Constant false alarm rate (CFAR) detection algorithms, which are widely used in frequency-modulated continuous wave (FMCW) radar systems, achieve target detection by employing a threshold determined on the basis of a predicted background level. However, in multitarget scenarios, the multitarget shadowing effect can lead to inaccurate prediction of the background level and improper setting of the threshold, which then results in severely degraded CFAR performance. To combat this multitarget shadowing effect, a novel CFAR algorithm based on sparsity adaptive correlation maximization (SACM-CFAR) is proposed in this work. The proposed SACM-CFAR algorithm realizes target detection by utilizing the correlation between linear measurements of the radar intermediate frequency (IF) signal and the sensing matrix. To achieve a desired false alarm rate, the proposed algorithm determines the threshold by estimating the distributed parameters of the reduced sample set obtained by removing the detected targets from the original sample set. Both simulation results and field test results verify that the proposed algorithm outperforms conventional algorithms in multitarget scenarios.

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.

Vehicle detection systems play a significant role in intelligent transportation systems (ITSs). At present, vehicle detection is realized either by very costly inductive loops, by video cameras that require good weather conditions and high computing resources, or by recently developed magnetic sensors that only obtain limited detection accuracy in congested traffic conditions. To overcome these challenges, this paper develops a passive radar system that is robust to bad weather conditions, low cost and radio silent for vehicle detection. The device is installed at the side of the road under crowded traffic conditions. In the developed system, cross-correlation (CC) operation is applied to obtain suboptimal detection gain and computational complexity. To decrease the impact of interference that is independently and identically distributed (i.i.d.) across isolated frequency channels and that of the Doppler effect produced by a mobile vehicle, a multichannel joint detection scheme based on multichannel fusion (MCF) is proposed and implemented. Furthermore, to achieve high resolution, an illuminator of opportunity (IO) with wide bandwidth is selected, and the system is then designed to receive a digital video broadcast-satellite (DVB-S) signal with a bandwidth of 37.5 MHz over multiple channels simultaneously. Simulation results show that the proposed MCF achieves better performance than the single-channel-based CC, and the advantage can be enhanced by increasing the number of channels for joint detection. Field test results further verify the novelty of the MCF and the developed passive radar prototype.

This letter examines the fusion detection challenge in multi-frequency-based passive bistatic radar (MF-PBR) in the presence of the signal-to-noise ratio (SNR) difference. The traditional equal-gain-combination (EGC) based methods suffer from performance loss when the SNR difference over the multiple frequency channels is enormous. To this end, a novel weighted combination strategy is proposed for quasi-coherent MF integration. Specifically, a closed-form expression of the fusion weight is derived via modifying the non-coherent integration-based fusion weight with respect to the MF channel parameters. Moreover, for the implementation of the proposed fusion technique, an iterative algorithm for the estimation of MF channel parameters is also developed. Field test results verify the superiority of the proposed method in terms of detection capability.

Passive bistatic radar (PBR)-based moving target detection (MTD) has benefited greatly from multi-frequency (MF) integration, which can effectively improve the detection capability of weak targets. However, with the increase in the coherent processing interval (CPI) and carrier-frequency separation, Doppler spread will appear in the range-Doppler maps (RDMs) over different frequency bands, which severely limits the processing gain of MF integration. In this paper, a novel MTD algorithm is proposed to achieve both long-time integration and quasi-coherent MF integration. More specifically, the proposed method consists of two main steps, where a modified Radon–Fourier transform (RFT), termed as MF-based RFT (MF-RFT), is, firstly, used to eliminate the Doppler spread via designing a sequential of MF-based Doppler filter banks. Following the MF-RFT, a phase-compensation-based method is also developed to further remove the residual phase errors. This method involves formulating an optimization problem based on the minimum-entropy criterion and employing a particle swarm optimization (PSO) algorithm to solve it, after which quasi-coherent MF integration can be achieved with robustness. Both numerical results and field test results based on digital video broadcasting-satellite (DVB-S) signals demonstrate that the proposed algorithm outperforms the existing methods in the scenario of weak MTD.