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The development of modern technologies has fundamentally transformed the field of radar signal and data processing. With the use of advanced algorithms and computational power, radars are now capable of extracting crucial information from received signals, facilitating improved target identification and tracking. This article presents some of the advanced technologies employed in radar signal and data processing and their impact on adaptability of radar systems. It traces the evolution of radar technology from old systems to the present, emphasizing the benefits of adaptive radar signal processing, which includes algorithms such as adaptive beamforming, Space-Time Adaptive Processing, and the integration of Machine Learning and Artificial Intelligence. In conclusion, challenges, and future prospects in the field of radar systems are discussed, with a focus on the potential integration of Artificial Intelligence methods, Cognitive radars, and Multiple Input Multiple Output technologies. Despite technical obstacles, opportunities emerge to enhance the performance of radar systems and achieve new levels of efficiency.
Abstract: The development of modern technologies has fundamentally transformed the field of radar signal and data processing. With the use of advanced algorithms and computational power, radars are now capable of extracting crucial information from received signals, facilitating improved target identification and tracking. This article presents some of the advanced technologies employed in radar signal and data processing and their impact on adaptability of radar systems. It traces the evolution of radar technology from old systems to the present, emphasizing the benefits of adaptive radar signal processing, which includes algorithms such as adaptive beamforming, Space-Time Adaptive Processing, and the integration of Machine Learning and Artificial Intelligence. In conclusion, challenges, and future prospects in the field of radar systems are discussed, with a focus on the potential integration of Artificial Intelligence methods, Cognitive radars, and Multiple Input Multiple Output technologies. Despite technical obstacles, opportunities emerge to enhance the performance of radar systems and achieve new levels of efficiency.
Keywords: Radar signal processing; Radar data processing; Adaptive radar; Adaptive algorithm.
1 INTRODUCTION
The development of modern technologies has the field of evolved signal and data processing in radars. These advancements have allowed for more efficient and precise detection, low probability of intercept (LPI), and electronic counter measurements. With the use of advanced algorithms and computational power, radars are now capable of extracting valuable information from the received signals, enabling better target identification and tracking. This paper presents some of the latest technologies used in radar signal and data processing and their radar performance impact.
M. I. Skolnik wrote in 1985: "Digital technology has allowed significant new capabilities in signal and data processing, and Very High-Speed Integrated Circuit (VHSIC) offers the promise of even greater performance. Most of the proposed radar applications of VHSIC seem to be describable as doing more of what is already being done. This is usually the case for any new technology, but it would not be surprising if, a decade hence, VHSIC is being used to achieve some new radar capability not now being pursued." 1 ] The following development proved him fully right, and high-performance integrated circuits, such as Field Programmable Gate Arrays (FPGAs), deeply affected the development of radars. [2],[3],[4]
The construction of modern radar systems, besides their specific features ensuring adaptability, also includes FPGAs. These fields, along with the corresponding adaptive algorithm, represent a comprehensive solution for processing the received signal. The choice of FPGA type and adaptive algorithm depends on the type of radar system, the environment in which the radar system is placed, and the type of transmitted and received signal. Radar systems that integrate FPGAs can provide a high degree of adaptability and flexibility in radar signal processing. [2], [3], [4], [5], [6], [7]
Nowadays, radar signal processing represents a complex set of digital techniques not only for receiving, but also for transmission and further manipulation of radar signals with the aim of obtaining high-quality information about the area of interest. It includes a wide range of advanced algorithms and computational methods, such as waveform design, modulation, demodulation, adaptive filtering, etc. which are applied to radar signals to enhance their quality and extract vital target information. These techniques enable radars to achieve high-resolution imaging, accurate target detection, improved signal-to-noise ratio, and other advanced functionalities, thereby playing a crucial role in the modernization and effectiveness of radar systems. [8],[9],[10]
In radar signal processing, the focal points lie in efficiency, speed, accuracy, and adaptability. The adaptability of radar systems is achieved through the incorporation of adaptive elements. These distinct components leverage their inherent properties and parameters to facilitate adaptive signal transmission, reception, filtering, and processing. [9],[10],[11],[12]
These modern technologies have innovated the field of radar signal processing, enabling more accurate and reliable information for various applications, including military surveillance, weather forecasting, and air traffic control. [10],[ll]
The primary objective of this paper is to represents a fundamental principles of adaptive radar signa processing. The inception of this paper delves into the intricacies of comparing technologies employes in old radar systems with contemporary advancements, primarily focusing on radar signal processing. A crucial aspect of radar signal processing, discussed in this article, is adaptability. This is achieved through the utilization of a digital signal processor with FPGA, where adaptive algorithms such as adaptive beamforming or Space-Time Adaptive Processing (STAP) can be programmed. In modern radars, ensuring adaptivity also involves the incorporation of Machine Learning (ML) or Artificial Intelligence (AI). By constructing a comparative model, these technologies can optimally identify a target in clutter and noise. The future integration of these algorithms may achieve faster processing, leading to real-time radar signal processing in big interferences.
