Signal Processing - September 2017 - 60
is unavoidable. As a result, a high sensitivity and fast acquisition technique is necessary for GNSS receivers operating in
GNSS-challenged environments.
This article covers a broad range of technical topics in GNSS
acquisition for standalone and assisted GNSS receivers. The
first half of this article introduces the fundamentals of conventional GNSS acquisition techniques and the acquisition techniques for new GNSS signals with various modulation schemes,
while the second half focuses on the recent signal processing
techniques for GNSS acquisition that achieve high sensitivity
and fast acquisition with low computational cost by utilizing
specialized signal processing algorithms and search strategies
different from the conventional acquisition techniques.
Fundamentals of GNSS signal acquisition
The GNSS signal acquisition function in a GNSS receiver
searches and detects incoming GNSS signals to initialize the
tracking function that actually produces fine GNSS signal measurements for positioning. In general, the GNSS signals are
spread by periodic long spreading codes, and the GNSS satellite motion relative to the receiver generates a Doppler-frequency
shift in the incoming signal as large as 5-10 kHz depending on
the receiver's motion. Since the precise time and location of
a GNSS receiver is initially unknown, the goal of the GNSS
acquisition function is to identify the prompt code phases and
Doppler frequencies of the incoming GNSS signals as quickly as
possible, within a resolution fine enough to initialize the tracking function successfully. Because the code phase and Doppler
frequency of an incoming GNSS signal can be at any hypothesis on the two-dimensional (2-D) hypothesis plane (also known
as the search space), the acquisition function may need to test
every hypothesis within the search space using a matched filter
(e.g., correlator) [2]-[4] to detect the incoming GNSS signal.
In general, the acquisition process can be both time consuming and computationally expensive when a GNSS receiver
tries to acquire weak GNSS signals (see the section "GNSS
Signal Model and Integration Techniques"). Conventionally, the number of code-phase hypotheses N Hc for binary
phase-shift keying (BPSK)-modulated GNSS signals is set to
two times the spreading code length L c (in other words, the
code-phase search step size ∆ x is set to a half code chip). On
the other hand, the number of Doppler-frequency hypotheses
N Hf can grow when the user dynamics relative to the satellite
motion is large and the Doppler-frequency search step size ∆ f
is small, where ∆ f is inversely proportional to the coherent
correlation interval Tco [2]-[4] (see the section "Search Strategy for Lower Acquisition Complexity"). In fact, to detect a
weaker GNSS signal, longer Tco is necessary, and the number
of Doppler-frequency hypotheses increases consequently. As a
result, both the acquisition time and acquisition complexity for
weaker GNSS signals increase exponentially due to the longer
time to test each hypothesis and the larger number of hypotheses in the search space. Note that the increase of acquisition
complexity is not a critical problem for GNSS receivers in
open-sky outdoor environments, where the signal attenuation
can be negligible.
To lessen the acquisition time and complexity, a GNSS
receiver needs to select an appropriate integration technique,
search strategy, and detection scheme based on the GNSS signal modulation, signal strength, available hardware resources,
computational capacity of the receiver, and target mean acquisition time (MAT), i.e., the expected time for signal acquisition,
etc. Next we introduce the conventional integration techniques,
search strategies, and detection schemes that involve the detection variables and detection thresholds.
GNSS signal model and integration techniques
A hypothesis testing utilizes the autocorrelation function
(ACF) [2]-[4] that performs correlation between the incoming GNSS signal y ^ t h that is downconverted to an intermediate frequency (IF) fI and a receiver replica signal x ^ t h . In
GNSSs, various signal modulation schemes are used for satellite signals in multiple frequencies. The conventional GPS
civil signal (i.e., L1 C/A signal) and Glonass L1 C/A signal
use BPSK modulation, and most new GNSS signals employ
modulation schemes to separate the data and pilot channels
and additional binary offset carrier (BOC) modulation to
separate the mainlobe of the signal spectrum into lower and
upper mainlobes to the carrier frequency. Currently, there are
more than eight variants of BOC modulations used or being
planned for the new GNSS signals [5]. Therefore, there should
be a number of mathematical expressions required for y ^ t h .
However, in this article, we consider three conventional modulation schemes of y ^ t h commonly used for GNSS acquisition
functions, as shown in the box at the bottom of the page, where
the subscripts D and P denote data and pilot channels,
respectively; A represents the amplitude; D ^ t h is the binary
phase-modulated navigation data at a data rate R b ^= 1/Tb h;
P ^ t h and S ^ t h are the binary phase-modulated primary and
secondary spreading codes, respectively; SC ^ t h denotes the
subcarrier signal; x, fD, and i are the unknown delay, Doppler frequency, and phase of the incoming signal, respectively; and v ^ t h represents the complex additive white Gaussian
noise process with two-sided power spectral density N 0 /2. In
addition, the length and the chip rate of P ^ t h are denoted as
L c and R c ^= 1/Tch , respectively. Note that (1a) includes GPS
Z AD (t - x) P (t - x) cos (2r ( f + f ) t + i) + v (t),
for BPSK
I
D
]
] A D D (t - x) PD (t - x) S D (t - x) SC D (t - x) cos (2r ( fI + fD) t + i)
]
y (t) = [ + A P PP (t - x) S P (t - x) SC P (t - x) sin (2r ( fI + fD) t + i) + v (t), for QPSK
]6 A D D (t - x) PD (t - x) SC D (t - x) + A P PP (t - x) SC P (t - x)@
]
] # cos (2r ( fI + fD) t + i) + v (t),
for in phase,
\
60
IEEE SIGNAL PROCESSING MAGAZINE
|
September 2017
|
(1a)
(1b)
(1c)
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
Signal Processing - September 2017 - 3
Signal Processing - September 2017 - 4
Signal Processing - September 2017 - 5
Signal Processing - September 2017 - 6
Signal Processing - September 2017 - 7
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Signal Processing - September 2017 - 191
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Signal Processing - September 2017 - 196
Signal Processing - September 2017 - Cover3
Signal Processing - September 2017 - Cover4
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