CH2 Literature Review & State‐of‐the‐art
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The average document age of 5.37 years suggests that the field is relatively young but maturing. An average of 12.74 citations per document indicates a moderate level of impact and recognition within the academic community.
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The high number of Keywords Plus ( Keywords Plus (ID): 5064, Author's Keywords (DE): 2035) and Author's Keywords indicates a wide variety of research topics and themes within the field. This diversity suggests that the field is multidisciplinary and covers numerous subtopics.
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With only 14 authors of single-authored documents, it is evident that collaboration is common in this research area. The majority of the research is conducted by teams rather than individual authors ( Grouped in 5 major clusters)
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An average of 3.56 co-authors per document highlights a collaborative research environment. The international co-authorship rate of 21.86% shows significant global collaboration, indicating that the research area is internationally recognized and researchers frequently work across borders.
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The majority of the documents are journal articles (360) and conference papers (281), which is typical for many scientific fields. The presence of 51 review papers suggests that there are substantial efforts to synthesize and summarize existing research, which is crucial for guiding future studies. (around 12%)
Based on [1], the following steps outline the process for conducting a literature review:
- RQ1: What are the advantages and disadvantages of FPGA-based implementations of Kalman filters vs software implementations?
- RQ2: What are the trends in FPGA-based implementations of Kalman Filters in academics?
- RQ3: How can FPGA-based implementations of Kalman filters, using VHDL, improve the performance and accuracy of IMU sensor fusion for quadcopters and prediction in power electronics?
- RQ4: What are the latest advancements in implementing Kalman filters on FPGAs?
- RQ5: How do FPGA-based Kalman filters compare with software-based implementations in terms of performance, power consumption, and resource utilization?
- RQ6: What are the current methodologies for IMU sensor fusion using Kalman filters in quadcopters?
- RQ7: What are the challenges and solutions in predicting power electronics parameters using Kalman filters?
- RQ8: What optimizations and innovations have been proposed to improve the efficiency of Kalman filters on FPGAs?
The research on FPGA-based Kalman filters is situated at the intersection of signal processing, control theory, and hardware design. Previous studies have demonstrated the potential of Kalman filters in various applications, from aerospace to finance, due to their ability to provide optimal state estimates in noisy environments. FPGAs, known for their parallel processing capabilities and reconfigurability, present a promising platform for implementing these filters. However, existing studies often focus on either software-based implementations or specific applications without thoroughly exploring the integration of Kalman filters on FPGAs for real-time, high-performance applications such as quadcopter navigation and power electronics.
- Software-based Implementations: How many studies focus on this? Why is this approach chosen? What are they losing by not using FPGA?
- High-Performance Metrics: Define what constitutes high performance and how much better FPGA implementations are.
- Performance Improvements: FPGA-based implementations of Kalman filters can achieve significant performance improvements over software-based solutions, particularly in terms of speed and latency.
- Resource Utilization: Efficient VHDL coding and FPGA resource management are crucial for optimizing the performance of Kalman filters.
- Application-Specific Optimizations: Tailoring the Kalman filter design to specific applications, such as IMU sensor fusion and power electronics, can enhance accuracy and reliability.
- Challenges: Common challenges include managing power consumption, handling non-linearities (addressed by Extended Kalman Filters), and ensuring scalability.
- Kalman Filtering: A recursive solution to the discrete-data linear filtering problem.
- Extended Kalman Filtering (EKF): An extension for non-linear systems.
- Distributed Kalman Filtering (DKF): Adaptation for distributed sensor networks.
- VHDL Programming: For describing the hardware implementation of Kalman filters.
- FPGA Design and Simulation: Using tools like GHDL for simulation and synthesis.
- Digital Signal Processing (DSP): Techniques for handling and processing sensor data.
- Bibliometrix: For bibliometric analysis of literature.
- Atlas.ti: For qualitative data analysis.
- FPGA Development Tools: Such as Xilinx Vivado and Altera Quartus for FPGA design and testing.
- Optimization Techniques: Developing new VHDL optimization techniques to reduce FPGA resource usage and power consumption.
- Hybrid Filtering Approaches: Combining traditional and extended Kalman filters to handle both linear and non-linear dynamics effectively.
- Scalable Architectures: Designing FPGA architectures that can be easily scaled for different levels of complexity and application requirements.
- Performance Benchmarks: Lack of comprehensive performance benchmarks comparing different FPGA-based Kalman filter implementations.
- Application-Specific Studies: Limited studies focusing on the specific applications of IMU sensor fusion in quadcopters and prediction in power electronics.
- Resource Utilization Metrics: Need for detailed analysis on how different VHDL coding practices impact FPGA resource utilization and power consumption.
The current body of knowledge highlights the effectiveness of Kalman filters in various domains, particularly when implemented on FPGAs for real-time applications. However, there is a significant scope for research in optimizing these implementations for specific high-performance applications such as drone navigation and power grid management. The use of advanced FPGA features and optimization techniques remains underexplored, presenting an opportunity for significant contributions to the field.
The aim of the systematic review is to compare, synthesize, and assess various studies on FPGA-based Kalman filters to determine their effectiveness in improving performance and accuracy in IMU sensor fusion and power electronics prediction. This review will help reveal the range and reliability of results, identify best practices, and provide a foundation for future research in this area.
