Acquisition and Processing of GPS signals based on
Software Defined Radio
Adquisición y Procesamiento de señales GPS basadas en Radio Definida por Software
Castillo Delacroix L.
#1
, Fagre M.
#*2
, Vaquila I.
*α3
, Cabrera M. A.
#4
#
Laboratorio de Telecomunicaciones, Facultad de Ciencias Exactas e Ingeniería, Universidad Nacional de Tucumán
Av. Independencia 1800, Tucumán (4000), Argentina
1
lecastillodelacroix@herrera.unt.edu.ar
2
mfagre@herrera.unt.edu.ar
4
mcabrera@herrera.unt.edu.ar
*
Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)
Ciudad Autónoma de Buenos Aires (C1425FQB), Argentina
3
ivaquila@unrn.edu.ar
α
INVAP S. E.
Rio Negro(R8400), Argentina
Abstract- This paper presents the development of a software tool
that implements an acquisition block for GPS signals using SDR
technology, specifically devices like the HackRF One and RTL-
SDR. The tool, implemented in Python, successfully detects
satellites and estimates their parameters, offering customizable
algorithm settings and the capability to visualize the satellite
search space in three dimensions. Comparative tests with the
GNSS-SDR software tool demonstrated excellent performance,
providing a solid foundation for further development toward a
complete SDR-based GNSS receiver. This work highlights the
potential of SDR platforms for research and development,
emphasizing their flexibility, potential for future upgrades, and
cost-effectiveness in advancing future GNSS technologies.
Keywords: GNSS, GPS, Receiver, SDR.
Resumen- Este trabajo presenta el desarrollo de una herramienta
de software que se implementa mediante un bloque de
adquisición para señales GPS utilizando tecnología de SDR,
específicamente dirigida a dispositivos como HackRF One y
RTL-SDR. La herramienta implementada en Python detecta
satélites y estima sus parámetros con muy buen desempeño,
ofreciendo configuraciones de algoritmo personalizables y posee
la capacidad de visualizar el espacio de búsqueda de satélites en
tres dimensiones. Las pruebas comparativas con la herramienta
de software GNSS-SDR demostraron un alto rendimiento,
proporcionando una base sólida para expandir el desarrollo
hacia un receptor GNSS completo basado en tecnología SDR. En
esta propuesta se destaca el potencial de las plataformas SDR
para la investigación y el desarrollo, enfatizando su flexibilidad,
capacidad de actualización y rentabilidad para avanzar en
futuras tecnologías GNSS.
Palabras Claves: GNSS, GPS, Receptor, SDR.
I. INTRODUCTION
In the last decades, location services through Global
Navigation Satellite System (GNSS), such as the Global
Positioning System (GPS), have become an essential feature
of mobile devices. GPS receivers require a high level of
integration, low cost, and reduced power consumption [1,2].
To achieve these requirements, they are implemented in
Application Specific Integrated Circuits (ASIC) [3,4]. This
solution is suitable for applications that only need to know
the position of the device but limits the user’s ability to
interact with the architecture, algorithm, or parameters of the
receiver [5,6]. Due to these limitations, the approach of
GNSS Receivers implemented in Software Defined Radio
(SDR) emerged, where all signal processing is done in the
software domain. This approach allows modifying the
algorithms or parameters in real-time or in post-processing,
simply by modifying the receiver software, thus granting a
high degree of flexibility to the design. The current GNSS
scenario involves multi-constellation systems, which pose
the challenge of designing receivers capable of processing
signals of different characteristics, mitigating interference,
and providing high-precision positioning, such as Precise
Point Positioning (PPP). Most current civilian receivers can
only decode GPS-L1 C/A signals, so research laboratories
are working on receivers implemented in software [7,8,9].
Development and research on SDR-based GNSS receivers
are expanding due to the flexibility and upgradeability they
offer, which is crucial for advanced applications and future
navigation technologies. A notable example is the open-
source GNSS-SDR project of the Center Tecnològic de
Telecommunications de Catalunya. This program,
Revista elektron, Vol. 8, No. 2, pp. 43-53 (2024)
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Recibido: 17/09/24; Aceptado: 02/12/24
https://doi.org/10.37537/rev.elektron.8.2.195.2024
Original Article
developed in C++ and combined with GNU-Radio, runs on
a general-purpose computer and allows selecting between
different algorithms and accessing intermediate signals,
which is useful to validate the development of the
acquisition stage [10,11,12].