2 RADAR TECHNOLOGY EVOLUTION
One of the key advancements in the radar technology evolution is the development of solidstate radar systems. Unlike traditional radar systems that utilize vacuum tubes, solid-state radars rely on transistors and integrated circuits, resulting in smaller and more efficient units. These modern radar systems offer higher reliability, increased flexibility, and improved radar signal processing capabilities, making them suitable for a wide range of applications, including weather monitoring, air traffic control, and military operations. Additionally, solid-state radars can generate highly accurate and detailed images, allowing for precise detection and tracking of targets in real-time. Overall, the evolution of radar technology has revolutionized radar signal processing capabilities and paved the way for more sophisticated and efficient radar systems. [13],[14], [15],[16]
Early radar systems, despite their innovative design and capabilities, were not without their limitations. One major drawback was their susceptibility to interference and jamming, which greatly impacted their effectiveness. Moreover, these systems had limited range and resolution, making it challenging to accurately detect and track fastmoving objects. The inability to identify and distinguish between multiple targets further hindered their utility in complex military operations. [13], [14]
In contrast, modern radar systems feature advancements in coherent optimization. State-of-theart antennas, such as Active Electronically Scanned Array (AESA) or Passive Electronically Scanned Array (PESA), demonstrate the capability to sustain phase coherence even under dynamic beam characteristics. The utilization of a robust and stable Transmitter (Tx), such as a solid-state Tx, significantly enhances the level of phase coherence in transmitted signals. Integrating a digital Receiver (Rx) with a high dynamic range in modern systems facilitates the maintenance of phase coherence and enables precise Digital Signal Processing (DSP). Consequently, DSP can achieve high coherence even with non-coherent and unstable transmitters or fully synthesized ones. This capability is particularly effective in military applications, where full radar adaptivity serves as a powerful tool for enhancing radar capability. In military contexts, the adaptability of transmitted signals can reveal the actual state of radar or scenario from the enemy's Electronic Warfare (EW) or Electronic Intelligence (ELINT) perspective. Radar's inherent chattiness further underscores the significance of adaptivity, making it clear that in military applications, adaptivity on the receiving side alone is not only more effective but also serves as a key aspect of Electronic CounterCountermeasures (ECCM). [15],[16]
Contemporary radar systems often incorporate advanced technologies that enable the synchronization of timing references, contributing to high time coherence. The design of new radar systems prioritizes coherence optimization throughout the entire system with fully adaptive components. Advanced antennas, Tx, Rx collectively contribute to a high degree of both phase and time coherence. [15],[16]
As shown in Tab.l, in older radar systems, antennas may exhibit limited capacity to maintain phase coherence, particularly during antenna rotation. Traditional Tx, such as klystrons or magnetrons, can suffer from restricted phase stability, thereby affecting the coherence of transmitted signals. Besides, older receiver, like superheterodyne or heterodyne Rx, may have coherence in received signals influenced by the construction and quality of amplifiers. [13],[14],[16]
Advancements in modern radar technology have greatly improved the detection capabilities of radars. Through the integration of sophisticated algorithms and processing techniques, radars demonstrate enhanced precision and accuracy in detecting and tracking targets. The development of cognitive radars further enhances adaptability and intelligence, allowing automatic adjustments and performance optimization based on the surrounding environment. [14],[15],[16]
The simplified block diagram of an advanced technology radar is illustrated in Fig. 1. The Tx of this radar is composed of semiconductor technology, emitting a signal with frequency diversity. This diversity involves the radar transmitting short and long pulses to ensure discriminative capability and radar range. These radars employ an adaptive phased-array antenna system, PESA or AESA and a receiver of the software-defined radio type. Radar signal processing of the received signal is carried out through DSP, which includes an FPGA with a programmed adaptive algorithm. This algorithm is optimized to the type of radar system, as well as its usage and environmental conditions. [15], [16]
These advancements in modern radar technology highlight the continuous efforts in radar signal processing advancements for improved radar capabilities. In conclusion, modern technologies have greatly improved radar signal processing. The use of advanced algorithms and ML techniques allows for better target detection and tracking, as well as enhanced identification of clutter and interference. [16]
3 ADAPTIVE RADAR SIGNAL PROCESSING
How we were mentioned, adaptive radar signal processing has revolutionized the way we analyze and interpret received signal, particularly with the integration of DSP and FPGA. These advanced technologies have significantly enhanced the capabilities of radar systems, allowing for more efficient and adaptive processing of signals. [17],[18]
