Jiazhi Zhang

Good stability is a prerequisite for vehicle operation. However, under certain extreme conditions, the vehicle’s state can quickly change and stability can be lost. In order to improve the stability of distributed drive electric vehicles (DDEVs) under these conditions, this paper proposes a hierarchical control strategy. The upper-level controller adopts a backstepping nonlinear control method to achieve stable yaw rate control. The lower-level controller combines backstepping and optimization to take into account the desired tire slip ratio while minimizing driver intervention and considering limited motor torque. Experimental results show that the proposed control strategy is superior to the average distribution (AD) strategy. The proposed control strategy can meet various requirements such as longitudinal/lateral stability, while reducing the interference on the driver’s torque demand in hazardous situations.

The nonstationary clutter is one of the biggest challenge to ocean remote sensing radar system such as high frequency (HF) mixed-mode surface wave radar. The performance of space-time adaptive processing (STAP) degrades badly with limited homogeneous secondary training data support. Single dataset algorithms overcome the problem by working on primary data solely. But the heavy computational complexity as well as the inaccurate estimation of the clutter covariance matrix restricts the practical application of these methods. In this letter, we propose a novel reduce-rank-based single dataset STAP to suppress the nonhomogeneous clutter in practical HF radar system. A fast implementation of subspace tracking algorithm is introduced to estimate the clutter subspace as well as reduce the computational cost via pulse iteration method. The effectiveness of the proposed method is verified by both simulated and experimental data. The results show it outperforms traditional single dataset STAP methods and the nonstationary clutter can be greatly suppressed.

Combined sky‐wave and surface‐wave monostatic high‐frequency (CSSM‐HF) radar is a new type of mono‐static high frequency (HF) radar that uses multiple combined propagation modes of skywave and surface wave to detect targets. However, the detection range of different propagation modes partially overlaps, which results in multiple mode echoes from one target. To track the target in the presence of multi‐mode echoes, a new multi‐mode target tracking algorithm is proposed in the study, which uses the detection range as a priori knowledge. In CSSM‐HF radar, the possible propagation mode of the target echo is related to the position of the target itself. Therefore, the study uses the detection range simulation results as a priori knowledge to construct a propagation mode judgment module to effectively improve the calculation efficiency and performance of the tracking algorithm. The simulation results show that the proposed algorithm can effectively track the target in the presence of multi‐mode echoes and the tracking performance of the target with lower abscissa speed is better. In addition, the mode judgment module directly affects the performance of the algorithm. The tracking performance will be reduced when the output of the mode judgment module has an error mode.

The nonhomogeneous clutter is one of the biggest challenge for ocean remote sensing radar system such as high frequency hybrid sky-surface wave radar. Space-time adaptive processing (STAP) is a powerful method to suppress the clutter but the training samples in adjacent range gates are not always independent and identically distributed with the cell under test in the heterogeneous environment. So the estimation of the clutter covariance matrix will be inaccurate and degrade the STAP performance. In this paper, a novel training sample selection method based on information geometric means is proposed to choose the more homogeneous secondary data containing the precise clutter information of cell under test. The simulation results of selection performance is conducted to show the advantages of the Information geometric mean based estimators compared with the existing estimators. Combined with joint domain localized STAP, the effectiveness of the proposed method is verified by experimental data. The results show the proposed method outperforms conventional STAP method and the nonhomogeneous clutter can be greatly suppressed.

High-frequency combined sky-wave and surface-wave monostatic radar (CSSM-HF RADAR) is a new type of radar system inspired by the skywave components of high-frequency surface-wave radar (HFSWR). The new type of radar uses the combined propagation paths of skywave and ground wave to detect targets, including four paths without considering ionospheric stratification. The related research of the CSSM-HF RADAR is relatively rare, and this paper carries out a detailed simulation analysis of its coverage area. Coverage area analysis is significant for subsequent work, such as target detection, frequency selection, and echo propagation path matching. In this paper, the signal-to-noise ratio (SNR) in the atmospheric background is derived at first, and then the parameters that change with propagation paths are analyzed. Finally, the coverage area of each propagation mode is calculated by setting the minimum detectable SNR. The simulation results show that the coverage areas of the four propagation paths at low frequency have overlapping regions. As the operating frequency increases, the coverage area of each propagation mode gradually separates, and some areas that cannot be covered may appear, however, the whole coverage area of the CSSM-HF RADAR increases.

The nonhomogeneous clutter is a major challenge for ship detection in high-frequency mixed-mode surface wave radar. In this paper, a geometric barycenter-based reduced-dimension space-time adaptive processing method is proposed to suppress the clutter. Given the measured dataset, the range correlation of sea clutter is first investigated. Then, joint domain localized processing is applied to solve the training samples starve scenario in a practical system. The geometric barycenter-based training data selector is presented to select valid training samples and improve the accuracy of the clutter covariance matrix estimation. Finally, the validity of the proposed method is verified using the experimental data and the results show that it outperforms the conventional method in the nonhomogeneous environment of a practical system.

The high-frequency hybrid sky-surface wave radar (HFSSWR) is a new kind of over-the-horizon radar system. Combining the advantages of both the sky wave radar and the surface wave radar, HFSSWR has drawn much attention in recent years. But the new system also brings new challenges. One of the most severe problems is that the heterogeneous environment makes a big challenge for the sea clutter suppression processing. Due to the nonstationary properties of the ionosphere, the first-order sea clutter statistics change significantly among range bins. So the new efficient sea clutter suppression method is required, and the clutter characteristics analyses are also in urgent need of research in order to guide the design of the algorithm in the background of HFSSWR. In this paper, utilizing the measured data set, we first analyse the range and spatial characteristics of the nonhomogeneous first-order sea clutter in HFSSWR. Then, an improved main-lobe cancellation (IMLC) method based on single notch space filter and correlation analysis is proposed to get training data which contains precise clutter information. A main-lobe clutter canceller based on the single notch space filter has been developed to block the target component, and an optimized correlation analysis (OCA) strategy is presented to choose the efficient training data. Finally, the method is examined by measured data and the results indicate that the method has a good performance for broadening first-order sea clutter suppression than the traditional method.

In order to adapt to the crowed High Frequency (HF) electromagnetic environment, improve the utilization rate of bandwidth and further realize the higher range resolution, the sparse random stepped frequency (SRSF) signal is introduced into HF radar. Signal model based on compressive sensing (CS) theory is put forward to reconstruct the range spectrum of targets. However, redundancy is generated due to external factors or unreasonable parameters in the process of estimating. Therefore, in this paper, key external factors affecting the range estimation performance are analyzed in detail. These factors mainly include mismatched distance redundancy, oversample redundancy, and also distortion from unknown velocities of moving targets. Specific elimination methods are also provided. Comparison between before and after elimination shows the effectiveness of the proposed method.

The echo signal of high-frequency surface wave radar is a signal which may contain both ocean path echoes and ionosphere path echoes. Due to the diffuse scattering characteristic of target, a portion of the echo radar radiation may travel upwards to the ionosphere. Under certain conditions, it may be reflected by the ionosphere and received by the receiving antennas. This constitutes a multipath propagation model and thus false track maybe formed, which will increase the risk of false alarms. In this letter, a new method aims to determine the correspondence of echo signals between the two propagation paths and to eliminate false track is presented. A geometric model based on this mixed propagation mode is established and a series of criteria is proposed to determine whether different suspected targets information belong to the same actual target. Through the experimental analysis using the measured data, the feasibility and correctness of the method is proved preliminary.