This paper presents the development of a Python-based
software tool for GPS signal acquisition based on SDR
devices. The acquisition stage, critical for detecting visible
satellites and estimating synchronization parameters like
Doppler shift and code delay, is implemented using
advanced algorithms and optimized for parallel processing.
Recent research has highlighted significant advancements in
signal acquisition and tracking for GPS-based Software-
Defined Radio (SDR) receivers, demonstrating the
feasibility of using SDR platforms for efficient acquisition
and tracking stages and the flexibility of SDR in optimizing
signal algorithms for enhanced performance under varying
environmental conditions [13,14,15]. This work contributes
to the field of SDR-based GNSS receivers by providing a
customizable and user-friendly platform for GPS signal
acquisition. The tool was fully implemented in Python, due
to the existence of libraries related to digital signal
processing and the extensive documentation available [16].
Besides, it supports multiple SDR formats and incorporates
visualization features to enhance usability. Comparative
tests with the GNSS-SDR software validate its accuracy and
performance, demonstrating its potential as a foundation for
a complete SDR-based GNSS receiver capable of multi-
frequency, multi-constellation processing [17,18,19,
20,21,22,23].
II. SYSTEM ARCHITECTURE
Although this development follows the SDR paradigm, the
proposed diagram shown in Figure 1 is applicable to both
Application Specific Integrated Circuit (ASIC) and SDR
implementations. The key difference lies in the approach: in
an ASIC implementation, all these blocks are hardwired
using transistors integrated into a single silicon chip, making
them fixed and unalterable. In contrast, the SDR approach
employs a reconfigurable radio frequency interface (RF
Front-End), where the SDR device is responsible for
converting the analog RF signal from the antenna into digital
samples. These samples are then processed by a general-
purpose processor, a Digital Signal Processor (DSP), or even
a Field Programmable Gate Array (FPGA). With ASICs, no
Fig. 1. GPS receiver block diagram implemented by SDR.
block can be modified once implemented in hardware,
whereas SDR implementations allow for the modification of
any parameter or block in the receiver chain simply by
adjusting the software. This flexibility provides a significant
advantage in SDR-based implementations, making them an
invaluable prototyping tool for DSP engineers, who can
rapidly test and iterate different architectures or algorithms.
Figure 1. shows the GPS receiver block diagram based on
SDR. The antenna used in this work was a GPS commercial
patch antenna, designed for a frequency of 1575.42 MHz,
corresponding to the L1 band. The RF Front-End block was
implemented using an SDR device which will amplify, filter
a digitize the analog signal received by the antenna. To
choose the SDR hardware, we analyzed two options
available in our laboratory. It’s important to mention that
both devices are widely used due to their low cost and good
performance. The commercial HackRF One [24] is a
versatile SDR platform, designed for both transmission and
reception of radio signals across a wide frequency range
from 1 MHz to 6 GHz. This open-source hardware device is
ideal for testing, developing, and experimenting with a
broad spectrum of modern and future radio technologies.
Whether used as a USB peripheral or configured for
standalone operation, the HackRF One offers flexibility and
adaptability for a variety of applications, making it a
valuable tool for hobbyists, researchers, and professionals in
the field of wireless communications. The RTL-SDR [25] is
a cost-effective SDR receiver, widely popular among
hobbyists and professionals for its versatility and ease of
use. Originating from repurposed USB TV tuner dongles,
the RTL-SDR can receive frequencies from approximately
500 kHz to 1.75 GHz. It supports a wide range of
applications, including radio astronomy, weather satellite
image reception, ADS-B aircraft tracking, and general radio
scanning. Its affordability, coupled with an extensive array
of compatible software, makes the RTL-SDR an excellent
entry point into the world of SDR for beginners and a
valuable tool for experienced users. In Table 1, there are the
main characteristics of both devices.
