About

Professor Dennis Akos is the director of the Radio Frequency and Satellite Navigation Laboratory at the University of Colorado Boulder. He implemented the first Global Positioning System (GPS) software defined radio in the mid-1990s, marking a significant milestone in satellite navigation technology. Since establishing the laboratory in 2005, Professor Akos, along with his collaborators and students, has advanced various aspects of satellite navigation technology, including antenna design, advanced radio processing, and digital signal processing. His work initially focused on the US Global Positioning System (GPS) but has since expanded to include the other global constellations: GLONASS, Galileo, and Beidou, as well as regional space-based augmentation systems. The efforts led by Professor Akos and his group have been instrumental in propelling low-cost GPS/GNSS technology into mobile phones, pioneering remote sensing receiver architectures using GPS/GNSS signals, and enhancing integrity and robustness in satellite navigation. The Aerospace Engineering Sciences department at the University of Colorado offers detailed instructional courses on satellite navigation operation, reflecting the importance and prevalence of GPS/GNSS technology despite its commoditization in the commercial marketplace. Current research under Professor Akos focuses on ensuring robust, continuous, and high-integrity operation of GPS/GNSS for all users across all platforms. His work also explores unique GPS/GNSS receiver architectures tailored for specialized applications such as space operations and remote sensing, as well as the use of GPS/GNSS technology for autonomous vehicles.

Research topics

• Computer Science
• Telecommunications
• Geography
• Real-time computing
• Engineering
• Geodesy
• Computer Security
• Aerospace engineering
• Geology
• Remote sensing
• Meteorology
• Embedded system
• Operating system
• Environmental science

Selected publications

• Performance Evaluation of the Network Location Provider In Android devices

  2025-04-28 · 1 citations

  articleSenior author

  The Network Location Provider (NLP) in Android devices plays a critical role in location services, especially in GNSS-challenged environments such as indoors or areas affected by RFI (Radio Frequency Interference). Leveraging Wi-Fi fingerprinting and cell tower localization methods, NLP operates continuously and supports the Fused Location Provider (FLP). However, its internal mechanisms remain largely undisclosed. In this study, we evaluate NLP performance and accuracy using Google Pixel 6 and Pixel 9 smartphones across various environments—indoor, rural, suburban, and urban—using a u-blox F9P receiver as ground truth. Our results reveal that Wi-Fi availability is the dominant factor in NLP accuracy, with errors exceeding 1 km without Wi-Fi, but reduced to sub-100 meters with W-Fi enabled. Interestingly, the newer Pixel 9 offered no clear advantage over Pixel 6, indicating that NLP accuracy is more influenced by server-side algorithms and database quality than hardware. We also explored how dynamic or spoofed inputs could affect NLP. A simulated moving Wi-Fi hotspot had no immediate impact, and we raise the hypothesis that spoofed GNSS tags could poison the underlying database. These findings offer insights into NLP’s mechanisms and contribute to ongoing efforts to enhance smartphone-based Positioning, Navigation, and Timing (PNT) technologies, particularly in environments where GNSS is unreliable.

  PublisherDOI

• Stress-Testing Flagship Smartphone Models With Real-World GNSS RFI to Determine Real-Time Emitter Localization Capabilities

  2025-04-28 · 4 citations

  article

  In 2022, Denver International Airport experienced GPS RFI that disrupted flights for 33 hours before the source was found and shut off. Also in 2022, Dallas Fort Worth International Airport experienced 44 hours of GPS RFI that closed a runway, and in this case the source was never found. Due to the rise of GNSS RFI, much research is being done to explore alternative positioning methods to complement or replace GNSS. These methods require expensive new equipment to be installed for both the providers and clients of the service. Alternatively, this paper demonstrates that a "J911" system that uses crowdsourced mobile phone data to localize an emitter is now feasible, as first described by Scott in 2011. This paper analyzes current flagships smartphone’s and their GNSS chipset to determine RFI localization capabilities, and deploys 15 of the best performing phones to locate a real-world emitter in real-time at Jammertest 2024.

  PublisherDOI

• GNSS Jamming and Spoofing Monitoring Using Low-Cost COTS Receivers

  ArXiv.org · 2025-09-17

  preprintOpen access

  The Global Navigation Satellite System (GNSS) is increasingly vulnerable to radio frequency interference (RFI), including jamming and spoofing, which threaten the integrity of navigation and timing services. This paper presents a methodology for detecting and classifying RFI events using low-cost commercial off-the-shelf (COTS) GNSS receivers. By combining carrier-to-noise ratio (C/N0) measurements with a calibrated received power metric, a two-dimensional detection space is constructed to identify and distinguish nominal, jammed, spoofed, and blocked signal conditions. The method is validated through both controlled jamming tests in Norway and real-world deployments in Poland, and the Southeast Mediterranean which have experienced such conditions. Results demonstrate that COTS-based detection, when properly calibrated, offers a viable and effective approach for GNSS RFI monitoring.

