Weak GNSS signal acquisition using prolate spheroid wave function based compressive sensing

Halder, Tamesh; Bhattacharya, Amitabha

doi:10.1109/techsym.2014.6808053

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…To achieve higher acquisition rate in GPS signal acquisition, Ihsan Alshahib Lami et.al [4], proposed a Jacket matrix based deterministic waveform generator as a best orthogonal transform to sparsify the received weak signals in a harsh environment. Bhattacharyya et.al [5], proposed a prolate spheroidal wave function as a basis transform to recover a weak GPS signal level of 35dB-Hz using compressed sensing approach. Prasant Misra et.al [6], implemented energy efficient GPS sensing on cloud offloading.…”

Section: Introductionmentioning

confidence: 99%

Weak GPS Acquisition Via Compressed Differential Detection Using Structured Measurement Matrix

Elango

Sudha

2016

International Journal on Smart Sensing and Intelligent Systems

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Abstract-Quality of the acquisition algorithm is the main criteria for assessment of the GPS receiver.Compressed sensing has been used recently in the GPS signal acquisition process to improve the acquisition sensitivity and find visible satellites in the two dimensional search space. In this paper, strategies for constructing the GPS Dictionary matrix using different measurement matrices are analyzed for weak signal conditions. Conventional acquisition methods use pure random or Bernoulli measurement matrices requiring huge storage memory and resulting in high computational cost for faithful sparse signal representation. Based on the restricted isometry and coherence properties, the Kronecker product of hybrid 'L' deterministic measurement matrix has been proposed for fast calculation and easy hardware implementation of the GPS acquisition module. Simulation results for weak signals of C/N 0 25 dB-Hz show that the compressed sensing combined with post-correlation differential detection scheme has the ability to determine more number of visible satellites. It is inferred that using this approach, 92% probability of obtaining 1.2 times more visible satellites than the uncompressed data length of 8 msec is achieved with lower computation time and significant improvement in average post correlation SNR value of 0.84 dB is obtained for four visible satellites.

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Section: Introductionmentioning

confidence: 99%