Firstly, the incorporation of DSP in radar systems has played a crucial role in the transition from analog to DSP. ADC allows for the conversion of analog signals collected by radar antennas into digital signals, facilitating more precise manipulation and analysis. This transformation enhances the radar's ability to filter, modulate, and analyze data, providing valuable insights into targets' positions, velocities, and characteristics. [17],[18],[19]
Secondly, FPGAs have become integral in radar signal processing, providing a versatile platform adaptable to diverse radar system needs. The programmable nature of FPGAs empowers radar engineers to deploy adaptive algorithms finely tuned to the radar's unique features and environmental conditions. This flexibility is crucial for optimizing radar performance across changing scenarios, ensuring efficient and accurate radar signal processing. [5],[6]
Implementation of DSP with FPGA in radar systems enables the use of advanced adaptive techniques. Adaptive filtering algorithms, for instance, can be applied to mitigate interference, improve signal-to-noise ratio, and enhance overall radar accuracy and resolution. This collaborative approach empowers radar systems to dynamically adjust and optimize their performance, making them more responsive to the challenges posed by complex and dynamic operational environments. [18],[19]
In conclusion, the integration of DSP with FPGA in radar systems represents a paradigm shift in radar signal processing. These technologies not only enhance the efficiency and precision of radar data analysis but also enable adaptive strategies that contribute to the overall effectiveness of radar systems in diverse scenarios. [17],[18],[19]
The principle of adaptive radar signal processing is illustrated in Figure 2. Adaptive radar signal processing provides the capability for the parameters of signal processing to dynamically adjust in realtime based on the characteristics of the input signal. In Figure 2, two radar systems are depicted, each composed of N adaptive, phase-linear systems that receive the reflected signal from the target. These received signals undergo processing in an adaptive processor, which, based on the parameters of the input signal, generates weighting coefficients or vectors. Subsequently, these coefficients undergo filtering, optimization, and processing. The results from both radar systems are then subjected to spatiotemporal decoding. Following this process, the output contains a signal that carries information about the target. [17],[18],[19],[20]
Most used DSP technique in radars is pulse compression. This technique allows for the separation of echoes from different targets that are closely spaced in time. By transmitting a long-coded pulse, the radar can distinguish echoes from closely spaced targets based on their unique codes. For pulse compression using nonlinear or linear frequency modulation (LFM) signals, the compressed signal y (%) can be given by: [20], [21] ...co
...
where х(т) is the received signal, s(t-i) represents a time-shifted version of the reference signal in waveform LFM s(t) by a time value т with a fo is the center frequency. [20],[21]
Pulse compression helps identify targets in environments with high interference and enables the radar to distinguish between closely spaced targets. This technique is often implemented using specialized radar signals with frequency diversity, and digital processing techniques, including the use of algorithms for pulse compression. [21],[22],[23]
Pulse compression with frequency diversity is a key tool in many radar systems such as in the RL2000 radar, which structure of signal consists of four pulses Pfi-f4 with different frequency and two short (Ри, Pf2) and long (Pf3, Pf4) pulses is in Fig. 3. This radar is primary surveillance radar, which ensures enhanced system stability and robust clutter suppression, preventing false reports while maintaining exceptional target detection capabilities up to 150 kilometers. It includes improved performance for target accuracy and resolution. [21],[22],[23],[24]
4 ADAPTIVE ALGORITHMS FOR RADAR SIGNAL PROCESSING
One of the typical adaptive algorithms that significantly enhance radar signal processing capabilities in radars is adaptive beamforming. This technique uses a phased array antenna system to electronically steer the radar beam, allowing for improved target detection and tracking. Another innovation is the use of Space-Time Adaptive Processing which is used for filtration spatiotemporal data in time and frequency domain. This procedure ensures the mitigation of undesired interference and facilitates expedited processing of incoming signals. Lastly, the integration of ML algorithms into radar systems has facilitated the automatic detection and classification of targets, significantly reducing operator workload. [24], [25]
4.1 Adaptive beamforming
Adaptive beamforming is a crucial technique in modern radar systems, revolutionizing radar signal processing. This method allows real-time adjustment of the beam pattern, effectively mitigating unwanted noise and interference. By employing advanced algorithms, adaptive beamforming enhances target detection, tracking, and imaging capabilities, playing a pivotal role in improving overall radar system performance. [25],[26]
Through adaptive filtering, the algorithm dynamically adjusts the beam pattern by modifying the weights applied to individual antenna elements. The purpose of adaptive beamforming is to improve radar performance, specifically the signal-to-noise ratio, spatial resolution, and overall sensitivity, in the presence of interfering signals and noise. It involves adjusting the weights applied to individual antennas through adaptive filtering to optimize the received signal. The output signal z(t) for an adaptive array with TV antennas elements and x(t) as the received signal can be expressed as: [25],[26]
...
