Due to the small size of foreign object debris (FOD) and varied complex weather conditions, the detection of FOD on airport runways is a great challenge. Radar is an important method for detecting FOD targets. However, almost all the existing systems are based on real apertures, which have disadvantages such as low azimuth resolution and susceptibility to rain interference. Here, an innovative FOD detection radar system based on arc-scanning synthetic aperture radar (AS-SAR) technology, the AS-SAR based FOD detection system (AS-FODR), achieves omnidirectional coverage with a very high azimuth resolution and the suppression of flicker clutter, such as rain drops in severe weather. According to the radar imaging simulation of a scene under rainy conditions and the information processing analysis of field experiments, a prototype system was built, and an efficient data process flow was proposed. In short, a one centimeter FOD target was detected on the runway more than 250 m away, proving that the use of AS-FODR is feasible and effective.

Arc-scanning synthetic aperture radar (AS-SAR) is an emerging technical means for detecting foreign object debris (FOD). Most FOD are small and appear as weak targets with a low signal-to-noise ratio (SNR) in AS-SAR images. Therefore, image noise is a fundamental challenge in detecting FOD on airport runways that leads to many false alarms. A weak scattering denoising method is proposed to aim at the noise caused by speckle and rough surface scattering. To enhance FOD detection, a transformation parameter concept is offered and adopted, which has different characteristics for the target and background. This paper estimates the transformation parameter through logarithms, normalization, and morphological erosion and optimizes them with edge-preserving filtering. The results show that despeckling and runway scattering suppression can be simultaneously implemented, and that field experiments validate the performance of this method.

Due to the small size and various types of foreign object debris (FOD), radar detection of FOD on airport runways is a great challenge, and there are often a large number of false alarms in the detection results. Arc-scanning synthetic aperture radar (AS-SAR) is an emerging method for detecting FOD targets, which achieves omnidirectional coverage with a very high azimuth resolution. However, this method faces a similar challenge. A direct way to reduce false alarms is to increase the detection threshold based on enhancing the target signal-to-noise ratio (SNR), and in this paper, the coherent accumulation of multiple images is used to improve the target SNR. The stable phase is also an important feature of the target distinguishing background. Therefore, it is important to maintain the stability of the target phase. Aiming at the systematic phase drift (SPD) caused by atmospheric disturbance and system hardware, a spatial and temporal model is established, a corresponding correction approach is proposed, and the performance of the correction approach is validated by field experiments.

Interferometric radar is a hot research topic in manmade target displacement measuring applications, as it features high precision, a large operation range, and a remote multiple point measuring ability. Most one-dimensional interferometric radars use single-input single-output (SISO) radar architecture to achieve a high repetition measuring rate of more than 200 Hz; however, it cannot resolve multiple targets with the same radial range but different azimuth angles. This paper presents a multiple-input multiple-output (MIMO) radar that adopts a limited number of antennas (usually tens) to simultaneously improve azimuth resolution and achieve a high repetition measuring rate. A MUSICAPES algorithm is proposed, which is cascades the multiple signal classification (MUSIC) algorithm and the amplitude and phase estimation (APES) filter. The MUSIC algorithm is used to further improve the angular resolution of the small array. The APES is used to precisely recover the phases of the multiple close targets by suppressing their mutual interferences. Simulations and experiments with a millimeter-wave radar validate the performance of the proposed method.

Foreign Object Debris (FOD) detection is paramount for airport operations. The precise identification and removal of FOD are critical for ensuring airplane flight safety. This study collected FOD images using optical imaging sensors installed at Urumqi Airport and created a custom FOD dataset based on these images. To address the challenges of small targets and complex backgrounds in the dataset, this paper proposes optimizations and improvements based on the advanced detection network Dab-Deformable. First, this paper introduces a Lightweight Deep-Shallow Feature Fusion algorithm (LDSFF), which integrates a hotspot sensing network and a spatial mapping enhancer aimed at focusing the model on significant regions. Second, we devise a Multi-Directional Deformable Channel Attention (MDDCA) module for rational feature weight allocation. Furthermore, a feedback mechanism is incorporated into the encoder structure, enhancing the model’s capacity to capture complex dependencies within sequential data. Additionally, when combined with a Threshold Selection (TS) algorithm, the model effectively mitigates the distraction caused by the serialization of multi-layer feature maps in the Transformer architecture. Experimental results on the optical small FOD dataset show that the proposed network achieves a robust performance and improved accuracy in FOD detection.