Tests were conducted with both devices to evaluate their
reception capabilities. During the tests with the HackRF, it
was impossible to achieve a lock on any satellite. After
several tests, it was concluded that it needs an external clock
signal because its internal oscillator is not precise enough to
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process GPS signals, which require 1 ppm (part per million)
or less. Various software configurations were tried, even
reducing the frequency and time resolution requirements to
attempt to achieve a lock, but it was unsuccessful.
In the tests with the RTL-SDR dongle, excellent results were
achieved for the acquisition, lock, and decoding of the
navigation message, successfully obtaining the position
solution.
Based on Table 1, it is observed that both devices have a
suitable frequency range to capture the L1 carrier and both
have an 8-bit quadrature ADC. Regarding the sampling
frequency, the RTL-SDR is very close to the Nyquist
sampling theorem limit, but it satisfies it, unlike the HackRF,
which far exceeds it. Both have a bias-t, but only the dongle
has a <1 ppm TCXO. Although the HackRF is a transceiver
and the RTL-SDR is only a receiver, the application to be
developed only involves reception, so this characteristic
does not affect it, but the enormous price difference between
the two does, precisely due to that functionality. For all the
reasons mentioned above and based on the tests conducted,
without making any modifications to the devices, where
only the RTL-SDR achieved successful reception, we
believe that the latter best suits our needs.
TABLE I.
Comparison of technical characteristics of the SDR devices analyzed
III. METHODOLOGY
A. Signal characteristics
The Digital Signal Processing (DSP) block shown in Figure
1 consists of three main stages: acquisition, tracking and
demodulation. In this work we will focus on the acquisition
stage.
Each satellite, referred to as a Satellite Vehicle (SV),
currently has two unique spreading codes or sequences for
signal transmission. The first one is the unencrypted Coarse
Acquisition (C/A) code, assigned to civilian use, while the
second is an encrypted code, known as the P(Y) code,
designated exclusively for military applications and
restricted from civilian access. The C/A code is modulated
only on the L1 carrier frequency, whereas the P(Y) code is
modulated on both the L1 and L2 carrier frequencies. In this
study, our focus is on the C/A code, and its spectral
characteristics are illustrated in the power spectral density
diagram of the L1 band, as shown in Figure 2.
Fig. 2. Power spectral density diagram for the L1 band.[8]
The C/A code is a spread sequence that belongs to the family
of Gold Codes [26]. These sequences are commonly known
as pseudo-random codes or Pseudo-Random Noise (PRN)
because of their apparently random nature. Pseudo-random
codes are utilized because they do not repeat within their
sequence, providing highly advantageous correlation
properties for signal decoding. In the GPS system the C/A
codes are binary and deterministic pseudo-random
sequences with noise-like properties. Each code consists of
1023 chips, with a chip analogous to a bit, however, a chip
does not carry useful information. These sequences consist
of 512 ones and 511 zeros and repeat every 1 ms,
corresponding to a frequency of 1.023 MHz.
Each SV is assigned a unique PRN code for a specific
period, which may be updated over time [9]. This code is
crucial because all satellites transmit simultaneously on the
same carrier frequency using a Code Division Multiple
Access (CDMA) scheme. In practice, this is achieved
through a combination of CDMA and Direct Sequence
Spread Spectrum (DSSS) technology, resulting in what is
known as Direct Sequence CDMA (DS-CDMA). This
encoding technique involves performing a logical Exclusive
OR (XOR) operation between the PRN code and the data to
be transmitted. Consequently, the signal is transmitted with
a bandwidth much broader than that required by the data,
reducing the power spectral density. The resulting signal
exhibits a noise-like spectrum, making it undetectable and
undecodable without the correct PRN code. From all this,
the transmitted signal for a k satellite is given by:
where,
and
are the signal power of C/A and P
(L1 and L2) codes,
and
are the C/A and P (Y) codes
assigned to the k satellite,
is the navigation data, and
and
are the carrier frequencies L1 and L2. Since in the
SDR
Freq.