where the weight vector w is iteratively updated using the Recursive Least Squares (RLS) algorithm. Variable P(t) is the inverse of the autocorrelation matrix, о denotes forgetting factor, x(t)H is the complex conjugate of received signal and e(t) is the error signal. [25],[26]
Adaptive beamforming in radar systems offers several advantages over traditional methods. Firstly, it enhances the radar's capability to detect and track multiple targets concurrently. Adaptive beamforming enables the system to reject interference, improving its overall sensitivity and accuracy. Moreover, this technique enhances the spatial resolution of the radar, allowing for better target discrimination. Lastly, adaptive beamforming facilitates the reduction of clutter and noise, resulting in improved detection performance in complex environments. [25],[26],[27]
In real-world scenarios, adaptive beamforming finds valuable use in radar systems for both military and civilian purposes. Its effectiveness in tracking moving targets, mitigating multi-path propagation and interference, and improving radar imaging capabilities highlights its significance in modern radar technologies. Technological advancements, including powerful computational tools and digital bcamforming techniques, have further improved radar capabilities. Radars can now process complex waveforms and extract valuable information from cluttered environments. Digital beamforming enhances the radar's ability to steer and shape its beam pattern, significantly enhancing target detection and tracking capabilities. [26],[27]
The adaptive beamforming process (flowchart is shown in Fig. 4, where received signals are processed through the correct initialization of algorithm parameters. Computations of the covariance matrix and weight coefficients are applied to update the beam pattern, allowing adaptation to changing conditions in the environment. The convergence process monitors whether the adaptive system has reached a stable state. This convergence process ensures stability, resulting in a final beam pattern used for effective target detection and localization in received signals. Detecting a target in a mixture of signal and noise can be a challenging process, and if it is not optimal, the process is repeated from the step calculation of covariance matrix and weight coefficients. [26], [27]
4.2 Space-Time Adaptive Processing
STAP is the optimized 2D filtration technique in modern radar systems that removes interference from received signal by radar. This algorithm involves using precision filtration of angle and Doppler frequency in spatial and time domain, which causes completely elimination noise and interference from received signal. [28],[29]
This method leverages knowledge about the spatial characteristics of interference and target signal to compute weight vectors, thereby minimizing interference and enhancing target detection. Its mathematical foundation is based on matrix algebra and estimation theory. [29]
The advancement of the STAP algorithms is closely intertwined with its diverse applications. Successful implementation necessitates an in-depth analysis of several aspects, including noise characteristics, performance and accuracy, computational capacity requirements, and other factors like interference immunity, calibration requirements, and dynamic range. [29],[30]
Each STAP algorithm is based on a flowchart applied to the specific radar system, external environment, and signal type in use. [31]
This flowchart is on Fig. 5 and illustrating the principles of individual steps, this demonstrates the correct configuration of the STAP algorithm. Its application allows for accurate target detection in received signals containing not only target information but also clutter and interference. [31]
The STAP typically operates on a matrix X(n,m), encompassing input signals received by the radar, where n represents pulse repetition frequency, and m represents number of antennas elements. The correlation operation is applied to this matrix, generating covariance matrix R(n,my which is interpreted as: [31]
...
where J is the total number of input signals and X(n,m)H is the complex conjugate of the matrix X(n,m). [31]
Alternatively, correlation can be performed between input signals and noise, yielding the covariance matrix V(n,m), which is interpreted as: [31]
...
where M represents the number of antenna positions, S(n,m) represents the noise and S(n,m)H is the complex conjugate transpose of S(n,m). Output signal after filtration and application of weight vectors X(n) is characterized as: [31]
...
where (m)H denotes the complex-conjugate transpose of the weight vector, which is compute from covariance matrix R(n,m) and V(n,m) and X(n,m). [30],[31],[32]
This formula represents the basic STAP method, from which have involved more contemporary, potent, and precise STAP methods. Their accurate implementation relies on comprehensive knowledge of application parameters, investigated signals, and the environment. [30],[31],[32]
4.3 Machine Learning and Artificial Intelligence in radar signal processing algorithms
ML and AI are emerging as valuable tools in radar signal processing. These techniques enable the extraction of meaningful information from effect radar signals. By leveraging advanced algorithms, ML algorithms can effectively detect and classify targets, track their movements, and even predict future behaviors. The application of AI in radar signal processing allows for more efficient and accurate detection and identification of targets, enhancing radar performance in various environments and scenarios. These advancements in ML and AI contribute to the continual improvement of radar systems and their capability to support critical tasks such as surveillance, object recognition, and threat detection. [30],[34]
The process of ML in radar systems is derived from the flowchart in Fig. 6, where received signals are processed based on the correct initial initialization of algorithm parameters. Data is collected from radar sensors, encompassing signals from targets, noise, and other influences. Following this, data undergo preprocessing, noise removal, and identification of relevant features for learning. The data is then divided into training and testing sets to verify the model. The selection of a suitable ML model, such as neural networks or decision trees, is followed by training the model on the training set. The model is subsequently validated on the testing set, and its parameters are optimized to improve performance. After successful validation, the model is implemented in real-time into the radar system for continuous monitoring and target detection. If needed, adaptation mechanisms are introduced for dynamic environments and changing radar conditions. The overall performance of the model is regularly monitored, and if it is not optimal, the process returns to the model selection stage. [33],[34]
ML and AI have brought about a paradigm shift in radar systems, significantly enhancing their capabilities. These technologies play a pivotal role in enabling radars to autonomously learn and adapt in real-time, thereby enhancing the precision of target detection, tracking, and classification. By leveraging ML algorithms, radar systems can discern patterns in return signals, facilitating predictive analysis and informed decision-making.
