Joint State Equality Constraint Identification and Recursive Filtering Based on Deep Learning

Yuemei QIN; Zhong CHEN; Yanbo YANG; Shuying LI

doi:10.12382/bgxb.2024.0578

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Joint State Equality Constraint Identification and Recursive Filtering Based on Deep Learning

更新时间：2025-09-05

- Joint State Equality Constraint Identification and Recursive Filtering Based on Deep Learning
- Acta Armamentarii Vol. 46, Issue 6, Pages: 240578(2025)
- 作者机构：
  
  1. 西安邮电大学自动化学院, 陕西西安 710121
  2. 西北工业大学自动化学院, 陕西西安 710129
  3. 信息融合技术教育部重点实验室, 陕西西安 710129
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2024.0578
  CLC：
- Received：12 July 2024，
  
  Published Online：28 June 2025，
  
  Published：10 June 2025
- 稿件说明：
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Yuemei QIN, Zhong CHEN, Yanbo YANG, et al. Joint State Equality Constraint Identification and Recursive Filtering Based on Deep Learning[J]. Acta Armamentarii, 2025, 46(6): 240578.
DOI：

Yuemei QIN, Zhong CHEN, Yanbo YANG, et al. Joint State Equality Constraint Identification and Recursive Filtering Based on Deep Learning[J]. Acta Armamentarii, 2025, 46(6): 240578. DOI： 10.12382/bgxb.2024.0578.

摘要

针对等式约束跟踪系统中多约束并存且当前约束信息不确定下状态估计问题

提出基于深度学习的联合等式状态约束辨识与递推滤波算法。利用门控循环单元构建约束判别网络

借助雷达量测实现当前时刻等式状态约束的在线辨识;在递推滤波框架下基于级联门控循环单元构建增益学习网络

实现概率模型与数据学习联合驱动的目标状态自适应估计;通过滤波投影联合约束判别网络辨识的约束信息和增益学习网络输出的状态估计

获得满足当前时刻等式状态约束的高精度目标状态估计。典型多道路目标跟踪实验结果表明:新算法相比卡尔曼滤波、交互式多模型(基于不同运动模型构建模式集/不同等式状态约束构建模式集)和KalmanNet等算法

在不同量测噪声水平下具有更高的估计精度和更好的鲁棒性。

Abstract

The state estimation problem of equality constraint-based target tracking where multiple constraints coexist and the current constraint information is unknown is presented.A joint state equality constraint identification and recursive filtering based on deep learning algorithm is proposed.The gated recurrent units are used to construct a constraint discriminant network

and the current state constraint is identified online by using the radar measurement.A filtering gain learning network based on cascaded gated recurrent units is built in the framework of recursive filtering to adaptively estimate the target state with the help of joint probabilistic modeling and data learning.The final high-precision filtered estimate which meets the real state equality constraint at current sampling instant is obtained based on filtering projection

which combines the state estimate obtained by the gain learning network with the state equality constraint identified by the constraint discriminant network.Experimental results of multi-target tracking example demonstrate that the proposed algorithm outperforms Kalman filtering

interacting multiple model (based on different motion modes/different state equality constraints) and KalmanNet algorithm in terms of estimation accuracy and robustness

with different levels of measurement noises.

关键词

Keywords

references

韩静茹 , 吴炎烜 , 杨喆 , 等 . 针对机动目标的防空导弹引战配合建模与仿真 [J ] . 兵工学报 , 2017 , 38 ( 增刊1 ): 200 - 204 .

HAN J R , WU Y X , YANG Z , et al . Modeling and simulation of fuze-warhead matching of air-defense missile for mobile target [J ] . Acta Armamentarii , 2017 , 38 ( S1 ): 200 - 204 . (in Chinese)

闫文旭 , 兰华 , 王增福 , 等 . 基于变分贝叶斯的星载雷达非线性滤波 [J ] . 航空学报 , 2020 , 41 ( 增刊2 ): 724395 .

