Linear Sparse Array Synthesis With Minimum Number of Sensors

The number of sensors employed in an array affects the array performance, computational load, and cost. Consequently, the minimization of the number of sensors is of great importance in practice. However, relatively fewer research works have been reported on the later. In this paper, a novel optimiz...

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Veröffentlicht in:IEEE transactions on antennas and propagation Jg. 58; H. 3; S. 720 - 726
Hauptverfasser: Ling Cen, Wee Ser, Zhu Liang Yu, Rahardja, S., Wei Cen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.03.2010
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Antennas
Apertures
Applied sciences
Arrays
Beam pattern synthesis
Beams (radiation)
Computation
Computational efficiency
Costs
Exact sciences and technology
Genetic algorithms
genetic algorithms (GAs)
Gratings
linear arrays
Mathematical models
Optimization
Optimization methods
peak sidelobe level (PSL)
Radiocommunications
Sensor arrays
Sensor systems
Sensors
Sidelobes
Signal synthesis
sparse arrays
Spatial resolution
Studies
Telecommunications
Telecommunications and information theory
Performance evaluation
Lateral lobe
peak sidelobe level (PSL)
Sparse array
Noise reduction
linear arrays
Computational complexity
Optimization
Interference suppression
Beam pattern synthesis
sparse arrays
Genetic algorithm
Signal processing
Numerical simulation
genetic algorithms (GAs)
Linear antenna arrays
Pattern synthesis
Antenna synthesis
ISSN:0018-926X, 1558-2221
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Zusammenfassung:The number of sensors employed in an array affects the array performance, computational load, and cost. Consequently, the minimization of the number of sensors is of great importance in practice. However, relatively fewer research works have been reported on the later. In this paper, a novel optimization method is proposed to address this issue. In the proposed method, the improved genetic algorithm that has been presented at a conference recently, is used to optimize the weight coefficients and sensor positions of the array. Sensors that contribute the least to the array performance are then removed systematically until the smallest acceptable number of sensors is obtained. Specifically, this paper reports the study on the relationship between the peak sidelobe level and the sensor weights, and uses the later to select the sensors to be removed. Through this approach, the desired beam pattern can be synthesized using the smallest number of sensors efficiently. Numerical results show that the proposed sensor removal method is able to achieve good sidelobe suppression with a smaller number of sensors compared to other existing algorithms. The computational load required by our proposed approach is about one order less than that required by other existing algorithms too.
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ISSN:0018-926X
1558-2221
DOI:10.1109/TAP.2009.2039292