AI algorithms contribute to the development of adaptive radar systems that possess the ability to self-optimize their parameters to effectively operate in diverse and changing conditions. This adaptability ensures optimal performance across various scenarios. In essence, the synergy between ML and AI has become indispensable in modern radar systems, equipping them to tackle the evolving challenges presented by complex and dynamic environments. [33],[34],[35]
These algorithms have greatly impacted radar signal processing. These algorithms can analyze and processing vast amounts of data to identify patterns and make predictions. They can effectively filter out noise and interference, improving signal quality. ML techniques also enable the detection and classification of targets, enhancing radar performance. Moreover, these algorithms can adapt to changing environments and learn from past experiences, making them highly efficient in real-time signal processing applications in radar systems.
For a nuanced ML model applied in radar, consider a simple logistic regression model for binary classification: [33],[34]
...
where Y(Q=1\A) denotes probability of class 1 given the input features A, ßo, ßi, ..., ßn, are the model parameters and A¡, A2, ..., An are the input features.
For a more sophisticated ML model, let's consider a deep neural network (DNN) with multiple hidden layers. The output y can be expressed as: [33], [34]
...
where T is the input feature vector, Wi, W2 are weight matrices, bi, b2 are bias vectors and ç is the activation function, such as the sigmoid or rectification linear unit (ReLU). [33],[34],[35]
These expressions provide intricate formulations for the corresponding radar signal processing techniques. It is essential to recognize that practical implementations may necessitate additional consideration and variations tailored to the specific requirements of the radar system. [34],[35],[36]
Another example of AI application in radar signal processing is target recognition. AI algorithms can be used to analyze radar signals and identify different types of targets, such as aircraft, ships, or automobiles. This is particularly useful in military applications, where quickly and accurately identifying targets is crucial for making strategic decisions. AI can also help in the classification of radar signals, enabling the detection of specific objects or anomalies in the environment. Overall, the use of AI in radar signal processing enhances the capabilities and effectiveness of radar systems in various domains. As we move further into the 21st century, the field of radar signal processing has experienced significant advancements, largely due to modern technologies. The integration of advanced algorithms and AI has allowed for improved target detection, clutter suppression, and overall system performance. Additionally, the utilization of highspeed digital signal processors and parallel computing techniques has enabled the processing of massive amounts of data in real-time, enhancing the radar's ability to track multiple targets simultaneously. [34],[35],[36]
5 CHALLENGES AND FUTURE PROSPECTS
In the realm of radar systems, we encounter several challenges that need to be successfully addressed in the coming years. One of the primary issues is the complexity of contemporary radar systems, requiring advanced algorithms and cutting-edge hardware architecture. The escalating demand for real-time processing poses challenges in terms of computational power and energy consumption. [37], [38]
The integration of multiple radar systems introduces challenges related to synchronization and interference, necessitating precise solutions to achieve effective coordination between systems. Despite these challenges, future prospects emerge. The application of AI methods, specifically deep learning, has the potential to significantly enhance the performance of radar systems, particularly in the areas of detection, tracking, and target classification. [37],[38]
New technologies, such as cognitive radars and Multiple Input Multiple Output (MIMO) radars show promising potential for enhancing the capabilities of radar systems. The development in the field of these advanced radar systems could bring new dimensions to the analysis and radar data processing. Despite technical challenges, opportunities are emerging to advance radar systems to a new level of performance and efficiency. [37],[38],[39]
Cognitive radars, leveraging their adaptability and intelligent decision-making capabilities, enable more efficient utilization of available resources and enhance target detection accuracy. [39],[40]
MIMO radars introduce an additional layer of complexity with multiple antennas at both the input and output, allowing for more precise localization and tracking of targets. These technologies provide us with tools to effectively address challenges associated with interferences and various environmental conditions. [41],[42]
The development in these systems is crucial for optimizing real-time signal processing. Utilizing advanced algorithms, adaptive approaches, and parallel processing allows for more efficient gathering and interpretation of radar data. What's even more significant, these technologies enable substantial improvements in performance, accuracy, and processing speed, creating new possibilities for the effective utilization of radar systems in various scenarios and conditions. [38],[39],[40],[41],[42]
6 CONCLUSION
The evolution of radar technology, coupled with advancements in radar signal and data processing, has significantly transformed the capabilities of radar systems. The integration of digital technologies, such as FPGAs and adaptive algorithms, has played a pivotal role in enhancing radar performance. The transition from early radar systems, with their limitations in interference susceptibility and range resolution, to modern solid-state radar systems highlights the strides made in achieving higher reliability, flexibility, and precision.
The utilization of adaptive radar signal processing, including techniques like pulse compression, adaptive beamforming, STAP, and the integration of ML and AI, has empowered radar systems to operate effectively in complex and dynamic environments. These technologies contribute to improved target detection, tracking, and classification, ensuring enhanced adaptability and intelligence.
