The modern active electronically scanned array (AESA) airborne radar features an interleaved mode, whereby situational awareness can be maximized via incorporation of the radar's electronic beam pointing capability. This interleaved mode can enable simultaneous operation of two or more modes, such as the air-to-air and sea surface-search modes, via the technique of time-sharing. One of the key requirements for successful realization of the interleaved mode is a beam scheduler design that facilitates effective control of the allocated resources for each mode. Thus, in this paper, we propose an effective beam scheduler that can control the allocated resources for each mode through the mode-switching option based on the radar's frames, bars, and beams. Furthermore, the proposed beam scheduler involves the mode-switching decision logic and calculates the processing ratio for each mode.
Abstract. In recent research, automatic target recognition (ATR) of infrared targets has been taking a lot of interest to the researchers. A rotation invariant method is useful in target recognition, classification and image analysis to reduce the number of training data. In particular, rotation invariant method, Radon transform, is an effective technique that is used for medical care such as computerized tomography (CT) image. This paper proposes a new rotation invariant algorithm for target recognition. The proposed method combines the gradient information and radon transform. The propose method, called gradient Radon (G-Radon), is applied to synthesized infrared images and compared with traditional radon transform and Zernike moments for validation.
Detecting remote targets is important to active protection system (APS) or infrared search and track (IRST) applications. In normal situation, the well-known constant false alarm rate (CFAR) detector works properly. However, decoys in APS or closely spaced targets in IRST degrade the detection capability by increasing background noise level in the CFAR detector. This paper presents a context aware CFAR detector by the intensity sorting and selection of background region to reduce the effect of neighboring targets that lead to incorrect estimation of background statistics. The existence of neighboring targets can be recognized by intensity sorting where neighboring targets usually show highest ranks. The proposed background statistics (mean, standard deviation) estimation method from median local pixels can be aware of the background context and reduce the effects of the neighboring targets, which increase the signal-to-clutter ratio. The experimental results on the synthetic APS sequence, real adjacent target sequence, and remote pedestrian sequence validated that the proposed method produced an enhanced detection rate with the same false alarm rate compared with the hysteresis-CFAR (H-CFAR) detection.
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