A Direct Feedback FVF LDO for High Precision FMCW Radar Sensors in 65-nm CMOS Technology.

A direct feedback flipped voltage follower (FVF) LDO for a high-precision frequency-modulated continuous-wave (FMCW) radar is presented. To minimize the effect of the power supply ripple on the FMCW radar sensor's resolution, a folded cascode error amplifier (EA) was connected to the outer loop of the FVF to increase the open-loop gain. The direct feedback structure enhances the PSRR while minimizing the power supply ripple path and not compromising a transient response. The flipped voltage follower with a super source follower forms a fast feedback loop. The stability and parameter variation sensitivity of the multi-loop FVF LDO were analyzed through the state matrix decomposition. We implemented the FVF LDO in TSMC 65 nm CMOS technology. The fabricated FVF LDO supplied a maximum load current of 20 mA with a 1.2 V power supply. The proposed FVF LDO achieved a full-spectrum PSR with a low-frequency PSRR of 66 dB, unity-gain bandwidth of 469 MHz, and 20 ns transient settling time with a load current step from 1 mA to 20 mA.

Real-time concealed-object detection and recognition with passive millimeter wave imaging.

Millimeter wave (MMW) imaging is finding rapid adoption in security applications such as concealed object detection under clothing. A passive MMW imaging system can operate as a stand-off type sensor that scans people in both indoors and outdoors. However, the imaging system often suffers from the diffraction limit and the low signal level. Therefore, suitable intelligent image processing algorithms would be required for automatic detection and recognition of the concealed objects. This paper proposes real-time outdoor concealed-object detection and recognition with a radiometric imaging system. The concealed object region is extracted by the multi-level segmentation. A novel approach is proposed to measure similarity between two binary images. Principal component analysis (PCA) regularizes the shape in terms of translation and rotation. A geometric-based feature vector is composed of shape descriptors, which can achieve scale and orientation-invariant and distortion-tolerant property. Class is decided by minimum Euclidean distance between normalized feature vectors. Experiments confirm that the proposed methods provide fast and reliable recognition of the concealed object carried by a moving human subject.