Range
[MHz]
Sampling
Rate
[MHz]
ADC
[bits]
TCXO
External
Clock
Price
[US$]
HackRF
One
1-6000
20
8
No
Yes
250
RTL-
SDRv3
24-1766
3.2
8
Yes
No
25
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present work we will only focus on the C/A code, which is
modulated in the L1 band, the parameters related to the P
and L2 codes are canceled. Then, our signal is simplified as
follows:
When propagating from the SV to the receiver in Earth this
signal is affected for different phenomena., mainly by the
deviation in the L1 carrier frequency due to the Doppler
effect and the propagation delay in the C/A code. The
Doppler effect is the result of the frequency shift caused by
the relative movement of the transmitter (located on the
satellite) with respect to the receiver on the ground, even
though the latter is static. The Doppler shift affects both the
process acquisition and GPS signal tracking. For a stationary
receiver, the maximum Doppler shift for the L1 frequency is
usually considered approximately ±5 KHz, and for a moving
one ±10 KHz. This effect, and the propagation delay on the
C/A code in its path from the transmitter to the receiver, are
two key parameters for the accurate decoding and
demodulation of the signal at the receiver. The estimation of
these parameters is the main function of the acquisition
stage, which is the focus of our study. So, we already know
the main characteristics of the transmitted signal, then we
can proceed to design the acquisition stage.
B. Acquisition algorithms
The acquisition stage aims to detect the satellites visible to
the receiver and obtain a first estimation of Doppler
deviation and code delay. We can express the received signal
as a combination of the n visible satellite signals:
Suppose we need to acquire the i satellite. The received
signal s should be multiplied by a locally generated C/A
code corresponding to the satellite being tracked. This
ensures that only if the phase of the local code matches the
phase of the received signal, meaning both codes are
perfectly time-aligned, the signals from other satellites, are
eliminated due to the correlation properties of the C/A codes.
Similarly, the carrier frequency must be filtered, as it is
affected by the Doppler shift. Therefore, knowing its exact
value is necessary for proper filtering. The acquisition
process can be viewed as a search for these two parameters
in a two-dimensional space or grid, called the Search Grid.
The axes of this grid are the Doppler Frequency and the
Code Delay. Since these parameters are continuous, we must
set a resolution for discretizing the space. The smallest
search resolution or step is called a bin, which results in a
frequency bin and a code bin. A combination of specific bins
represents a unique position within the search space, known
as a cell, as shown in Figure 3. To perform the search
process, a test statistic must be evaluated in each cell. The
result of this function is then compared to a predefined
threshold. This comparison determines whether the satellite
is present and identifies its synchronization parameters.
Fig. 3. Algorithm Search Space
A key aspect of any acquisition algorithm is defining the
dimensions of the search space, the frequency and code
search ranges, and the resolution between two consecutive
bins. For the code delay axis, the range is determined by the
length of the C/A code, specified by the GPS L1 C/A signal
specification. This code has a length of 1023 chips, starting
from position 0. The resolution between two consecutive
bins must be at least one chip, resulting in 1023 code bins.
However, it is possible to perform a higher-resolution search
using smaller separations between bins. For the frequency
axis, the range is typically ± 5 KHz for a static receiver and
±10 KHz for a moving receiver. The resolution is commonly
given by frequency bins equal to 1/T, where T is the coherent
integration time. This implies that as the integration periods
extend, the width of the frequency bins decreases, enhancing
the frequency resolution. The improvements in the
resolution of each axis depend exclusively on the algorithm
used. There are different algorithms to perform the search,
such as Serial Search (SS), Parallel Frequency Space Search
(PFSS), and Parallel Code Phase Search (PCPS) acquisition,
the latter being the option chosen in this work. The PCPS
algorithm, whose block diagram is shown in Figure 4,
performs the search by going through all possible frequency
values and parallelizing the search through the code
parameter, which has a significantly higher number of steps.
Instead of multiplying the input signal by the local PRN for
all possible code delays, as SS and PFSS algorithms do, it
uses the circular cross-correlation technique based on the
Fourier Transform. This technique relies on the property that
the Discrete Fourier Transform (DFT) of circular cross-
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correlation is equivalent to the multiplication of the DFT of
the code and the DFT of the input signal, taking the complex
conjugate of one of them. Performing the inverse transform
of this multiplication yields the circular cross-correlation in
the time domain. The squared magnitude of this result
represents the test statistic and is compared with a
predefined threshold to determine if the desired satellite has
been acquired. If there is a peak in the test statistic, its index
over the code dimension indicates the delay of the PRN code
of the input signal for the analyzed frequency.