The comparison between old and modern radar systems underscores the benefits of advancements in antenna technology, transmitter, and receiver components, as well as radar signal processing techniques. The adoption of solid-state components phased array antennas, and software-defined receivers has significantly improved radar coherence, stability, and overall performance.
Challenges in the field of radar systems, such as computational power requirements, synchronization issues, and the complexity of contemporary systems, are acknowledged. However, the future holds promising prospects with the potential integration of AI methods, cognitive radars, and MIMO radars. These technologies aim to address current challenges and further optimize radar systems for real-time signal processing, adaptability, and efficiency.
In summary, the continuous development of radar technology and signal processing techniques has paved the way for more sophisticated, adaptable, and efficient radar systems. As we navigate into the future, the integration of cutting-edge technologies and innovative approaches holds the key to overcoming current challenges and unlocking new dimensions in radar system capabilities.
Acknowledgements
The paper has been supported by the outputs of the research project "NI4200561 - Radar Signals Generation and Processing in Cognitive Radars" funded by the Ministry of Defense of the Slovak Republic through the inter-ministerial sub-program 06E0I-Research and development in support of state defense.
References
[1] SKOLNIK, M. I. Fifty Years of Radar [online]. Proceedings of the IEEE, 1985. [cit. 2024-2-25]. Available at: https ://ieeexplore-1 ieee-1 org1 1 erproxy. evtisr. sk/ stamp/stamp J s p?tp=&arnumber= 1457400
[2] MOELLER, T. J. and MARTINEZ, D. R. Field programmable gate array-based radar frontend digital signal processing [online]. Seventh Annual IEEE Symposium on FieldProgrammable Custom Computing Machines, 1999. [cit. 2024-2-25]. Available at: https://ieeexplore-1 ieee-1 org1 1 erproxy. c vtisr. sk/ stamp/stamp J s p?tp=&arnumber=803679
[3] CHEN, S., XU, W., XU, D., WANG, H. and CAO, J. Research and Implementation of Radar Signal Processing Algorithm for FPGA-Based DBF System [online]. IEEE MTT-S International Micro wave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP), 2023. [cit. 2024-2-25]. Available at: https ://ieeexplore-1 ieee-1 org1 1 erproxy. c vtisr. sk/ stamp/stamp J s p?tp=&arnumbcr= 10381354
[4] ZHANG, Q., FENG, S., FU, L., WEN, L, GE, J., and MA, J. Design and Implementation of Adaptive Digital Beamforming Based on FPGA [online]. 4th China International SAR Symposium (CISS), 2023. [cit. 2024-2-25]. Available at: https ://ieeexplore-1 ieee-1 org1 1 erproxy. c vtisr. sk/ stamp/stamp J s p?tp=&arnumbcr= 1038003 6
[5] CRASTO, N., KUMAR, T. K., BARUA, P. and NEMANI, S. FPGA implementation of back projection algorithm for radar imaging [online]. 2013 International Conference on Radar, 2013. [cit. 2024-2-26]. Available at: https://doi.org/10.1109/RADAR.2013.6651967
[6] SHEHATA, M. G., AHMED, F. M., SALEM, S. and ZAKARIA, H. Design and Implementation of LFMCW Radar Signal Processor for Slowly Moving Target Detection Using FPGA, [online]. 12th International Conference on Electrical Engineering, 2020. [cit. 2024-226]. Available at: https ://ieeexplore-1 ieee-1 org1 1 p003 5. erproxy. evtisr. sk/stamp/stamp J s p?tp=&arnumbcr=9171701
[7] SUMMERS, R., YEARY, M., SIGMARSSON, H. and RINCON, R. Architecture Study for a Bare-Metal Direct Conversion Radar FPGA Testbench [online]. 2021 IEEE Radar Conference, 2021. [cit. 2024-2-26]. Available at: https ://ieeexplore-1 ieee-1 org1 1 p003 5. erproxy. evtisr. sk/stamp/stamp J s p?tp=&arnumber=9455320
[8] ALAEE-KERAHROODI, M., BABU, P., SOLAN ALI AN, M. and SHANKAR, M. R. B. Signal Design for Modern Radar Systems. Artech House, 2022. 392 s. ISBN 978-1-63081892-0.
[9] PECCARELLI, N., JAMES, B., FULTON, C. and GOODMAN, N. Dynamic Range Considerations for Modern Digital Array Radars [online]. 2020 IEEE International Radar Conference, 2020. [cit. 2024-2-26]. Available at: https://doi.org/10.1109/RADAR42522.2020.91 14607
[10] OFENHÄUSER, F. Analog-Digital Converters for Industrial Applications Including an Introduction to Digital-Analog Converters. Springer, 2015. 333 s. ISBN 978-3662-47019-0.