  PublisherOA PDFDOI

• Utilizing SBAS Signals for RFI Detection and Characterization

  Proceedings of the Satellite Division's International Technical Meeting (Online)/Proceedings of the Satellite Division's International Technical Meeting (CD-ROM) · 2025-10-01

  article

  Jamming and spoofing of Global Navigation Satellite System (GNSS) signals have become increasingly prevalent, particularly in connection with ongoing conflicts in Eastern Europe and the Middle East. Although these radio frequency interference (RFI) events are often localized to conflict zones, their collateral effects can extend across wide regions, spanning tens to hundreds of miles. The growing frequency of RFI disturbances in commercial contexts highlights the urgent need for robust detection and characterization systems to safeguard aviation, maritime, and other GNSS-dependent sectors. Prior work has shown that received power and signal-to-noise ratio metrics are effective for identifying RFI, as they capture the balance between received signal strength and noise floor levels. Leveraging these metrics, commercial GNSS receivers—without requiring hardware modifications—can detect and characterize both jamming and spoofing events. The effective monitoring range is influenced by receiver sensitivity, and when deployed as a network, such receivers can deliver critical RFI awareness for land, sea and air-based applications. In this paper, we present a LCM monitoring network implementation using data from the 2024 Jammer Test, demonstrating its capability to detect and characterize GNSS jamming and spoofing.

  PublisherDOI

• Performance Evaluation of the Latest Smartphones and Smartwatch Using GNSS Raw Measurements

  2025-04-28 · 4 citations

  article

  Google Smartphone Decimeter Challenge (GSDC) led to significant improvements in smartphone positioning over the past few years. Building upon this progress, our study pursues two main objectives. First, we evaluate the latest GNSS chipset performance across major smartphone manufacturers—including the Google Pixel 9, Pixel 6/7/8, Samsung S24, Xiaomi 14T Pro, and the Samsung Galaxy Watch 7—focusing on newly released devices not covered in the GSDC. This enables a closer examination of device-specific behaviors and limitations that remain underexplored. Second, we apply precise GNSS techniques such as DGNSS, PPK and PPP using RTKLIB, leveraging Tim Everett’s smartphone-optimized RTKLIB variant, which emerged from GSDC efforts. Using NMEA solutions as a baseline, we compare performance across positioning modes. Our findings show that all tested devices achieved reliable performance with RTKLIB, with PPK float accuracy reaching the 1-meter level. Preliminary results also suggest that the Galaxy Watch 7 delivers reasonable pseudorange quality and DGNSS capability. By offering insights into raw GNSS measurement quality and providing practical data processing recommendations, this study contributes to ongoing efforts to improve precise positioning on Android devices.

  PublisherDOI

• Real-World Spoofing Detection and Characterization Using Low-Cost Receivers

  Proceedings of the Institute of Navigation ... International Technical Meeting/Proceedings of the ... International Technical Meeting of The Institute of Navigation · 2025-02-13 · 3 citations

  article

  The Global Navigation Satellite System (GNSS) is vulnerable to Radio Frequency Interference (RFI) due to the low-powered nature of its signals. Spoofing is a type of RFI which sends GNSS-like signals to receivers making them believe they are at a false location. Recently, due to the conflicts taking place in Eastern Europe and the Middle East, there has been an increase in spoofing attacks that often also affect civilians far away from war zones. Although spoofing detection and characterization methodologies have been developed in the past, the previous lack of real-world spoofing data has limited studies to lab-based experiments or large-scale outdoor testing campaigns, both in artificial interference environments. While these artificial data are useful during the development process, real-world data are needed to understand the spoofing techniques used in uncontrolled environments. This paper analyzes GNSS data collected from the southeast Mediterranean Sea during the Summer of 2024 using two u-blox F9P receivers, one of the L1/L2 model and one of the L1/L5 model. The study’s goals are two fold: First to demonstrate the utility of low-cost GNSS receivers for detecting and characterizing spoofing. Second to assess the effectiveness of various previously developed spoofing detection techniques on a real-world dataset.

  PublisherDOI

• Assessment of Android Network Positioning as an Alternate Source for Robust PNT

  Sensors · 2025-12-02

  articleOpen accessSenior author

  Android devices employ several methods to calculate their position. This paper's focus is the Network Location Provider (NLP), which leverages Wi-Fi and cell tower signals via the fingerprinting/database approach. Unlike GNSS-based positioning, the NLP should be able to compute positions even when the device is indoors or experiencing GNSS radio frequency interference (RFI), making it an enticing candidate for ensuring robust PNT solutions. However, the inner workings of NLP are largely undisclosed, remaining as a 'black-box' system. Using the Samsung S24 and Xiaomi Redmi K80 Ultra, we explored the NLP's response to GNSS spoofing and offline operation (no network connection), as well as attempting NLP spoofing. The GNSS spoofing test confirmed that when satellite signals are spoofed, the NLP solution is maintained at the truth location. This reinforces the robustness of the NLP in RFI environments. In offline mode, NLP continued to operate without a Google server connection, indicating that it can compute positions locally using internally stored cache data. This behavior deviates from the conventional understanding of NLP and offers valuable insights into the latest NLP mechanism. These findings build upon previous work to uncover the inner workings of the NLP and ultimately contribute to robust smartphone PNT.

