Adaptive near Real-Time RFI Mitigation Using Karhunen–Loève Transform
Abstract
1. Introduction
1.1. Radio Frequency Interference
1.2. The Karhunen–Loève Transform
1.3. Using KLT Decomposition for RFI Mitigation
1.4. KLT Direct Excision
2. Materials and Methods
2.1. Experimental Set-Up Description
2.2. Mitigation Performance Evaluation
- A Delta function: An instantaneous signal where all the RFI power is concentrated in a single, randomly chosen temporal bin.
- A train of pulses with a 10% duty cycle: a train of rectangular pulses with a Pulse Repetition Time (PRT) of samples, a pulse width of samples, and a central frequency of .
- A train of pulses with a 50% duty cycle: a sequence of rectangular pulses sharing the same PRT and frequency, each with a pulse width of samples.
- A continuous wave (CW): A single tone signal (sinusoidal), simulating a narrowband modulation, of of frequency.
- An amplitude-modulated continuous wave (CW): A single-tone sinusoidal signal centered at frequency , representing narrowband modulation, and modulated by a slowly varying signal formed by the sum of two Gaussian envelopes to introduce non-stationarity.
- A narrowband chirp signal centred at : A linearly swept chirp with a bandwidth of and a pulse repetition time of samples. Such chirp patterns are typical of RADAR signals and jamming sources.
- A wideband chirp signal: A linearly swept chirp covering a bandwidth of , with a pulse repetition time of samples.
- The combination of a CW and a narrowband chirp signal, as defined above.
2.3. Real-Time Asynchronous Mitigator Description
3. Results
3.1. Mitigation Performance
- Temporal is ideal for delta functions and pulsed signals, and less for the modulated CW. It reaches good mitigations (<−25 dB) for moderate resolution loss (<8%), particularly in the case of low duty cycles. However, it does not work for narrowband signals.
- DFT offers very good performance for narrowband RFI, particularly for CW signals, achieving performances of <−30 dB with low resolution loss (<2%). However, it does not work for wideband signals.
- STFT is able to mitigate all types of waveform, but achieving lower performances for selected types than the above. For example, it is able to mitigate both delta functions and CW, but temporal and DFT offer a better performance for these types, respectively. In addition, the resulting resolution loss is quite high (reaching (>20%) for INR > 10.
- DWT is qualitatively similar. It offers better results for delta functions, but its performance is below STFT for the rest of the types.
3.2. Operation in Real Time
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
RFI | Radio Frequency Interference |
KLT | Karhunen–Loève Transform |
PCA | Principal Component Analysis |
SVD | Singular Value Decomposition |
SDR | Software Defined Radio |
COTS | Commercial Off-The-Shelf |
ADC | Analog-to-Digital Converter |
AGC | Automatic Gain Control |
I/Q | In-phase/Quadrature |
INR | Interference-to-Noise Ratio |
CW | Continuous Wave |
FMCW | Frequency-Modulated Continuous Wave |
RL | Resolution Loss |
MP | Mitigation Performance |
ITU | International Telecommunication Union |
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Díez-García, R.; Camps, A. Adaptive near Real-Time RFI Mitigation Using Karhunen–Loève Transform. Remote Sens. 2025, 17, 2578. https://doi.org/10.3390/rs17152578
Díez-García R, Camps A. Adaptive near Real-Time RFI Mitigation Using Karhunen–Loève Transform. Remote Sensing. 2025; 17(15):2578. https://doi.org/10.3390/rs17152578
Chicago/Turabian StyleDíez-García, Raúl, and Adriano Camps. 2025. "Adaptive near Real-Time RFI Mitigation Using Karhunen–Loève Transform" Remote Sensing 17, no. 15: 2578. https://doi.org/10.3390/rs17152578
APA StyleDíez-García, R., & Camps, A. (2025). Adaptive near Real-Time RFI Mitigation Using Karhunen–Loève Transform. Remote Sensing, 17(15), 2578. https://doi.org/10.3390/rs17152578