Fig. 4. Block Diagram of Parallel Code Phase Search acquisition
algorithm
This algorithm sweeps only over the frequency dimension,
that is, across all possible frequencies, which results in the
number of combinations given by:
The algorithm allows for defining the resolution and bin size
in the frequency dimension. However, this choice is not
entirely independent, as it depends on the length of the data
on the DFT, which means the number of samples analyzed,
and consequently the integration period. The size of a
frequency bin is commonly defined as follows:
which results in a resolution (the maximum frequency
separation between two consecutive bins) of 1/2T [12]. For
example, for integration periods of 1 ms, frequency bins of
1 kHz are obtained, resulting in a maximum resolution of
500 Hz. For integration periods of 2 ms, frequency bins of
500 Hz are obtained, resulting in a maximum resolution of
250 Hz. To get better resolution, a longer integration period
must be used to increase the number of DFT samples in
equation 5. Additionally, the algorithm indirectly improves
the resolution in the code dimension by increasing the
number of samples, either by increasing the sampling
frequency fs or the integration time T, as a correlation value
is obtained for each sampling instant. For example, using a
sample rate of 2 MHz and an integration time of 1 ms yields
2000 samples, representing 2000 bins of code instead of
1023. That is 2000/1023=1.96 samples for each bin,
resulting in a resolution of 1/1.96=0.51 between bins. In
summary, the accuracy of the parameters estimated with this
algorithm is ±1/2 bin in frequency, and in the code
dimension, it depends on the sampling frequency.
A good way to compare the algorithms mentioned above is
by evaluating the number of combinations each requires and
the complexity of their implementation, as shown in Table
2.
TABLE II.
Results of the comparison between SS, PFSS, and PCPS Acquisition
algorithms.
Algorithm
Combinations
Complexity
Serial Search Acquisition
41943
Low
Parallel Frequency Space
Search Acquisition
1023
Medium
Parallel Code Phase Search
Acquisition
41
High
As observed, the Serial Search algorithm performs the worst
due to the large number of combinations needed compared
to the other two algorithms. The performance of both
parallelized algorithms strongly depends on the
implementation of the DFT. There are many
implementations for the DFT, the Fast Fourier Transform
(FFT) stands out as the fastest and most widely used.
As previously explained, the acquisition process involves
searching in a two-dimensional grid to determine if the
target satellite is present in any cell and, if so, obtaining its
coordinates, which correspond to the synchronization
parameters Doppler shift and code phase. This requires
evaluating the test statistic in each cell (SS), column (PFSS),
or row (PCPS) processed, depending on the algorithm, and
comparing it with a pre-established threshold to decide if the
satellite is present. In all three algorithms, the test statistics
are implemented using the mathematical function "squared
modulus." There are different ways to find the maximum
value of the test statistic across the entire search space,
which will be compared to the threshold. In the chosen
implementation PCPS, the value corresponding to the
maximum of the first row (or the first frequency) processed
is initially stored along with its position on the grid. If the
statistic for the new analyzed frequency exceeds the stored
value, the new value is saved; otherwise, the process moves
to the next frequency. This process continues until all
frequencies are examined, resulting in the identification of
the maximum test statistic across the entire search space.
Calculating the decision threshold is critical for any
acquisition algorithm. If the threshold value is too low, it
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leads to more false positives; if too high, it may miss
detecting satellites. Advanced techniques like the Constant
False Alarm Rate (CFAR) [27,28] can calculate adaptive
thresholds, but they add complexity and are beyond the
scope of this work. Therefore, a fixed threshold of 0.019 is
implemented, derived from averaging numerous tests under
various propagation conditions, which can also be modified
by the user to adapt to different conditions and locations.
Techniques for determining satellite presence can be
classified based on the number of times or windows a cell is
analyzed during a non-coherent integration period:
• Single-dwell: Analysis is performed only once per
cell to decide, using a single coherent integration
window.
• Multiple-dwell: Analysis is repeated on the same
cell at regular intervals, with averaging performed
to decide, using two or more coherent integration
windows that form a non-coherent integration
window. The number of coherent integrations and
the required positive acquisitions (max dwells)
must be defined for acquisition over the non-
coherent integration window.
