Abstract
To detect weak Global Position System (GPS) signal indoors, various high-sensitivity detection algorithms have been proposed. However, a common tradeoff between high-sensitivity and computation burden impedes development of high sensitivity GPS receiver. As another strategy, chaotic oscillator, sensitive to periodic signal and inert to noise, possesses huge advantage in weak signal acquisition. In this paper chaotic oscillator is employed in weak GPS signal acquisition. With numerical indication of Lyapunov exponents (LEs), chaotic oscillator can achieve acquisition in extremely weak GPS signal. Compared with conventional algorithm, chaotic oscillator consumes less acquisition time and is capable of detecting weak GPS signal. In the final section of paper, results from computer simulation illustrate that chaotic oscillator algorithm can acquire GPS signal at 48 dB/2MHz SNR.
Highlights
Global Position System (GPS) signal indoors become extremely weak because of fading, refraction, reflection, and multipath interference [1]
Line-of-sight GPS signal is at 44 dBHz [2], while signal strength will degrade larger than 25 dB in bad case [3, 4]
Lyapunov exponents (LEs) can be employed in GPS signal acquisition based on chaotic oscillator [11]
Summary
GPS signal indoors become extremely weak because of fading, refraction, reflection, and multipath interference [1]. Until now there are three typical solutions to weak GPS signal acquisition in high-sensitivity receiver, alternate halfbit accumulation [5], full-bit accumulation with estimation of bit transition time [5], and differentially coherent acquisition algorithm [6]. Alternate half-bits algorithm [5] divided raw GPS signal data into equal cells by the length of 10 milliseconds, and all cells alternately belong to two groups. Simulation in [5] illustrated well performance of alternate half-bit accumulation in detecting weak GPS signal. In differential coherent [6], perform received data correlation with replica signal at the length of 1 millisecond, multiply all the adjacent two correlation results, accumulate all products to get detection statistic, and compare the statistic with threshold to judge whether expected signal exist.
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