Radar Interference Mitigation: A Review

A. Classical Approaches

1)

Hardware Approaches: Hardware approaches involve designing specialized transceivers [33,34] and other hardware components to mitigate interference. These approaches focus on optimizing the hardware design to enhance radar performance and reduce susceptibility to interference.

2)

Filtering Techniques: Filtering is a foundational technique, leveraging frequency or spatial filters to suppress interference [35]. For example, bandpass filters target specific frequency ranges to isolate desired signals in FMCW radars, while spatial filtering uses beamforming [22,28] to nullify interference from known directions. A notable application is phased array radar systems using spatial filters to track aircraft while ignoring ground clutter.

3)

Time-Domain Gating: This approach mitigates interference by identifying and removing corrupted time segments. For example, pulsed radars can discard segments of the signal overlapping with interference. While simple and effective, time-domain gating is less effective in scenarios with continuous interference, such as urban vehicular environments.

4)

Interpolation Algorithms: Interpolation algorithms are used to reconstruct missing or corrupted segments of the time-domain signal. These algorithms estimate the missing data points based on the surrounding data, providing a continuous and smooth signal. Common interpolation methods include linear interpolation, spline interpolation, and polynomial interpolation. For example, spline interpolation uses piecewise polynomials to estimate the missing data points, providing a smooth and continuous signal reconstruction. These algorithms are particularly useful in scenarios where the interference is intermittent and can be isolated in the time domain. In [30], the disrupted signal segments are interpolated through auto-regressive model based method, where the detected disrupted samples are interpolated by the iterative method with an adaptive thresholding algorithm (IMAT) in [49].

B. Advanced Approaches

1)

Adaptive Signal Processing: Adaptive techniques, such as Wiener filtering and least mean squares (LMS) algorithms, dynamically adjust to changing interference patterns, enhancing robustness. For example, Wiener filtering can optimize the signal-to-noise ratio in dynamic scenarios like naval radar systems facing multipath interference.

2)

Compressed Sensing: Compressed sensing is a signal processing technique that reconstructs a signal from a small number of samples, exploiting the sparsity of the signal in some domain [11,12]. This approach is particularly useful for radar systems, where the signal is often sparse in the time or frequency domain. Compressed sensing algorithms, such as convex optimization, greedy algorithms, and Bayesian methods, can reconstruct the signal with high accuracy from a limited number of samples. For example, convex optimization algorithms, such as Basis Pursuit, solve an optimization problem to find the sparsest solution that fits the observed data. Greedy algorithms, such as Orthogonal Matching Pursuit (OMP), iteratively select the most significant components of the signal to reconstruct it. Bayesian methods, such as Bayesian Compressive Sensing (BCS) [13], use probabilistic models to estimate the signal and its sparsity pattern. These algorithms have been successfully applied to radar signal reconstruction, providing accurate and efficient interference mitigation.

3)

Interference Mitigation with Communication Techniques: Interference mitigation can also be achieved through communication techniques, such as RadarMAC [16], RadCom [14], and RadChat [15]. These techniques leverage the communication capabilities of radar systems to coordinate and mitigate interference [40,41]. RadCom, or Radar Communication, integrates radar and communication functionalities to reduce mutual interference. This approach uses frequency division multiplexing to separate radar and communication signals, allowing them to coexist without interference. RadCom systems can dynamically adjust the timing and frequency of radar transmissions based on communication signals, reducing the likelihood of interference. RadChat is a distributed networking protocol designed to mitigate interference among FMCW-based automotive radars. It uses radar and communication cooperation to coordinate radar transmissions and reduce mutual interference. RadChat can significantly reduce radar mutual interference in single-hop vehicular networks, improving the performance and reliability of automotive radar systems.

4)

Machine Learning Techniques: Machine learning (ML) techniques are increasingly being employed for radar interference mitigation due to their ability to learn from data and adapt to new interference patterns [23,27]. Supervised learning models, such as support vector machines (SVMs), are used to classify interference types for mitigation. These models are trained on labeled datasets, where different types of interference are annotated, allowing the model to learn the characteristics of each interference type. Unsupervised methods, such as clustering, are used to detect anomalies in radar signals. These methods do not require labeled data and can identify patterns and clusters in the data that correspond to different interference types. For instance, supervised learning models like support vector machines (SVMs) classify interference types for mitigation [27], while unsupervised methods such as clustering detect anomalies in radar signals.

A. Types of Datasets

• Synthetic Datasets: These are generated using simulation tools and models to create controlled interference scenarios. Synthetic datasets are valuable for initial testing and development of algorithms, as they allow for precise control over the parameters and conditions. For example, synthetic datasets can simulate various types of interference, such as FMCW radar interference, to evaluate the performance of different mitigation techniques.
• Real-World Datasets: These are collected from actual radar systems operating in real environments. Real-world datasets provide a more accurate representation of the challenges faced by radar systems, including unpredictable interference patterns and environmental factors. For instance, datasets collected from automotive radars in urban traffic conditions can capture the complexity of mutual interference in dense vehicular networks.
• Hybrid Datasets: These combine synthetic and real-world data to leverage the advantages of both. Hybrid datasets can provide a comprehensive evaluation framework by incorporating controlled scenarios from synthetic data and realistic conditions from real-world data. This approach helps in developing robust algorithms that perform well in diverse environments.

