For K-distributed sea clutter, a constant false alarm rate (CFAR) is crucial as a desired property for automatic target detection in an unknown and non-stationary background. In multiple-target scenarios, the target masking effect reduces the detection performance of CFAR detectors evidently. A machine learning based processor, associating the artificial neural network (ANN) and a clustering algorithm of density-based spatial clustering of applications with noise (DBSCAN), namely, DBSCAN-CFAR, is proposed herein to address this issue. ANN is trained with a symmetrical structure to estimate the shape parameter of background clutter, whereas DBSCAN is devoted to excluding interference targets and sea spikes as outliers in the leading and lagging windows that are symmetrical about the cell under test (CUT). Simulation results verified that the ANN-based method provides the optimal parameter estimation results in the range of 0.1 to 30, which facilitates the control of actual false alarm probability. The effectiveness and robustness of DBSCAN-CFAR are also confirmed by the comparisons of conventional CFAR processors in different clutter conditions, comprised of varying target numbers, shape parameters, and false alarm probabilities. Although the proposed ANN-based DBSCAN-CFAR processor incurs more elapsed time, it achieves superior CFAR performance without a prior knowledge on the number and distribution of interference targets.

An antenna array system with high angular resolution is proposed to adapt the demands of both medium-range radar (MRR) and longrange radar (LRR) detections for 77 GHz automotive radars. Both the MRR and LRR modes are integrated into one substrate based on the optimized sparse array topology, which makes full use of the antenna aperture size to improve the angular resolution of the proposed system. Two-dimensional series-fed weighting arrays are designed via the Taylor synthesis method to effectively heighten the antenna gain and restrain the sidelobe level. After completing the fabrication, measurement results of the proposed antenna array are in good agreement with the simulation results. Moreover, the angular resolution is verified to be 0.5°by adopting the coherent signal space method (CSM) with stepped frequency transmitting waveform, which validates the effectiveness of the proposal.

For radar target detection, the selection of the optimal constant false alarm rate (CFAR) detector usually relies on clutter distribution types. By integrating two types of Mean Level and log-t CFAR detectors, a reconfigurable hardware architecture is proposed and implemented on field programmable gate array (FPGA). It allows to switch a suitable detector for specific clutter distribution and configure the parameters including the number of reference and guard cells, the threshold factor, and the desired false alarm probability. Synthesis results reveal its advantages of occupying 18% less hardware resources than the architecture that naively integrates two types of detectors. According to the experimental results, the proposed architecture can perform a processing speed of 100 MHz and require only 83 microseconds for a clutter of 8192 samples.

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