning for an unmanned aerial vehicle (UAV) in adversarial environments including radar-guided surface-to-air missiles (SAMs) and unknown threats. SAM lethal envelope and radar detection for SAM threats and line-of-sight (LOS) calculation for unknown threats are considered to compute the cost for path planning. In particular, dynamic SAM lethal envelope is taken into account for path planning in that SAM lethal envelope does change its direction according to the flight direction of UAV. In addition, terrain masking, nonisotropic radar cross section (RCS), and dynamic constraints of UAV are considered to determine the cost of the path. An improved particle swarm optimization (PSO) algorithm is proposed for finding an optimal path. The proposed algorithm is composed of preprocessing steps, multi-swarm PSO algorithm, and postprocessing steps. The Voronoi diagram and Dijkstra algorithm as preprocessing steps provide the initial path for the multi-swarm PSO algorithm which uses multiple swarms with sub-swarms for the balance between exploration and exploitation. Postprocessing steps include waypoint insertion and 3D path smoothing. The computation time is reduced by using the map generation, the coordinate transformation, and the graphic processing unit (GPU) implementation of the algorithm. Various simulations are carried out to compare the performance of the proposed method according to the number of iterations, the number of swarms, and the number of cost evaluation points. The t -test results show that the suggested method is statistically better than existing methods.

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