Signal Processing - September 2017 - 67
code acquisition requires a high acquisition complexity due to the
long code length, some GPS L2C acquisition techniques leverage
CM code acquisition to acquire the CL code [24]; once the CM
code is acquired, there are only 75 CL code-phase hypotheses left.
In some studies [25], [26], partial correlation algorithms are utilized to reduce the computational cost in the FFT-based technique
for L2C signals (introduced in the section "Low Computational
Techniques Achieving Fast Acquisition and High Sensitivity"),
and, in [6], MGDC is used for a partial acquisition of L2C signals.
In the case of the Galileo E1 OS, E5a, and E5b and GPS L5
signals, the pilot and data channels have an equal signal power,
which makes it effective to combine both channels for higher
acquisition sensitivity [20], [27], [28]. For Galileo E1 OS signals,
however, both channels have sinBOC(1,1) and sinBOC(6,1) components carrying about 91% and 9% of the total signal power,
respectively. Therefore, it may be useful and efficient for a
receiver acquiring Galileo E1 OS signals to utilize both channels
with the sinBOC(6,1) component neglected to reduce the acquisition complexity.
Channel-combining technique for GNSS signals
at different frequencies
Since there are GNSS satellites synchronously transmitting
ranging signals at different frequencies, a receiver can make
use of all available signals from the same satellite for increased
sensitivity and higher positioning accuracy. In [29], a Galileo
receiver is developed to acquire both Galileo E5a and E5b
signals coherently, for which it is exploited that the data-bit
and code-chip boundaries are synchronized between the two
signals, the code delays of the two signals are the same, and
the Doppler-frequency ratio between E5a and E5b signals is
the same to the carrier frequency ratio between E5a and E5b.
Exploiting the same assumption, a dual frequency receiver for
coherent acquisition of GPS L1 C/A and GPS L2C signals is
developed and demonstrated successfully [30].
Low computational techniques achieving
fast acquisition and high sensitivity
While higher acquisition sensitivity can be achieved by
increasing N i and N co and by channel combining in G ($), low
computational fast acquisition techniques are of great interest
when N t (= N i N co = fs Tt) should be too large and very long
coherent integration is inevitable. There have been a number of techniques introduced in the literature to reduce the
huge computational cost and the large MAT required by the
conventional time-domain and frequency-domain (i.e., FFTbased) correlation techniques for increased sensitivity. This
section focuses on some of the recent signal processing techniques for high sensitivity and fast acquisition.
Among a large number of various low computational, high
sensitivity, and fast acquisition techniques, we investigate
techniques in three categories: sample-domain techniques,
frequency-domain techniques, and assisted acquisition techniques. Averaging techniques are introduced as well-known
sample-domain techniques, where a receiver reduces the size
of the signal samples and employs specialized search strategies
to cope with the timing and frequency uncertainties caused by
the sample reduction. For frequency-domain techniques, recent
fast computation algorithms enabling huge computational cost
reduction are introduced. All of the frequency-domain techniques utilize data-segmentation schemes, specialized signal
processing, and specialized search strategies. Note that the signal processing techniques and search strategies used in both the
sample- and frequency-domain techniques are different from
those in the conventional techniques discussed in the sections
"Fundamentals of GNSS Signal Acquisition" and "Channel
Combining Techniques for New GNSS Signals." The assisted
acquisition techniques represented by the assisted-GNSS (AGNSS) are introduced as one of the most widely used recent
techniques in comparison to both the sample- and frequencydomain techniques that are for standalone GNSS receivers.
Also note that there are different kinds of low computational, fast acquisition techniques not introduced in this article. For
example, hypothesis compression techniques compress the 2-D
hypothesis plane into a much smaller compressed hypothesis
plane and perform a quicker search over the compressed hypothesis plane [11]. There are multisatellite maximum likelihood (MSML) acquisition techniques [31] that can acquire multiple satellite
signals simultaneously. In practice, the hypothesis compression
techniques suffer from SNR degradation with respect to the compression rate, and the recent MS-ML techniques require a fine time
and frequency assistance or may require a huge computational cost.
Sample-domain techniques: Averaging techniques
Averaging techniques are used to reduce the number of signal
samples to process in the GNSS signal acquisition. The averaging
process can be performed over neighboring (consecutive) signal
samples or over the blocks of signal samples, which results in a
larger ambiguity of timing than processing the original samples
or results in a loss of signal energy when the averaged signal
samples from different blocks have different phases, respectively.
To mitigate these side effects, the averaging techniques have to
employ additional signal processing techniques with search strategies different from the conventional techniques.
Averaging correlation technique
When the sampling rate is very high (i.e., fs & 2R c ) and there are
too many signal samples to be processed with the time-domain
correlation or the FFT-based techniques, the averaging correlation (AC) technique [32] can be useful to reduce the number of
signal samples by taking the average of every N sc (# fs Tc) consecutive samples of y B 6n@, where y B 6n@ is the Doppler-frequency compensated samples in Figure 1. The receiver replica x 6n@ is
generated at a reduced rate fs /N sc accordingly, so that the correlation length and the size of the FFT and IFFT become N sc times
smaller. However, when the averaged sample is constructed of
signal samples across a chip boundary, signal samples belonging
to the neighboring chips may cancel each other and cause an SNR
loss in the correlation output. Therefore, only the signal samples
within the same code chip should be averaged to maximize the
SNR. In this technique, N sc sets of averaged sample sequences
with different sample offsets are generated first, and each set is
IEEE SIGNAL PROCESSING MAGAZINE
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September 2017
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Table of Contents for the Digital Edition of Signal Processing - September 2017
Signal Processing - September 2017 - Cover1
Signal Processing - September 2017 - Cover2
Signal Processing - September 2017 - 1
Signal Processing - September 2017 - 2
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Signal Processing - September 2017 - Cover3
Signal Processing - September 2017 - Cover4
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