Search

Clutter is the radar noise signal that is reflected from the elements surrounding the targets. Since clutter degrades the radar system’s range and doppler frequency detection capability, clutter suppression is a critical signal-processing algorithm that can improve the performance of a radar system. This paper proposes a ground-clutter suppression method using denoising encoder-decoder deep learning network with dual encoding channels, residual connections, and skip connections. Radar signal dataset pipeline was generated using MATLAB in order to train the network. In this paper, deep learning-based clutter suppression method that can be applied in various operating conditions, is discussed. [ABSTRACT FROM AUTHOR]

The purpose of an interleaved mode is to simultaneously provide two or more modes to the user. For this, the time resources allocated to each mode are reduced, and thus, the performance of each mode is degraded. Therefore, a scheduling method is required that can minimize the performance degradation of each mode by appropriately allocating resources. In the interleaved mode that operates the ground imaging and air-to-air search/track modes, the image update interval is longer than that of the single mode operation, and air-to-air tracking performance can be degraded; therefore, scheduling must be planned considering them. In this paper, to always obtain images above a certain proportion of the current search area while minimizing the degradation of air-to-air tracking performance, a scheduling algorithm that adaptively adjusts the image update interval by utilizing the posture and position change of the radar-mounted aircraft is proposed. [ABSTRACT FROM AUTHOR]

In a radar system, a clustering algorithm is mainly used to group adjacent cells using CFAR algorithm results. However, in closely spaced airborne targets, it is difficult to divide the targets using common clustering methods. Therefore, this paper proposes a clustering method using a monopulse rather than increasing the range and Doppler resolution. Even with adjacent hits, if the monopulse value is more than a specified threshold, the results are not grouped into one cluster. [ABSTRACT FROM AUTHOR]

In radar operations, the detection performance is reduced owing to various external factors. These factors include interference and other targets, as well as losses from the atmosphere and terrain. Because of its geomorphic characteristics, radar operating in Korea is affected by interference, and its performance is degraded. This paper describes the simulation of the environment of near-radar interference and multiple targets. The modeling and simulation (M&S) tool was developed to predict and prevent the degradation in radar performance in advance. We validated existing constant false alarm rate (CFAR) detection algorithms by generating signals through the M&S tool and proposed a new CFAR algorithm to enhance the radar performance. Consequently, radar performance is enhanced by the new CFAR algorithm in radar interference and multiple target environments. [ABSTRACT FROM AUTHOR]

Recent advance in low cost and light-weight drones has extended their application areas in both military and private sectors. Accordingly surveillance program against unfriendly drones has become an important issue. Drone detection and classification technique has long been emphasized in order to prevent attacks or accidents by commercial drones in urban areas. Most commercial drones have small sizes and low reflection and hence typical sensors that use acoustic, infrared, or radar signals exhibit limited performances. Recently, artificial intelligence algorithm has been actively exploited to enhance radar image identification performance. In this paper, we adopt machined learning algorithm for high resolution radar imaging in drone detection and classification applications. For this purpose, simulation is carried out against commercial drone models and compared with experimental data obtained through high resolution radar field test. [ABSTRACT FROM AUTHOR]

In a multi-function radar, the adaptive beamforming algorithm is one of the several methods that can be applied to restore the degraded SINR(Signal-to-Interference Noise Ratio) to its original level. This algorithm is employed for the regular operation of radars in jamming situations, when unwanted high-power signals are received. In this paper, we introduce a few adaptive beamforming algorithms that can be applied to a multi-function digital radar in jamming situations. Furthermore, we compare and analyze the adaptive beamforming performance in terms of SINR and sidelobe level characteristics by applying each algorithm to a specific planar array comprising optimized subarray structures. [ABSTRACT FROM AUTHOR]