Human-vehicle classification plays an important role in advanced driver assistance systems (ADAS). The use of millimeter wave (mmWave) radar sensor in human-vehicle classification algorithms is of great significance since the sensor maintains to be robust in severe weather (e.g. fog, snow, etc.). To improve classification accuracy under complex scenes of autonomous driving, a new mmWave radar point cloud classification algorithm is proposed in this paper, which realizes human-vehicle classification employing a newly proposed point cloud feature vector with eleven dimensions and based on kernel support vector machine (SVM) classifier. To verify the validity and robustness of the proposed feature vector, a 77 GHz radar is used to collect two datasets for static and moving objects, respectively, with each dataset taken for pedestrians and vehicles at different distances and angles. Experimental results show that the proposed algorithm achieves higher classification accuracy than a conventional one based on signal features. For the comparison based on the same number of dimensions, the number of dimensions of the proposed feature vector is decreased by removing the features with low significance. Experimental results verify that the proposed algorithm maintains advantage over the conventional one.

In this letter, we present a unified Bayesian inference framework for generalized linear models (GLM) which iteratively reduces the GLM problem to a sequence of standard linear model (SLM) problems. This framework provides new perspectives on some established GLM algorithms derived from SLM ones and also suggests novel extensions for some other SLM algorithms. Specific instances elucidated under such framework are the GLM versions of approximate message passing (AMP), vector AMP (VAMP), and sparse Bayesian learning (SBL). It is proved that the resultant GLM version of AMP is equivalent to the well-known generalized approximate message passing (GAMP). Numerical results for 1-bit quantized compressed sensing (CS) demonstrate the effectiveness of this unified framework.

In this letter, we consider the direction of arrival (DOA) estimation problem from one-bit quantized measurements in both single and multi snapshot scenarios. First, by formulating the one-bit DOA estimation in the single snapshot as a generalized linear model inference problem, the recently generalized sparse Bayesian learning (Gr-SBL) algorithm is applied to solve it. Then, the Gr-SBL is extended to the multi snapshot scenario by decoupling the multi snapshot one-bit DOA estimation problem into a sequence of the single snapshot sub-problems. Numerical results demonstrate the efficiency of the proposed Gr-SBL algorithms.

One-bit radar, performing signal sampling and quantization by a one-bit ADC, is a promising technology for many civilian applications due to its low-cost and lowpower consumptions. In this paper, problems encountered by one-bit LFMCW radar are studied and a two-stage target detection method termed as the dimension-reduced generalized approximate message passing (DR-GAMP) approach is proposed. Firstly, the spectrum of one-bit quantized signals in a scenario with multiple targets is analyzed. It is indicated that highorder harmonics may result in false alarms (FAs) and cannot be neglected. Secondly, based on the spectrum analysis, the DR-GAMP approach is proposed to carry out target detection. Specifically, linear preprocessing methods and target predetection are firstly adopted to perform the dimension reduction, and then, the GAMP algorithm is utilized to suppress high-order harmonics and recover true targets. Finally, numerical simulations are conducted to evaluate the performance of one-bit LFMCW radar under typical parameters. It is shown that compared to the conventional radar applying linear processing methods, one-bit LFMCW radar has about 1:3 dB performance gain when the input signal-to-noise ratios (SNRs) of targets are low. In the presence of a strong target, it has about 1:0 dB performance loss.

In conventional fast-ramp based target detection schemes for the frequency modulated continuous wave (FMCW) radar systems, all chirp sequences are performed fully on two-dimensional fast Fourier transforms (FFTs) to estimate range and velocity of a target, and all range-Doppler cells are scanned by the two-dimensional (2D) cell-averaging constant false alarm rate (CA-CFAR) detector to extract valid targets against background noise. This results in an extremely high computational complexity. To decrease the complexity while maintaining a desired performance, this paper proposes a target detection scheme based on distributed one-dimensional (1D) CA-CFAR and region of interest (ROI) pre-processing, which performs Doppler-FFT only for range ROI cells and scans only Doppler ROI cells to extract pre-targets. Simulation results indicate that the proposed scheme significantly decreases complexity and simultaneously improves detection performance in terms of detection probability, figure of merit (FoM) and CFAR loss, as compared to the conventional schemes. The advantages of the proposed scheme over the conventional ones are further verified by field tests under multi-target scenario, using our recently developed FMCW radar with high angular resolution.