Based on [1] this are important field to check
- Journals – you might limit your search just to certain trusted ones;
- Date of publication;
- Named authors – you might limit it to certain authors whose work is well-known in this area;
- Location – where the research was undertaken a hospital, health department, factories, banks, and so on;
- Funding – consider whether it was funded by a commercial concern, which might affect its impartiality;
- Research question – you might only consider those that address a research question relevant to yours;
- Type of methodology;
- Population and sampling methodology – consider what size of sample was chosen and how the subjects were selected;
- Intervention – you will have to consider what sort of intervention was involved and how the data was analysed.
[1] B. Greetham, How to write your literature review, 1st ed. in Macmillan study skills. London: Macmillan International Higher Education, 2021.
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This page outlines the systematic literature review (SLR) plan for the project on FPGA-based Kalman filters using VHDL, focusing on applications in IMU sensor fusion for quadcopters and power electronics prediction. The review will use multiple databases and tools, including Bibliometrix and Atlas.ti, to ensure a comprehensive analysis.
- RQ1: What are the latest advancements in implementing Kalman filters on FPGAs?
- RQ2: How do FPGA-based Kalman filters compare with software-based implementations in terms of performance, power consumption, and resource utilization?
- RQ3: What are the current methodologies for IMU sensor fusion using Kalman filters in quadcopters?
- RQ4: What are the challenges and solutions in predicting power electronics parameters using Kalman filters?
- RQ5: What optimizations and innovations have been proposed to improve the efficiency of Kalman filters on FPGAs?
- Scopus
- arXiv
- Google Scholar
- IEEE Xplore
- SpringerLink
- Kalman filter
- FPGA
- VHDL
- IMU sensor fusion
- Quadcopter navigation
- Power electronics prediction
- Digital signal processing (DSP)
- Real-time systems
- Extended Kalman filter (EKF)
- Distributed Kalman filter (DKF)
For IEEE Xplore: ("Kalman filter" AND "FPGA") AND ("VHDL" OR "IMU" OR "quadcopter" OR "power electronics")
- Publication Date: Last 10 years
- Document Type: Journals, Conferences
- Subject Areas: Signal Processing, Aerospace, Robotics, Power Electronics
- Papers published in peer-reviewed journals or conferences
- Studies focusing on Kalman filter implementation on FPGA
- Research on VHDL programming for Kalman filters
- Articles discussing applications in IMU sensor fusion and power electronics
- Publications from the last 10 years
- Studies not related to Kalman filters or FPGA
- Non-peer-reviewed articles, white papers, and opinion pieces
- Papers not available in full text
- Publications older than 10 years unless they are seminal works
- Search and Collect Literature: Execute the search strategy across the selected databases. Export the results and maintain proper records of search results and filters used.
- Screen and Select Studies: Screen the collected papers based on title and abstract. Apply the inclusion and exclusion criteria to select the most relevant studies.
Extract the following data from the selected studies:
- Bibliographic Information: Title, authors, publication year, journal/conference
- Study Focus: FPGA architecture, Kalman filter type (standard, EKF, DKF), application domain (IMU, power electronics)
- Methodology: Implementation details, VHDL code aspects, simulation setup
- Results: Performance metrics, comparison with other methods, advantages, and limitations
- Conclusion: Key findings, contributions, and future research directions
Evaluate the quality of the selected studies using a predefined checklist, focusing on aspects such as methodological rigor, clarity of presentation, and relevance to the research questions.
Analyze and synthesize the extracted data to answer the research questions. This involves comparing different studies, identifying trends, and summarizing key findings.
Using Bibliometrix, the following metrics can be used:
- Publication Count: Number of papers published per year
- Citation Analysis: Number of citations per paper, H-index, G-index
- Co-authorship Analysis: Collaboration patterns among authors, institutions, and countries
- Keyword Analysis: Frequency and trends of keywords and terms used
- Source Analysis: Analysis of journals, conferences, and publishers
- Thematic Mapping: Identification of key themes and their evolution over time
Atlas.ti can be integrated into the SLR for qualitative analysis of the literature:
- Import Documents: Import the full-text articles into Atlas.ti for coding and qualitative analysis.
- Code Development: Develop a coding scheme based on the research questions and extracted data.
- Thematic Analysis: Identify and analyze themes and patterns within the literature.
- Network Visualization: Use Atlas.ti's tools to visualize relationships and networks among concepts, authors, and themes.
Structure the systematic literature review report as follows:
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Introduction
- Background and motivation for the review
- Research questions and objectives
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Methodology
- Search strategy
- Selection criteria
- Data extraction and quality assessment processes
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Results
- Overview of selected studies
- Synthesis of findings for each research question
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Discussion
- Interpretation of results
- Comparison with existing literature
- Practical implications and challenges
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Conclusion
- Summary of key findings
- Recommendations for future research
- Search and Data Collection: Perform the search on each database and export the results.
- Screening and Selection: Use a tool like Rayyan for systematic reviews to screen the articles.
- Data Extraction and Quality Assessment: Use Excel or a similar tool to organize and assess the extracted data.
- Bibliometric Analysis: Use Bibliometrix for quantitative analysis of the literature.
- Qualitative Analysis: Use Atlas.ti for thematic analysis and network visualization.
- Synthesis and Report Writing: Compile the report, ensuring proper citation and referencing.
By following this plan, you can conduct a thorough and systematic literature review that provides valuable insights into the implementation of Kalman filters on FPGA platforms for IMU sensor fusion and power electronics prediction.