  PublisherOA PDFDOI

• GNSS L5/E5a Code Properties in the Presence of a Blanker

  NAVIGATION Journal of the Institute of Navigation · 2025-01-01

  articleOpen accessSenior author

  <h3>Abstract</h3> Modern global navigation satellite system L5/E5a code families offer improved correlation properties, with lower auto-correlation sidelobes and cross-correlations, compared with legacy Global Positioning System L1 coarse/acquisition (C/A) codes. However, these codes encounter unique L5/E5a interference environments, particularly those including interference due to pulses from distance-measuring equipment and tactical air navigation systems. In civil aviation, temporal blanking is the assumed countermeasure. In temporal blanking, incoming samples are set to zero when the peak envelope power exceeds a threshold, blanking the codes within the sampled signals and affecting their correlation with non-blanked replicas. Through extensive simulations, this study analyzes L5/E5a code properties under blanking duty cycle (<i>bdc</i>) values of 0%–75% over a 1-ms integration time. Results indicate reduced auto-correlation and cross-correlation protections, although these effects remain superior to those of L1 C/A codes until <i>bdc</i> reaches approximately 60%. Further increases in <i>bdc</i> to 75%, likely due to increasing air traffic, diminish these advantages. Additionally, simulations show that Doppler residuals have a minimal impact on L5/E5a correlation properties.

  PublisherOA PDFDOI

• Empirical Error Modeling of Android GNSS Using Machine Learning for PVT Improvement

  Proceedings of the Satellite Division's International Technical Meeting (Online)/Proceedings of the Satellite Division's International Technical Meeting (CD-ROM) · 2024-10-09 · 2 citations

  article

  Accurate modeling of GNSS measurement errors is imperative for an effective computation of GNSS position and velocity using raw measurements. This is because the model allows us to quantitatively define the accuracy of each measurement. There are multiple different ways to model the errors including the theoretical approaches using elevation angles and signals’ Carrierto-Noise-Ratio-Densities (C/No). Also, in the case of Android devices, the embedded Application-Specific Integrated Circuit (ASIC) chipsets also provide the expected measurement noises. Furthermore, there are empirical methods where the errors can be modeled using training data. The questions that may be raised include which theoretical model will provide the most accurate position, velocity, and time (PVT) solution, whether the chipset reported noises can be trusted, and how the empirical models should be trained. The novelty of the paper lies in addressing all these pending questions. The paper assesses the positional accuracies from each model and shows that the theoretical model, incorporating both elevation angles and C/No features, achieves the highest accuracy. Also, for the chipsets, Samsung and Qualcomm provide noise values that are representative of the expected measurement residuals, while MediaTek and Broadcom provide over-bounding estimates. Finally, the paper discusses the empirical modeling approach, detailing how an Extended Kalman Filter (EKF) can be applied to estimate and eliminate receiver clock bias and drift terms from the residuals before using them as model labels. For the model features, elevation angles, C/No, and user heading are utilized. A tree-based machine learning (ML) regression model is employed. The results indicate that when test environments closely resemble the training environments, the empirical models outperform the theoretical ones; however, when the environments differ, there is a decline in performance. All data for this study comes from the publicly available Google Smartphone Decimeter Challenge (GSDC) 2023 dataset, making the proposed methodologies and analysis easily replicable.

  PublisherDOI

• LOCALIZATION OF RFI EMITTERS IN GNSS CHALLENGED ENVIRONMENTS

  Advances in the astronautical sciences · 2024-01-01

  book-chapterSenior author
  PublisherDOI

Frequent coauthors

• Per Enge

  67 shared

• Sherman Lo

  51 shared

• Todd Walter

  28 shared

• Sam Pullen

  28 shared

• Dong-Kyeong Lee

  21 shared

• David S. De Lorenzo

  Trimble (United States)

  15 shared

• Valery U. Zavorotny

  Cooperative Institute for Research in Environmental Sciences

  13 shared

• Ming Luo

  University of Chinese Academy of Sciences

  13 shared

Education

• B.S., Electrical and Computer Engineering

  Ohio University

  1990

• M.S., Electrical and Computer Engineering

  Ohio University

  1992

• M.S., Mathematics

  Ohio University

  1996

• Ph.D., Electrical and Computer Engineering

  Ohio University

  1997

Awards & honors

• Fellow, Institute of Navigation (2022)
• Institute of Navigation Thurlow Award (2009)
• Samuel M.. Burka Award for the outstanding paper of the 2005…
• Best Paper at IEEE Position Location and Navigation Symposiu…
• Best Presentation Award, Institute of Navigation GPS-ION (20…