A related parameter is the "dwell time," which is the time
needed to verify the satellite presence. For single-dwell, it
equals the coherent integration period; for multiple-dwell, it
is the product of max dwell and the coherent integration
time. Figure 5 illustrates a multiple-dwell technique with
three coherent integration windows. If max dwells are set to
two, at least two of the three integrations must show positive
acquisition for cell acquisition. The developed tool allows
users to choose between single-dwell or multiple-dwell
decisions, configuring the number of dwells and max dwells
accordingly. The navigation data is transmitted at 50 bps,
resulting in a 20 ms period where the bit is set to 1 or -1,
after which a transition may occur. If a bit of transition
occurs during the acquisition process, it can cause errors. To
ensure optimal algorithm performance, no bit transitions
should occur in the analyzed data sequence. While longer
data sequences increase the likelihood of successful satellite
acquisition, they also demand more processing time and
capacity. Additionally, the frequency resolution, which is
inversely proportional to the integration time, improves with
longer integration periods, enhancing the number of DFT
samples. For all these reasons, the length of the data to be
analyzed should be chosen carefully. This length should not
be less than 1 ms, which is the duration of a complete C/A
code. Using a shorter length would result in correlations
with incomplete codes, affecting the performance of the
algorithm. Therefore, the length should be an integer
multiple of 1 ms. A recommended duration is 2 ms, as 1 ms
does not provide good frequency resolution. However, in our
implementation, this parameter can be configured by the
user for each execution of the program.
Fig. 5. Structure of an integration window with multiple dwells
Fig. 6. Block Diagram of the initial version of the implemented algorithm
C. Implementation and Optimization
To implement the proposed algorithm, it is necessary to
locally generate the PRN code of the satellite to be searched.
Additionally, a small adjustment to the number of samples
for each chip is required. The signal samples entering the
acquisition stage have been sampled by the ADC at a
specific frequency, in our case 2 MS/s. Therefore:
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Considering
and
then
from equation 6 the number of samples per chip will be
. It follows that for the 1023 C/A code chips, our
block must generate samples for each
C/A code period, as shown below:
The initial version of our algorithm shown in Figure 6 was
designed to search for only one satellite to validate the
studied methods. The software tool was developed entirely
in Python. After the first test, we observed a bottleneck in
data processing, which significantly increased the
processing time. To address this issue, we decided to modify
the data reading process by implementing generating
functions. These functions generate each data item only
when it needs to be processed, like lists functions, but
without keeping the data in memory. The data doesn't exist
until requested, eliminating the need to load the entire list
before using it. Generating functions are especially useful
when dealing with large datasets, where lists can consume
Fig. 7. Block Diagram of part of the modified version of the algorithm
significant memory, or when data is received in real-time,
such as an SDR device, making it impractical to wait for all
data to arrive before processing. Furthermore, a new
acquisition strategy was adopted. To detect a satellite that is
visible at any given second, and assuming it remains visible
for at least 0.5 to 1 second, it is sufficient to search for it
once, twice, or multiple times per second, depending on the
number of integrations selected. It is not necessary to search
for the satellite in every sample throughout all seconds of
the file. Once the performance issues with the algorithm
were resolved, we began the scaling process to search for all
32 satellites, our main aim. The simplest and fastest way to
implement this is through a classic for loop, which iterates
through all the satellites and applies the proposed algorithm
to each one. The main disadvantage of using a for loop is
that it was designed to search for a single satellite at a time,
and in each iteration, the process of reading the input
samples is repeated, even though the samples are always the
same. Although it is possible to modify the reading process
so that it is done only once and then the 32 satellites are
searched through the loop, it was already observed in the
first implementation that the processing took a considerable
amount of time. Furthermore, as is characteristic of the for
loop, it is a blocking execution which means that we must
wait for the search for one satellite to finish before starting
the next one, despite there being no limitations between
individual searches for each satellite. For these reasons, and
with a future implementation that can process samples in
real-time in mind, we decided to investigate different
parallelization methods at the processor level that allow for
searching more than one satellite at the same time. This
approach would also take advantage of the multicore
architectures of current processors to improve performance.
Currently, it is common practice to parallelize code by
isolating a specific function that can be executed multiple
times and running it on different processors to enable
processors on a machine, by running independent parallel
TABLE III.
