Summary
Radar imaging is an advanced remote sensing technique that maps the reflectivity of distant objects by transmitting modulated signals at radio frequencies and processing the detected echoes. By proper waveform selection, it is currently possible to image the surface of planets or asteroids from Earth with a relatively high degree of resolution, despite the astronomical distances to these objects. Waveforms that are used for radar astronomy are characterized by a large spectral bandwidth and long time duration, which can be obtained by phase modulating a long pulse train. An example of phase modulation is binary phase coding in which each pulse of the train is randomly assigned a phaseof 0 or π. The corresponding echo pulses are correlated with the sequence of transmitted pulses to resolve the main features of an object. The correlation of long binary sequences can yield high resolution with significant clutter suppression. However, this process requires the selection of an optimum binary phase code among a significant number of possibilities. For a given code of length N, where N is the number of code elements, the population size is 2N. Certain members of this population will exhibit good qualities for imaging, while others may behave poorly. In this chapter, we discuss the principles of radar imaging and the implementation of a genetic algorithm to find the “good” member codes from a population of codes of length N. This algorithm was successfully tested for binary phase codes of lengths N = 13 and 24 pulses. For the short length N = 13 case, in which an optimum code is known to exist, the rate of convergence to this optimum was poor relative to that of an exhaustive search. However, for the longer codes tested (N = 24), the genetic algorithm converged much faster than an exhaustive search for the large solution space.
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Flores, B.C., Kreinovich, V., Vasquez, R. (1997). Signal Design for Radar Imaging in Radar Astronomy: Genetic Optimization. In: Dasgupta, D., Michalewicz, Z. (eds) Evolutionary Algorithms in Engineering Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-03423-1_23
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DOI: https://doi.org/10.1007/978-3-662-03423-1_23
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