WEN F Q , WANG J , SHI J P , et al . Auxiliary vehicle positioning based on robust DOA estimation with unknown mutual coupling [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 6 ): 5521 - 5532 . DOI: 10.1109/JIoT.6488907 http://doi.org/10.1109/JIoT.6488907 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6488907 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6488907

HAN R Y , GUAN Q S , YU F C , et al . Congestion and position aware dynamic routing for the internet of vehicles [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 12 ): 16082 - 16094 . DOI: 10.1109/TVT.25 http://doi.org/10.1109/TVT.25 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=25 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=25

龚诗雄 , 王旭 , 孔国杰 , 等 . 多车协同目标跟踪方法 [J ] . 兵工学报 , 2022 , 43 ( 10 ): 2429 - 2442 .

GONG S X , WANG X , KONG G J , et al . Methods for multi-vehicle cooperative object tracking [J ] . Acta Armamentarii , 2022 , 43 ( 10 ): 2429 - 2442 . (in Chinese) DOI: 10.12382/bgxb.2021.0462 http://doi.org/10.12382/bgxb.2021.0462 Multi-vehicle information fusion technology is an important way to improve the perception of the environment of ground unmanned systems. To address the problem of discontinuous and unstable object tracking in single-vehicle sensors caused by vision occlusion and blind spots, a result-level fusion system model for centralized multi-vehicle cooperative perception is proposed. The system model uses lidar as the vehicle perception sensor and stands on the D-S evidence theory to fuse the environment grid maps constructed by different vehicles at the main control terminal to obtain a global static environment map. Based on this environment model, a multi-vehicle cooperative object detection and tracking method is designed. First, a maximum value suppression method is used to resolve the fusion conflict of detected objects. Then, a cascaded dynamic object matching and tracking management method is designed to complete object prediction and tracking and send the results to vehicles. The test results of a real-vehicle system composed of two unmanned vehicles suggest that when the object is occluded, the proposed multi-vehicle cooperative object detection and tracking architecture can obtain more comprehensive environmental information of the object than a single-vehicle perception system. No tracking object is missed, and no jump occurs. The error between the tracker's output position state result and the detection result is small. The state of the tracked object can be accurately estimated, and the tracking trajectory remains continuous, thus effectively improving the field of vision of the single-vehicle environment.

CHEN Z L , CHEN K , XU R F , et al . An efficient gridless vehicle positioning method via angle estimation [C ] // Proceedings of the 2021 13th International Conference on Wireless Communications and Signal Processing . Changsha, China : IEEE , 2021 : 1 - 5 .

张征 , 刘春光 , 马晓军 , 等 . 一种基于数据融合的全轮驱动车辆质心侧偏角估计方法 [J ] . 兵工学报 , 2020 , 41 ( 5 ): 842 - 849 . DOI: 10.3969/j.issn.1000-1093.2020.05.002 http://doi.org/10.3969/j.issn.1000-1093.2020.05.002 为准确估计全轮电驱动车辆行驶状态参数，设计了一种基于数据融合的质心侧偏角估计方法。该方法充分利用低成本普通车载传感器信息、电机输入信息和驾驶信号，在建立非线性3自由度 车辆模型和轮胎模型基础上，采用无迹卡尔曼滤波算法对质心侧偏角进行估计；同时通过信号积分法估计质心侧偏角，结合车辆行驶工况和路面条件，将无迹卡尔曼滤波和信号积分两种算法结果进行了数据融合。基于硬件在环实时仿真平台进行了车辆操纵仿真验证，结果表明，提出的估计算法与单一估计算法相比，具有更高的观测精度，能够满足多种行驶工况下的质心侧偏角观测需求。

