This article presents a model-based motion planning and control system for autonomous vehicles and its experimental validation. The system consists of four modules: 1) global routing; 2) behavior planner; 3) local trajectory generation; and 4) trajectory tracking. The algorithm and software of each module are detailed, including a behavior planner with unified models to handle typical scenarios in both highway and urban driving, a deterministic sampling algorithm for robust responsive trajectory generation, and a dynamics-and-delay-aware preview algorithm to achieve accurate trajectory tracking. The developed system is implemented and tested at the Mcity test facility with a full-size automated car and a dozen of challenging traffic scenarios.

The innovative bistatic synthetic aperture radar (BiSAR) mission LuTan-1 (LT-1) employs a non-interrupted synchronization scheme to achieve high-precision phase synchronization. While radio frequency interference (RFI) is a major factor in deteriorating phase synchronization performance. To present the effect of RFI on the synchronization phase clearly, the characteristics of RFI in the synchronization link are described in detail, and a precise analytic expression between the amplitude, frequency, and phase of RFI and synchronization phase error is established. Furthermore, a novel pulse compression-based notch (PCN) method is proposed to eliminate the phase error introduced by RFI. In the proposed method, the saturated distortion signals resulting from strong interferences are detected and discarded by the distribution features of their modes. Then, inspired by contrary thinking, the synchronization signal after pulse compression is notched instead of the RFI. A fast missing-data iterative adaptive approach (Fast MIAA) is performed to recover the gaped RFI signal and remove it from the compressed signal. Finally, the correct synchronization phases can be extracted from the peak positions of the remaining signals. Experimental results derived from using simulated and real synchronization data of the LT-1 system validate the performance of the proposed RFI mitigation method.

The traditional methods of jamming against synthetic aperture radar (SAR), which are implemented based on linear frequency-modulated (LFM) signals, are currently unable to meet the needs of electronic countermeasures. Compared with the LFM signal, the frequency of the nonlinear frequency-modulated (NLFM) signal varies nonlinearly with time. Because of this time-frequency structure, the NLFM signal is naturally mismatched with the linear matched filter of SAR; thus, a stronger and more flexible jamming effect is produced. In this paper, the NLFM signal is used in the SAR jamming field for the first time, and a smart jamming method against SAR based on the NLFM signal is proposed, which can produce the following two jamming effects: barrage and deceptive. First, the shaping factor is defined to generate the expected NLFM signal. The shaping factor controls the jamming range in the fast time direction for barrage jamming. For deceptive jamming, a jamming method involving piecewise weighting is proposed. The shaping factor is used to control the defocusing degree of false targets. Subsequently, the pulse compression results of the jamming signal are deduced, and the influence of different parameters on the jamming effect is theoretically analyzed. Experimental results based on simulation show that the method can control the jamming range and effect in the fast time direction while achieving a flexible deceptive jamming effect.

Lane detection on road segments with complex topologies such as lane merge/split and highway ramps is not yet a solved problem. This paper presents a novel graph-embedded solution. It consists of two key parts, a learning-based low-level lane feature extraction algorithm, and a graph-embedded lane inference algorithm. The former reduces the over-reliance on customized annotated/labeled lane data. We leveraged several open-source semantic segmentation datasets (e.g., Cityscape, Vistas, and Apollo) and designed a dedicated network that can be trained across these heterogeneous datasets to extract lane attributes. The latter algorithm constructs a graph to represent the lane geometry and topology. It does not rely on strong geometric assumptions such as lane lines are a set of parallel polynomials. Instead, it constructs a graph based on detected lane nodes. The lane parameters in the world coordinate are inferred by efficient graph-based searching and calculation. The performance of the proposed method is verified on both open source and our own collected data. On-vehicle experiments were also conducted and the comparison with Mobileye EyeQ2 shows favorable results.

The azimuth multichannel synthetic aperture radar (SAR), as one of the mainstream technologies for achieving high-resolution and wide-swath (HRWS) imaging, has been successfully employed in several on-orbit SAR missions. However, the unavoidable phase errors among channels result in azimuth ambiguity, deteriorating the recognizability of targets in SAR images. Additionally, the radio frequency interference (RFI) exacerbates the difficulty of phase error estimation. To address this issue, a phase error calibration method based on least spectrum difference (LSD) is proposed. Firstly, the multichannel signals are reconstructed using the linear mapping form of the reconstruction algorithm. Secondly, the objective function is established based on the continuity of the azimuth spectrum, by which only the signals near the discontinuity points are proposed. Finally, the optimal estimation of the phase differences can be obtained after iteration. In LSD method, the RFI to the objective function is mitigated due to the operation in the range-Doppler domain, and the iteration is proposed only using a few data near the discontinuity points thus saving much computational cost. Experimental results based on the simulated data and real bistatic echoes of the LuTan-1 (LT-1) mission validate the superiority of the proposed LSD method.

The purpose of barrage jamming against the synthetic aperture radar (SAR) is to conceal the targets to be protected, but the jamming blocks on the regions of interest (ROIs) generated by the existing barrage jamming methods cannot fully destroy the structural information of the targets in the SAR images, resulting in jamming effects that need to be improved. Aiming at this deficiency, we analyze the relationship between the statistical characteristics of the jamming templates and the jamming effects using the structural similarity (SSIM), and design a multi-scale jamming template. The jamming blocks corresponding to this template can better disturb the structural information of the targets. In addition, in order to reduce the complexity of signal modulation and ensure a large effective jamming coverage, we propose a two-step jamming signal modulation method. Simulation experiments demonstrate the performances of the signal modulation method and the jamming effect of the multi-scale template.