[11]LACOMME, P., HARD ANGE, J. P., MARCHAIS, J. C. and NORMANT, E. Air and Spaceborne Radar Systems. SciTech, 2001. 526 s. ISBN 978-0-815-51613-2.
[12] ROHAĽ, P. and OCHODNICKÝ, J. Target tracking by adaptive filtering [online]. Science and Military, 2019. [cit. 2024-23-2]. Available at: http://sm.aos.sk/images/archive/SM l2019.pdf
[13] WIDROW, B., MANTEY, P. E., GRIFFITHS, L. J. and GOODE, B. B. Adaptive antenna systems [online]. Proceedings of the IEEE, 1967. [cit. 2023-12-10]. Available at: https://ieeexplore-1 ieee-1 org-1 dikuiiqqO 1 c2
[14] HUSSEIN, K., AMAR, A. S. L, ZERKY, A., ABOULATTA, M., MAHMOUD, A. K. and MABROUK, M. Design and Implementation of SDR Platform for Radar Applications [online]. 2023 International Telecommunications Conference, 2023. Available at: [cit. 2023-12-10]. https://doi.org/10.1109/ITC-Egypt58155. 2023.10206266
[15] SGAMBATO, P., BOTTA, N., CELENT ANO, S., DIO, C., MARZANO, G., MUSIO, О. and PETRILLO, C. System manager for AESA radar systems [online]. 2015 IEEE Radar Conference, 2015. [cit. 2023-12-10]. Available at: https ://ieeexplore-1 ieee-1 org1 1 1 n00d7. erproxy. c vtisr. sk/stamp/stamp .j s p?tp=&arnumber=7131279
[16] ZHU, C., HU, W., ZHU, H. and LI, L. Research on Radar Development and Application and Signal Processing Technology [online]. 2021 IEEE International Conference on Electronic Technology, Communication, and Information, 2021. [cit. 2023-12-10]. Available at: https://ieeexplore-1 ieee-1 org1 1 1 n00d7. erproxy. c vtisr. sk/stamp/stamp .j s p?tp=&arnumber=9563524
[17] WANG, В. C. Digital Signal Processing Techniques and Applications in Radar Image Processing. Wiley, 2008. 368 s. ISBN 978-0470-37782-6.
[18] BHAKTAVATSALA, S. DSP Application in Radar [online]. M. Tech Credit Seminar Report, 2002. [cit. 2023-12-11]. Available at: http s : //www.ee. iitb. ac. in/~es group/es mtech02 sem/es02 sem rep bhakta.pdf
[19] ZHANG, M. K. and ZHANG, Y. A signal processing practice method based on LFMCW radar [online]. International Conference on Computer Science and Network Technology, 2017. [cit. 2023-12-11]. Available at: https://ieeexplore-1 ieee-1 org1 1 1 nO 103. erproxy. c vtisr. sk/ stamp/stamp .j s p?tp=&arnumber=8343737
[20] PAINCHAUD, G. R., McKENZIE, J. A. H., BLANCHETTE, M. and VOY, A. An experimental adaptive digital pulse compression subsystem for multi-function radar applications [online]. IEEE International Conference on Radar, 1990. [cit. 2023-12-11]. Available at: https://ieeexplore-1 ieee-1 orgIdikuiiln00b3.erproxy.cvtisr.sk/document/201 154/metrics
[21] LEIFER, M. C. and HAUPT, R. L. Power amplifier and power supply distortion of pulse compression radar chirps [online]. 2016 IEEE Radar Conference, 2016. [cit. 2023-12-11]. Available at: https://doi.org/10.1109/RADAR.2016.7485074
[22] WANG, S., LI, Z., ZHANG, Y., CHEONG, B., and LI, L. Implementation of Adaptive Pulse Compression in Solid-State Radars: Practical Considerations [online]. IEEE Geoscience and Remote Sensing Letters, 2015. [cit. 2023-1211]. Available at: https ://ieeexplore-1 ieee-1 org1 1 ln00d7. erproxy. cvtisr.sk/document/717 2487
[23] WOLF, C. RL-2000/GCI[online]. Radartutorial, [cit. 2024-24-2]. Available at https://www.radartutorial.eu/19.kartei/03 .atc/ka rteO 18 .en.html#this
[24] MOO, P. W. and DING, Z. Adaptive Radar Resource Management. Academic Press, 2015. 158 s. ISBN 978-0-128-04210-6.