ZHANG Z , LIU C G , MA X J , et al . Method for estimating sideslip angle of all-wheel drive vehicle based on data fusion [J ] . Acta Armamentarii , 2020 , 41 ( 5 ): 842 - 849 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2020.05.002 http://doi.org/10.3969/j.issn.1000-1093.2020.05.002 A method for estimating sideslip angle based on data fusion is proposed to obtain the driving state parameters of all-wheel electric drive vehicle. On the basis of three-degree-of-freedom vehicle model and tire model, the method fully utilizes the information from a low-cost common vehicle-mounted sensor, in-wheel motor input information as well as driving signals, and the sideslip angle is estimated by using the unscented Kalman filter algorithm. Besides, the sideslip angle is estimated by signal integration method. Combined with vehicle driving conditions and road conditions, the estimated values of the unscented Kalman filtering algorithm and the signal integral algorithm are fused to obtain the final estimation of sideslip angle. A series of simulations were conducted on the hardware-in-the-loop real-time simulation platform. The results show that the proposed estimation algorithm has higher observation accuracy compared with the single estimation algorithm, which can meet the requirements of mass center sideslip angle observation under various driving conditions. Key

BUEHRER R M , WYMEERSCH H , VAGHEFI R M . Collaborative sensor network localization: algorithms and practical issues [J ] . Proceedings of the IEEE , 2018 , 106 ( 6 ): 1089 - 1114 . DOI: 10.1109/JPROC.2018.2829439 http://doi.org/10.1109/JPROC.2018.2829439 https://ieeexplore.ieee.org/document/8359446/ https://ieeexplore.ieee.org/document/8359446/

谢良波 , 李升 , 周牧 , 等 . 基于散射体信息的室内NLOS多站协作定位算法 [J ] . 通信学报 , 2021 , 42 ( 5 ): 63 - 74 . DOI: 10.11959/j.issn.1000-436x.2021070 http://doi.org/10.11959/j.issn.1000-436x.2021070 针对现有视距（LOS）定位方法在非视距（NLOS）环境中定位精度急剧恶化的问题，提出一种基于散射体信息的室内NLOS多站协作定位算法，可在完全没有LOS路径的情况下进行定位。首先，利用多AP以及联合场景先验信息协同确定目标NLOS区域和散射体模糊区域；其次，根据信号的到达角对散射体区域进行约束，并在区域内搜索散射体的位置信息；然后，利用这些信息构造基于差分飞行时间的误差最小化方程；最后，提出混合使用遗传算法和列文伯格马夸尔特算法求解目标方程。仿真及真实环境模拟测试结果显示，所提算法仅通过NLOS路径即可定位目标。

XIE L B , LI S , ZHOU M , et al . Scatterer information based indoor NLOS multiple base station cooperative localization algorithm [J ] . Journal on Communications , 2021 , 42 ( 5 ): 63 - 74 . (in Chinese) DOI: 10.11959/j.issn.1000-436x.2021070 http://doi.org/10.11959/j.issn.1000-436x.2021070 In indoor environments, the localization accuracy of existing line of sight (LOS) solutions will deteriorate severely in non-line-of-sight (NLOS) environment.In order to solve this problem, an scatterer information based indoor NLOS multiple base stations cooperative localization algorithm was proposed, which could realize localization when no LOS path was available.Firstly, the target NLOS area and scatterer blur area were collaboratively determined through multiple AP and joint scene prior information.Secondly, the areas of scatterer were further constrained according to the angle of arrival.Then, an error minimization equation based on the differential time of flight was established by employing angle, scatterer and time.Finally, a hybrid algorithm using genetic algorithm and Levenberg Marquardt algorithm was proposed to solve the objective equation.Simulation and measurement results show that the proposed algorithm can localize the target with only NLOS paths.

PAL P , VAIDYANATHAN P P . Coprime sampling and the music algorithm [C ] // Proceedings of 2011 Digital Signal Processing and Signal Processing Education Meeting . Sedona, AZ, US : IEEE , 2011 : 289 - 294 .

