Radar Target Tracking Algorithm Based On New Particle Swarm Optimization Particle Filter

Wu, Ying; Cen, Wu; Wang, Long; Sun, Li; Wang, D.X.

doi:10.1145/3510513.3510528

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…Table 1 shows papers related to PSO methods for target estimation. There is a great deal of research going on [15][16][17], and relevant research for missile applications can be found [18][19][20]. Also, various attempts have been made to accelerate the algorithm to overcome the limitations of the PSO method described in Section 1.…”

Section: Introductionmentioning

confidence: 99%

Parallelized Particle Swarm Optimization on FPGA for Realtime Ballistic Target Tracking

Park,

Lee,

Kwon

et al. 2023

Sensors

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This paper addresses the problem of tracking a high-speed ballistic target in real time. Particle swarm optimization (PSO) can be a solution to overcome the motion of the ballistic target and the nonlinearity of the measurement model. However, in general, particle swarm optimization requires a great deal of computation time, so it is difficult to apply to realtime systems. In this paper, we propose a parallelized particle swarm optimization technique using field-programmable gate array (FPGA) to be accelerated for realtime ballistic target tracking. The realtime performance of the proposed method has been tested and analyzed on a well-known heterogeneous processing system with a field-programmable gate array. The proposed parallelized particle swarm optimization was successfully conducted on the heterogeneous processing system and produced similar tracking results. Also, compared to conventional particle swarm optimization, which is based on the only central processing unit, the computation time is significantly reduced by up to 3.89×.

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Section: Introductionmentioning

confidence: 99%

Parallelized Particle Swarm Optimization on FPGA for Realtime Ballistic Target Tracking

Park,

Lee,

Kwon

et al. 2023

Sensors

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Nonlinear crossing strategy-based particle swarm optimizations with time-varying acceleration coefficients

Watanabe,

2024

Appl Intell

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In contemporary particle swarm optimization (PSO) algorithms, to efficiently explore global optimum solutions, it is common practice to set the inertia weight to monotonically decrease over time for stability, while allowing the two acceleration coefficients, representing cognitive and social factors, to adopt decreasing or increasing functions over time, including random variations. However, there has been little discussion on a unified design approach for these time-varying acceleration coefficients. This paper presents a unified methodology for designing monotonic decreasing or increasing functions to construct nonlinear time-varying inertia weight and two acceleration coefficients in PSO, along with a control strategy for exploring global optimum solutions. We first construct time-varying coefficients by linearly amplifying well-posed monotonic functions that decrease or increase over normalized time. Here, well-posed functions ensure satisfaction of specified conditions at the initial and terminal points of the search process. However, many of the functions employed thus far only satisfy well-posedness at either the initial or terminal points of the search time, prompting the proposal of a method to adjust them to virtually meet specified initial or terminal points. Furthermore, we propose a crossing strategy where the developed cognitive and social acceleration coefficients intersect within the search time interval, effectively guiding the search process by pre-determining crossing values and times. The performance of our Nonlinear Crossing Strategy-based Particle Swarm Optimization (NCS-PSO) is evaluated using the CEC2014 (Congress on Evolutionary Computation in 2014) benchmark functions. Through comprehensive numerical comparisons and statistical analyses, we demonstrate the superiority of our approach over seven conventional algorithms. Additionally, we validate our approach, particularly in a drone navigation scenario, through an example of optimal 3D path planning. These contributions advance the field of PSO optimization techniques, providing a robust approach to addressing complex optimization problems.

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Research on UAV Passive Localization Based on Greedy Strategy and Two-degree Error Analysis Model

Zhang,

2023

2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology (ICCASIT)

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Radar Target Tracking Algorithm Based On New Particle Swarm Optimization Particle Filter

Cited by 3 publications

References 6 publications

Parallelized Particle Swarm Optimization on FPGA for Realtime Ballistic Target Tracking

Parallelized Particle Swarm Optimization on FPGA for Realtime Ballistic Target Tracking

Nonlinear crossing strategy-based particle swarm optimizations with time-varying acceleration coefficients

Research on UAV Passive Localization Based on Greedy Strategy and Two-degree Error Analysis Model

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