[25] VOURAS, P. and FREBURGER, B. Application of adaptive beamforming techniques to HF radar [online]. IEEE Radar Conference, 2008. [cit. 2023-12-12]. Available at: https://ieeexplore-1 ieee-1 org1 1 1nO1a5. erproxy. c vtisr. sk/stamp/stamp J s p?tp=&arnumber=4720868
[26] ZHANG, H., LUO, L, CHEN, X., LIU, Q. and ZENG, T. Fresnel based frequency domain adaptive beamforming for large aperture distributed array radar [online]. IEEE International Conference on Signal Processing, Communications and Computing, 2016. [cit. 2023-12-12]. Available at: https://ieeexplore1 1 org-1 dikuii 1nO1a5. erproxy. cvtisr. sk/ stamp/stamp. j sp ? tp=&arnumb er=7753658
[27] SHARMA, S. and SUJATHA, S. SMI algorithm - Adaptive beamforming for radar systems [online]. International Conference on Wireless Communications, Signal Processing and Networking, 2017. [cit. 2023-12-12]. Available at: https ://ieeexplore-1 ieee-1 org1 1 1nO1a5. erproxy. cvtisr. sk/stamp/stamp J s p?tp=&arnumber=8300125
[28] BÜRGER, W. Space-Time Adaptive Processing: Algorithms [online]. NATO RTO Educational notes EN-SET-086 Advanced Radar Signal and Data Processing, 2006. [cit. 2023-12-15]. Available at:
[29] GUERCI, J. R. Space-Time Adaptive Processing for Radar. Artech House, 2015. 292 s. ISBN 978-1-608-07820-2.
[30] MELVIN, W. L. A STAP Overview [online]. IEEE Aerospace and Electronic Systems Magazine, 2004. [cit. 2023-12-15]. Available at: https://ieeexplore-1 ieee-1 org1 1 1пООЬЗ. erproxy. cvtisr. sk/stamp/stamp .j s p?tp=&arnumber= 1263229
[31] ĽONCOVÁ, J. and OCHODNICKÝ, J. A Modified STAP Algorithm for Ground-Based Radar [online]. 2023 Communication and Information Technologies (KIT), 2023. [cit. 2024-2-26]. Available at: https://doi.org/10.1109/KIT59097.2023.102970 20
[32] KLEMM, R. Introduction to space-time adaptive processing [online]. IEE Colloquium on Space-Time Adaptive Processing, 1998. [cit. 2023-12-12]. Available at: https://ieeexplore1 1 org-1 dikuii 1 пООЬЗ .erproxy.cvtisr.sk/ stamp/stamp.] sp?tp=&arnumber=675648
[33] MARS, P. Learning Algorithms. Theory and Applications in Signal Processing, Control and Communications. CRC Press, 2017. 240 s. ISBN 978-1-315-89487-4.
[34] MAJUMDER, U. K., BLASCH, E. P. and GARREN, D. A. Deep Learning for Radar and Communications Automatic Target Recognition. Artech House, 2020. 290 s. ISBN 978-1-630-81637-7.
[35] KIM, Y. Application of Machine Learning to Antenna Design and Radar Signal Processing [online]. International Symposium on Antennas and Propagation, 2018. [cit. 2023-12-17]. Available at: https ://ieeexplore-1 ieee-1 org1 1 1nO1a5. erproxy. cvtisr. sk/stamp/stamp J s p?tp=&arnumber=8627834
[36] MANNA, M. L., MONSURRÔ, P. and TRIFILETTI, A. Machine learning techniques for frequency sharing in a cognitive radar [online]. IEEE Radar Conference, 2018. [cit. 2023-12-17]. Available at: https://ieeexplore1 1 org-1 dikuii 1 nO 1 a5. erproxy. cvtisr. sk/ stamp/stamp.] sp?tp=&arnumber=83 78650
[37] BROOKER, G. M. Understanding Millimeter Wave FMCWRadars [online]. 1st International Conference on Sensing Technology, 2005. [cit. 2023-12-18]. Available at: http s : //studylib .net/doc/18695679/under standin g-millimetre-wave-fmcw-radars
[38] JANOUDI, V., SCHOEDER, P., GREBNER, T., SCHWARZ, WALDSCHMIDT, D. C., DICKMAN, J. and APPENRODT, N. Antenna Array Design for Coherent MIMO Radar Networks [online]. IEEE Radar Conference, 2023. [cit. 2023-12-20]. Available at: https://ieeexplore-1 ieee-1 org1 1 1nO1a5. erproxy. cvtisr. sk/stamp/stamp .j s p?tp=&arnumber=l 0149789
[39] GUERCI, J. R. Cognitive radar: The Knowledge-Aided Fully Adaptive Approach, Second Edition. Artech House, 2020. 200 s. ISBN 978-1-630-81773-2.
[40] ZHU, H., ZHU, Z., SU and ZHANG, F. J. New Algorithms in Cognitive Radar, [online]. 11th International Congress on Image and Signal Processing, BioMedical Engineering, and Informatics, 2018. [cit. 2024-28-2]. Available at: https://ieeexplore-1 ieee-1 org1 1 ce. erproxy. cvtisr. sk/stamp/stamp .j s p?tp=&arnumber=8633239
[41] LI, J. and STOICA, P. MIMO Radar Signal Processing. Wiley-IEEE Press, 2009. 594 s. ISBN 978-0-470-17898-0.
[42] BERGIN, J. and GUERCI, J. R. MIMO Radar. Theory and Application. Artech House, 2018. 229 s. ISBN 978-1-630-81522-6.
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