LIU S H , MAO Z H , ZHANG Y M , et al . Rank minimization-based Toeplitz reconstruction for DOA estimation using coprime array [J ] . IEEE Communications Letters , 2021 , 25 ( 7 ): 2265 - 2269 . DOI: 10.1109/LCOMM.2021.3075227 http://doi.org/10.1109/LCOMM.2021.3075227 https://ieeexplore.ieee.org/document/9411879/ https://ieeexplore.ieee.org/document/9411879/

ZHOU C W , GU Y J , FAN X , et al . Direction-of-arrival estimation for coprime array via virtual array interpolation [J ] . IEEE Transactions on Signal Processing . 2018 , 66 ( 22 ): 5956 - 5971 . DOI: 10.1109/TSP.78 http://doi.org/10.1109/TSP.78 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=78 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=78

LI J F , SHEN M W , JIANG D F . DOA estimation based on combined ESPRIT for co-prime array [C ] // Proceedings of 2016 IEEE 5th Asia-Pacific Conference on Antennas and Propagation . Taiwan, China : IEEE , 2016 : 117 - 118 .

周成伟 , 郑航 , 顾宇杰 , 等 . 互质阵列信号处理研究进展:波达方向估计与自适应波束成形 [J ] . 雷达学报 , 2019 , 8 ( 5 ): 558 - 577 .

ZHOU C W , ZHENG H , GU Y J , et al . Research progress on coprime array signal processing: direction-of-arrival estimation and adaptive beamforming [J ] . Journal of Radars , 2019 , 8 ( 5 ): 558 - 577 . (in Chinese)

ZHANG D Y , MOENESS G A , BRAHAM H . Sparsity-based DOA estimation using co-prime arrays [C ] // Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing . Vancouver, Canada : IEEE , 2013 : 3967 - 3971 .

ZHAO T , ARYE N . Sparse direction of arrival estimation using co-prime arrays with off-grid targets [J ] . IEEE Signal Processing Letters , 2014 , 21 ( 1 ): 26 - 29 . DOI: 10.1109/LSP.2013.2289740 http://doi.org/10.1109/LSP.2013.2289740 http://ieeexplore.ieee.org/document/6657727/ http://ieeexplore.ieee.org/document/6657727/

WU X H , ZHU W P , YAN J . Direction of arrival estimation for off-grid signals based on sparse Bayesian learning [J ] . IEEE Sensors Journal , 2016 , 16 ( 7 ): 2004 - 2016 . DOI: 10.1109/JSEN.2015.2508059 http://doi.org/10.1109/JSEN.2015.2508059 https://ieeexplore.ieee.org/document/7353103/ https://ieeexplore.ieee.org/document/7353103/

SUN F G , WU Q H , SUN Y M , et al . An iterative approach for sparse direction-of-arrival estimation in co-prime arrays with off-grid targets [J ] . Digital Signal Processing , 2017 , 61 : 35 - 42 . DOI: 10.1016/j.dsp.2016.06.007 http://doi.org/10.1016/j.dsp.2016.06.007 https://linkinghub.elsevier.com/retrieve/pii/S1051200416300707 https://linkinghub.elsevier.com/retrieve/pii/S1051200416300707

SI W J , ZENG F H , HOU C B , et al . A sparse-based off-grid DOA estimation method for coprime arrays [J ] . Sensors , 2018 , 18 ( 9 ): 3025 . DOI: 10.3390/s18093025 http://doi.org/10.3390/s18093025 http://www.mdpi.com/1424-8220/18/9/3025 http://www.mdpi.com/1424-8220/18/9/3025 Recently, many sparse-based direction-of-arrival (DOA) estimation methods for coprime arrays have become popular for their excellent detection performance. However, these methods often suffer from grid mismatch problem due to the discretization of the potential angle space, which will cause DOA estimation performance degradation when the target is off-grid. To this end, we proposed a sparse-based off-grid DOA estimation method for coprime arrays in this paper, which includes two parts: coarse estimation process and fine estimation process. In the coarse estimation process, the grid points closest to the true DOAs, named coarse DOAs, are derived by solving an optimization problem, which is constructed according to the statistical property of the vectorized covariance matrix estimation error. Meanwhile, we eliminate the unknown noise variance effectively through a linear transformation. Due to finite snapshots effect, some undesirable correlation terms between signal and noise vectors exist in the sample covariance matrix. In the fine estimation process, we therefore remove the undesirable correlation terms from the sample covariance matrix first, and then utilize a two-step iterative method to update the grid biases. Combining the coarse DOAs with the grid biases, the final DOAs can be obtained. In the end, simulation results verify the effectiveness of the proposed method.

