As radar sensors are being miniaturized,there is a growing interest for using them in indoor sensing applications such as drone obstacle avoidance. In those novel scenarios,radars must perform well dense scenes with large number of neighboring scatterers. Central to performance the detection algorithm used separate targets from background noise and clutter. Traditionally,most systems use conven...

This article presents a deep neural network-based constant false alarm rate (NNB-CFAR) detector for simulated high-frequency surface wave radar (HFSWR) data. A network is trained to identify fluctuation parameters of each cell range-Doppler power spectrum based on the patterns present in neighbouring cells. The estimated are then used calculating detection threshold with user-specified probabil...

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Being equipped with a millimeter-wave radar allows a low-flying helicopter to sense the surroundings in real time, which significantly increases its safety. However, nonhomogeneous clutter environments, such as a multiple target situation and a clutter edge environment, can dramatically affect the radar signal detection performance. In order to improve the radar signal detection performance in ...

This paper concerns PN signal detection in multipath interference by using a passive correlation receiver with an OS CFAR processor. Noise level estimation is performed in both windows, using the ordered statistics (OS) approach. The minimum of the average decision threshold is used as an OS estimation criterion of effectiveness [1] The CFAR processor parameters are obtained by Monte Carlo simu...

The signal returns from radar targets are usually buried in thermal noise and clutter. Target detection is commonly performed by comparing radar returns to an adaptive threshold such that a constant false alarm rate (CFAR) is maintained. The threshold in a CFAR detector is set on a cell by cell basis according to the estimated noise/clutter power, which is determined by processing a group of re...

A hardware architecture that implements an adaptive filter based on energy analysis of radar echoes to improve the detection of the Constant False Alarm Rate (CFAR) algorithm is presented. Signal processing based on energy analysis emphasizes the edge of the echoes improving the performance of the detection process. The energy filter coefficients and CFAR parameters are calculated adaptively by...