B. Key Datasets in Radar Interference Mitigation

• Automotive Radar Datasets: These datasets capture interference scenarios in automotive applications, such as mutual interference between radars in multi-lane traffic and cross-interference from nearby vehicles. Examples include the COSMOS Radar RobotCar Dataset [7], the ARIM Dataset [63], Raw ADC data [8,62], which provide extensive data for developing and testing interference mitigation techniques in autonomous driving.
• Weather Radar Datasets: These datasets focus on interference scenarios in weather radar systems, such as clutter from wind turbines and multi-path propagation. The NEXRAD Dataset [9] is a valuable resource for researchers working on weather radar interference mitigation, offering a large collection of radar data from the National Weather Service.
• Maritime Radar Datasets: These datasets capture interference scenarios in maritime environments, such as reflections from waves and interference from other vessels. The C-CORE Radar Dataset [10] provides data for developing and testing interference mitigation techniques in maritime navigation and surveillance.

C. Challenges in Dataset Collection

• Data Diversity: Ensuring that datasets capture a wide range of interference scenarios is essential for developing robust algorithms. This includes variations in radar types, operating conditions, and environmental factors. For example, automotive radar datasets should include data from different traffic conditions, weather conditions, and vehicle types to provide a comprehensive evaluation framework.
• Data Quality: High-quality data is crucial for accurate training and evaluation of algorithms. This includes ensuring that the data is free from noise and artifacts that could impact the performance of interference mitigation techniques. For instance, weather radar datasets should be carefully processed to remove any spurious signals or artifacts that could affect
• Data Quality: High-quality data is crucial for accurate training and evaluation of algorithms. This includes ensuring that the data is free from noise and artifacts that could impact the performance of interference mitigation techniques. For instance, weather radar datasets should be carefully processed to remove any spurious signals or artifacts that could affect the accuracy of the data.
• Data Annotation: Annotating datasets with accurate labels and metadata is essential for supervised learning algorithms. This includes labeling different types of interference, such as mutual interference, clutter, and multi-path propagation. For example, automotive radar datasets should include annotations for different types of
• interference, such as interference from nearby vehicles, reflections from road surfaces, and clutter from roadside objects.

D. Future Directions in Dataset Development

• Standardization: Developing standardized datasets for benchmarking and evaluation is crucial for advancing the field of radar interference mitigation. Standardized datasets provide a common framework for comparing the performance of different algorithms and techniques. This includes defining standard metrics for evaluating interference suppression efficiency, computational complexity, and implementation feasibility.
• Open Access: Making datasets publicly available to the research community can accelerate the development of new interference mitigation techniques. Open access datasets provide researchers with the necessary data to develop and test their algorithms, fostering collaboration and innovation. For example, initiatives like the IEEE Dataport provide a platform for sharing and accessing radar datasets.
• Collaborative Efforts: Collaboration between academia, industry, and government agencies can enhance the quality and diversity of radar datasets. Collaborative efforts can leverage the expertise and resources of different stakeholders to collect and curate comprehensive datasets. For instance, partnerships between automotive manufacturers, research institutions, and regulatory agencies can lead to the development of high-quality automotive radar datasets.

A. Performance Analysis

B. Challenges

1)

Real-Time Implementation: Advanced techniques often struggle with real-time constraints. For instance, deploying a deep learning model on an automotive radar may exceed processing time requirements in high-speed scenarios. Real-time implementation is critical for applications such as collision avoidance in autonomous vehicles, where timely and accurate detection of obstacles is essential for safety.

2)

Scalability: Integrating ML-based methods into large-scale systems, such as nationwide air traffic management, demands extensive data and model generalization to diverse conditions. Scalability is a significant challenge, as it requires the development of models that can handle a wide range of interference scenarios and adapt to different environments.

3)

Hardware Limitations: Deploying complex algorithms in resource-constrained environments, such as compact drones, requires optimizing for power and memory efficiency. Hardware limitations can impact the feasibility of implementing advanced interference mitigation techniques, particularly in applications where size, weight, and power constraints are critical.

C. Research Gaps

1)

Improved algorithms for real-time operation, such as lightweight neural networks. Developing efficient algorithms that can operate in real-time is essential for applications where timely response is critical. Lightweight neural networks, for example, can provide a balance between performance and computational efficiency, making them suitable for real-time interference mitigation.

2)

Hybrid approaches combining classical and advanced techniques, for instance, using adaptive filtering alongside machine learning-based anomaly detection. Hybrid approaches can leverage the strengths of both classical and advanced methods, providing robust and efficient interference mitigation. For example, adaptive filtering can be used to dynamically adjust to changing interference patterns, while machine learning-based anomaly detection can identify and mitigate complex interference scenarios.

3)

Standardized datasets for benchmarking, such as publicly available radar datasets capturing diverse interference scenarios. The availability of standardized datasets is crucial for benchmarking and evaluating the performance of different interference mitigation techniques. These datasets should capture a wide range of interference scenarios, including mutual interference between automotive radars, clutter from wind turbines, and multi-path propagation in urban areas.

4)

Improved data collection methods to enhance the quality and diversity of radar datasets. This includes developing advanced sensor technologies and data acquisition systems that can capture high-resolution radar data in various environments. For example, automotive radar datasets should include data from different traffic conditions, weather conditions, and vehicle types to provide a comprehensive evaluation framework.

5)

Enhanced data annotation techniques to ensure accurate labeling of interference types. This includes developing automated annotation tools that can accurately label different types of interference, such as mutual interference, clutter, and multi-path propagation. For example, automotive radar datasets should include annotations for different types of interference, such as interference from nearby vehicles, reflections from road surfaces, and clutter from roadside objects.