ZUO W L , XIN J M , ZHENG N N , et al . Subspace-based near-field source localization in unknown spatially nonuniform noise environment [J ] . IEEE Transactions on Signal Processing , 2020 , 68 : 4713 - 4726 . DOI: 10.1109/TSP.78 http://doi.org/10.1109/TSP.78 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=78 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=78

ZHANG X F , CHEN W Y , WANG Z , et al . Localization of near-field sources: a reduced-dimension MUSIC algorithm [J ] . IEEE Communications Letters , 2018 , 22 ( 7 ): 1422 - 1425 . DOI: 10.1109/LCOMM.2018.2837049 http://doi.org/10.1109/LCOMM.2018.2837049 https://ieeexplore.ieee.org/document/8359308/ https://ieeexplore.ieee.org/document/8359308/

CHEN G H , ZENG X P , JIAO S , et al . Accuracy near-field localization algorithm at low SNR using fourth-order cumulant [J ] . IEEE Communications Letters , 2022 , 24 ( 3 ): 553 - 557 . DOI: 10.1109/COML.4234 http://doi.org/10.1109/COML.4234 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4234 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4234

KUANG M D , XIE J , WANG L , et al . Fast reweighted smoothed L0-Norm near-field source localization based on fourth-order statistics [J ] . IEEE Communications Letters , 2022 , 26 ( 1 ): 74 - 78 . DOI: 10.1109/LCOMM.2021.3125984 http://doi.org/10.1109/LCOMM.2021.3125984 https://ieeexplore.ieee.org/document/9605260/ https://ieeexplore.ieee.org/document/9605260/

PAN J J , RAJ S P , MEN S Y . A search-free near-field source localization method with exact signal model [J ] . Journal of Systems Engineering and Electronics , 2021 , 32 ( 4 ): 756 - 763 . DOI: 10.23919/JSEE.2021.000065 http://doi.org/10.23919/JSEE.2021.000065 Most of the near-field source localization methods are developed with the approximated signal model, because the phases of the received near-field signal are highly non-linear. Nevertheless, the approximated signal model based methods suffer from model mismatch and performance degradation while the exact signal model based estimation methods usually involve parameter searching or multiple decomposition procedures. In this paper, a search-free near-field source localization method is proposed with the exact signal model. Firstly, the approximative estimates of the direction of arrival (DOA) and range are obtained by using the approximated signal model based method through parameter separation and polynomial rooting operations. Then, the approximative estimates are corrected with the exact signal model according to the exact expressions of phase difference in near-field observations. The proposed method avoids spectral searching and parameter pairing and has enhanced estimation performance. Numerical simulations are provided to demonstrate the effectiveness of the proposed method.

WANG M Z , NEHORAI A . Coarrays, MUSIC, and the cramér-rao bound [J ] . IEEE Transactions Signal Processing , 2017 , 65 ( 4 ): 933 - 946 . DOI: 10.1109/TSP.2016.2626255 http://doi.org/10.1109/TSP.2016.2626255 http://ieeexplore.ieee.org/document/7738579/ http://ieeexplore.ieee.org/document/7738579/

王绪虎 , 白浩东 , 张群飞 , 等 . 阵列互耦情况下基于稀疏贝叶斯学习的离网格DOA估计 [J ] . 振动与冲击 , 2022 , 41 ( 17 ): 303 - 312 .

WANG X H , BAI H D , ZHANG Q F , et al . Off-grid DOA estimation based on sparse Bayesian learning under interaction among array elements [J ] . Journal of Vibration and Shock , 2022 , 41 ( 17 ): 303 - 312 . (in Chinese)