Comparison between different methods of Multiprocessing module in
Python
processes using child processes instead of threads. The
maximum number of processes that can run at the same time
is limited by the number of host processors. The
multiprocessing.Pool() class provides the apply(), map(),
and starmap() methods to execute the passed function in
parallel. Choosing the most suitable one for our
implementation must consider the following parameters:
multiple arguments, concurrency, blocking and order of
results. Table 3 presents a summary of the methods and
parameters mentioned, with the aim of being able to
compare them. All three methods offer synchronous and
asynchronous variants. Due to the design of our algorithm,
it requires a method that can handle multiple arguments and,
for simplicity, able to return ordered results. According to
Methodology
Multiple
Arguments
Concurrence
Blocker
Sorted
Results
Pool.map
No
Yes
Yes
Yes
Pool.map_async
No
Yes
No
Yes
Pool.apply
Yes
No
Yes
No
Pool.apply_sync
Yes
Yes
No
No
Pool.starmap
Yes
Yes
Yes
Yes
Pool.starmap_async
Yes
Yes
No
No
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Table 3, the only method that satisfies these criteria is
Pool.starmap. To implement this, we first initialize the n
processors using the Pool class, then pass the functions we
want to execute in parallel to starmap. In our case, we set
the number of processors to the total number of system
processors minus one, to avoid overloading the operating
system and to prevent potential issues. This is a common
practice that ensures efficient use of resources when running
algorithms on platforms with many cores. Then, the
structure of our initial algorithm is modified as shown in
Figure 7.
Another modification, as shown in Figure 7, was also made
regarding the way of generating the PRN codes belonging to
each satellite. Previously, a single code belonging to the
searched satellite had to be generated at the time of
execution. But when searching for the 32 satellites, instead
of generating the codes one by one each time, it is
convenient to pre-allocate or previously generate a list with
the codes of the 32 satellites, removing this function of the
interior of the diagram proposed in Figure 6.
IV. RESULTS
To validate our implementation, we compared the obtained
results with the generated by the GNSS-SDR software tool.
To achieve this, the developed tool allows to record samples,
and read raw sample files, whether they were recorded using
this tool or with the GNSS-SDR software. Due to the
existence of two different types of sample files, our reading
function fits to both formats (packaging type, number of
bytes per sample, sign and amplitude correction factors).
Furthermore, it has been developed so that, if we need to add
another type of reading format from a different SDR device,
it is relatively easy to extend the code if the characteristics
of the samples are known.
We chose three cases to compare these tools using GNSS-
SDR format files, modifying two parameters, the coherent
integration time and the Doppler resolution. The cases
analyzed are using an Integration time of 1, 2 and 4 [ms] and
a Doppler resolution of 1000, 500, 250 [Hz], corresponding
to Case 1, 2 and 3 respectively. The results obtained from
the comparison are summarized in Table 4.
As an additional verification method to identify the satellites
present (excluding their synchronization parameters)
detected by the developed tool, the GNSS Status application
for Android devices was used while recording the signal file.
This application displays real-time information on the
GNSS satellites present at the location of the device,
including signal-to-noise ratio, elevation and azimuth
angles, position, and accuracy, among other parameters. As
shown in Figure 8, the satellites detected during the file
recording include satellites 2, 12, 24, 25, and 29. These
results agree with those obtained by our tool and GNSS-
SDR tool. Although the application lists additional satellites,
their acquisition is significantly influenced by factors such
as signal-to-noise ratio, line of sight, and mask angle.
Fig. 8. List of Satellites acquired by GNSS Status mobile application while
recording data samples using RTL-SDR.
TABLE IV.
Comparison of the results obtained processing the GNSS-SDR input
format files with GNSS-SDR software tool, and the tool developed in this
work.
