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Target Detection Using Radar Networks
Published in Hai Deng, Zhe Geng, Radar Networks, 2020
Other than the CA-CFAR detector and its two variations mentioned above, the order statistics (OS)-based CFAR algorithms are also widely used for target detection in nonhomogeneous interference. The OS-CFAR proposed in (Rohling, 1983) ranks the reference cells in ascending numerical order according to the interference power, and the detection threshold is selected based on k-th reference cell of the ordered list. Although the OS-CFAR is effective in mitigating the effects of the interfering targets, it suffers from excessive false alarms when clutter edges are present.
Radar Electronic Warfare
Published in Habibur Rahman, Fundamental Principles of Radar, 2019
Constant false alarm rate (CFAR): CFAR(Johnston 1979) is a technique that is necessary because of the limitation of the computer in automatic systems. It prevents the computer from being overloaded by lowering the capability of the radar to detect desired targets. Also, this is a radar receiver ECCM technique wherein the receiver adjusts its sensitivity as the intensity of the undesired signal varies. This makes the functioning of radars possible in an environment where interference due to signals from clutter, rain, jammers, and other radiating sources are present. These undesired signals can obscure real targets on the radar display or overload a computer so as to degrade decisions on absolute detection threshold criteria. The CFAR technique keeps the detection of false alarm rate constant when the radar is receiving these undesired signals. CFAR does not usually permit the detection of a target if the target is weaker than the jamming, but it does attempt to remove the confusing effects of the jamming. Thus, CFAR does not give immunity from jamming; it merely makes the operation in the presence of jamming more convenient by making the receiver less sensitive.
Radar Signal Processing: An Example of High Performance Embedded
Published in David R. Martinez, Robert A. Bond, Vai M. Michael, High Performance Embedded Computing Handbook, 2018
A. Bond Robert, I. Reuther Albert
Within a subband, time-delay and equalization processing compensate for differences in the transfer function between subarray channels. The adaptive beamforming stage transforms the subbanded data into the beam-space domain, creating a set of focused beams that enhance detection of target signals coming from a particular set of directions of interest while filtering out spatially localized interference. The pulse compression stage filters the data to concentrate the signal energy of a relatively long transmitted radar pulse into a short pulse response. The Doppler filter stage applies a fast Fourier transform (FFT) across the PRIs so that the radial velocity of targets relative to the platform can be determined. The STAP stage is a second beamforming stage, designed to adaptively combine the beams from the first beamformer stage to remove ground clutter interference. The subband synthesis stage recombines the processed subbands to recoup the full bandwidth signal. The detection stage uses constant false-alarm rate (CFAR) detection to determine whether a target is present. The estimation stage computes the target state vector, which consists of range rate, range, azimuth, elevation, and signal-to-noise ratio (SNR). Often, the estimation task is considered a back-end processing task since it is performed on a per-target basis; however, it is included here with the front-end tasks since in many existing architectures it is performed in the front-end where the signal data used in the estimation process are most readily available.
M-Sweeps multi-target analysis of new category of adaptive schemes for detecting χ2-fluctuating targets
Published in Journal of Information and Telecommunication, 2020
The radar system detection performance is related to target models as well as the background environments. In these systems, Pd is sensitive to the non-stationary clutter statistics along with the number of outliers that may be exist in the returned echoes. Therefore, the real work on detection is coming up with an appropriate threshold. The adaptively setting of the detection threshold, through the estimation of the background noise level included in the CUT, represents the key factor of any CFAR architecture. CFAR is needed for maintaining operation for automatic detection and tracking systems. Different concepts of CFAR processing have been investigated. The most popular ones are the CA-, OS- and TM schemes. Each one of these procedures has its merits and demerits and may be optimal under particular environment conditions. The difficulty in finding a solution based on a single CFAR processor to deal with diverse noise backgrounds has led to the development of composite CFAR strategy. The analysis of these standard processors as well as some of composite CFAR algorithms is our objective in this section.
Multi-target CFAR detector based on compressed sensing radar system
Published in International Journal of Electronics, 2023
Boning Feng, Huotao Gao, Yunkun Yang, Fangyu Ren, Taoming Lu
Constant false alarm rate (CFAR) technology has been widely used for radar target detection. This technique ensures that the detector maintains the same false alarm rate in different ambient clutter. So far, various research directions have been developed in the constant false alarm technique. To address the problem that the detection performance of CFAR algorithm is significantly degraded in non-homogeneous environment and multi-target scenarios. The existing literature mainly focuses on the improvement of the echo signal processing method, the clutter model, and the background-level prediction method.