Case
Satellite
Software Tool
GNSS-SDR
This work
Doppler
Delay
Doppler
Delay
1
2
-
-
0
657
12
1000
1274
1000
797
29
-
-
2000
782
2
2
-500
1620
-500
657
12
1000
876
1000
796
24
-1500
1194
-1500
437
25
2500
1900
2500
651
29
2500
1020
2500
781
3
2
-
-
-250
657
12
1000
649
1000
793
24
-1500
2000
-1500
439
25
2750
1462
2750
645
29
2250
385
2250
775
After searching for all the satellites with the tool, it is known
which of them are present, so that individual searches can be
carried out for them and the graphical representation of the
search space can be observed. For example, Figure 9 shows
the plots obtained by our tool and by GNSSSDR, using an
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Fig. 9. Three-dimensional plots of the search space for our tool (left column) and GNSS-SDR tool (right column) for Satellite 29 (Top row), Satellite 25 (Middle
row) and Satellite 1 (Bottom row).
integration Time = 2 [ms] and Doppler Resolution = 500
[Hz], for the individual searches of satellites 29 and 25 both
present and satellite 1, absent. On the top and middle rows
of Figure 9, corresponding to satellites 29 and 25
respectively, we can see that there is a maximum peak on the
correlation matrix represented in the search space with
coordinates in the Doppler frequency and delay axes that
coincide with the values of these parameters previously
obtained by the tool during the search for all satellites. In
other words, the acquisition of these satellites has been
achieved with those synchronization parameters. Also, we
can see on the bottom row of Figure 9 that the same does not
apply to satellite 1, which is not found as it is absent.
As shown in Table 4, each execution of the developed tool
consistently produces the same code delay values for the
same input file. In contrast, the GNSS-SDR software tool
yields varying delay values between executions, which do
not match those produced by our tool. This variability is
attributed to the internal workings of GNSS-SDR, which is
designed to operate in real-time across various SDR devices.
It internally relies on the GNU Radio Scheduler, where each
processing block runs as quickly as possible. This causes the
exact execution order to fluctuate based on the machine's
current load, making the process non-deterministic.
However, this randomness in execution does not
significantly affect the final position calculation, resulting
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only in minor variations in the decimal points of longitude,
latitude, and altitude. If the hardware environment is known,
real-time requirements are absent, or a fixed and known
amount of data is read from a file (buffer size), the sample
management can be more controlled, allowing for orderly
and efficient processing. This ensures a deterministic
workflow, as demonstrated by the developed tool, where
the same input consistently produces the same output.
Additionally, the Doppler frequency results generated by the
tool are identical to those obtained by GNSS-SDR in every
execution.
V. CONCLUSIONS
In this work, a software tool was developed to implement
the acquisition stage of GPS signals using SDR technology.
The tool successfully detects satellite signals and their
associated parameters, allowing users to configure various
algorithm settings quickly and intuitively. Besides, it
provides a graphical representation of the satellite search
space in three dimensions and enables results to be saved for
further analysis. The tool was fully implemented in Python,
a widely used programming language in the scientific
community, which simplifies maintenance and updates. Its
compatibility ensures that it can run seamlessly on any
computer with Python installed. The Python code used in
this study is available in the GitHub repository “PyGNSS-
SDR” [29]. The tool supports input data files in multiple
formats, including those generated by RTL-SDR and GNSS-
SDR devices, providing flexibility for different research
scenarios. Validation tests demonstrated excellent
agreement between the results of the developed tool and
those obtained using GNSS-SDR software, confirming its
accuracy and reliability.
While the tool currently represents only the acquisition
block of a GNSS receiver, it establishes a robust foundation
for the development of a complete receiver. The envisioned
system is intended for applications such as supporting
Ground-Based Augmentation Systems (GBAS), conducting
ionospheric scintillation studies, and providing GPS signal
integrity reports. The use of SDR technology offers a highly
flexible and cost-effective approach to GNSS receiver
development, particularly in the current landscape of multi-
constellation systems (GPS, GLONASS, Galileo, Beidou)
and multi-frequency bands. SDR platforms democratize
access to GNSS research, enabling researchers and
developers to engage in this field without the prohibitive
costs associated with traditional hardware-based
approaches. The tool developed in this project introduces a
novel testing framework for GNSS research and
development in our country. Future work will focus on
implementing the tracking and demodulation stages,
extending support to additional frequency bands (L2 and
L5), and enabling compatibility with other constellations
(GLONASS, Galileo, Beidou). Other planned advancements
include integrating adaptive thresholding techniques, such
as Constant False Alarm Rate (CFAR), for enhanced
detection threshold calculation. These developments aim to
further expand the capabilities and applications of the tool
in GNSS research.
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