YAN W X , LAN H , WANG Z F , et al . Nonlinear filtering for spaceborne radars based on variational bayes [J ] . Acta Aeronautica et Astronautica Sinica , 2020 , 41 ( S2 ): 724395 . (in Chinese) DOI: 10.7527/S1000-6893.2020.24395 http://doi.org/10.7527/S1000-6893.2020.24395 Spaceborne radars play an important role in early warning defense systems because of their unique advantages such as wide detection range, long distance and all-weather surveillance capability. Due to the high-speed movement of the platform and the strong nonlinear observation function, high-accuracy target tracking for spaceborne radars is difficult. In this paper, we propose a variational Bayes-based nonlinear filtering method, which transforms the nonlinear state estimation problem into an optimization problem. The analytical solution is obtained via a closed-loop iteration manner. Moreover, a pitch angle estimation method is presented using the a priori information of target height. Simulation results show that, compared with the extended Kalman filter, unscented Kalman filter, and the converted measurement Kalman filter, the proposed variational Bayes-based nonlinear filtering method achieves the best estimation accuracy.

YANG Y B , LIU Z G , QIN Y M , et al . Novel pseudo-linear Kalman filtering for 3D angle-only tracking in the presence of observer’s location errors [J ] . Automatica , 2023 , 155 : 111114 .

YANG Y B , LIU Z G , QIN Y M , et al . Iteratively distributed instrumental variable-based pseudo-linear information filter for angle-only tracking [J ] . ISA Transactions , 2023 , 138 : 359 - 372 .

张昀普 , 单甘霖 . 道路约束下多传感器协同地面目标跟踪的管理方法 [J ] . 兵工学报 , 2022 , 43 ( 3 ): 542 - 555 . DOI: 10.12382/bgxb.2021.0122 http://doi.org/10.12382/bgxb.2021.0122 为实现道路约束下地面目标的有效跟踪、控制传感器系统的辐射损失，提出一种多传感器协同管理方法。将传感器管理过程描述为部分可观马尔可夫决策过程，建立道路约束下目标跟踪模型和传感器截获损失模型，给出跟踪精度和截获损失的具体计算方法，并提出一种多普勒盲区下的目标预测状态修正方法；针对高维数下管理方案求取困难的问题，设计了一种莱维飞行- 樽海鞘群算法以快速获得高质量的解。仿真实验结果表明：相比于经典寻优算法，所提算法具有更好的全局搜索能力，能够在缩短寻优时间的同时找到高质量的解；所提管理方法能够有效解决地面目标跟踪问题，既保证了跟踪任务的完成质量，又提高了传感器系统的生存能力。

ZHANG Y P , SHAN G L . Multi-sensor cooperative management for ground target tracking under road constraints [J ] . Acta Armamentarii , 2022 , 43 ( 3 ): 542 - 555 . (in Chinese) DOI: 10.12382/bgxb.2021.0122 http://doi.org/10.12382/bgxb.2021.0122 A multi-sensor cooperative management method is proposed to effectively track the ground target under road constraints and control the radiation loss of the sensor system. The sensor management process is described as a partially observable Markov decision process. A road-constrainted target tracking model and a sensor interception loss model are established, the calculation methods for tracking accuracy and interception loss are presented, and a correction method for target prediction state in Doppler blind zone is proposed. In order to solve the problem that a management scheme is difficultly got when the system state dimension is high, a Levy flight-salp swarm algorithm is designed to obtain a high-quality solution quickly. The simulated results show that the proposed algorithm has better global search capability, and can find high-quality solutions while shortening the optimization time compared with the classic optimization algorithms. The proposed management method can effectively solve the problem of ground target tracking, which not only guarantees the completion quality of the tracking task, but also improves the survivability of the sensor system.

熊光明 , 罗震 , 孙冬 , 等 . 基于红外相机和毫米波雷达融合的烟雾遮挡无人驾驶车辆目标检测与跟踪 [J ] . 兵工学报 , 2024 , 45 ( 3 ): 893 - 906 . DOI: 10.12382/bgxb.2022.0602 http://doi.org/10.12382/bgxb.2022.0602 战场环境下无人驾驶车辆的感知系统易受烟雾、扬尘等天气的影响,对关键目标的检测与跟踪能力大大下降,造成目标漏检、目标误检、目标丢失等严重后果。针对该问题,开发毫米波雷达和红外相机融合系统,采用目标级融合方式建立简洁有效的融合规则,提炼和组合各传感器的优势信息,最终输出稳定的目标感知结果。对毫米波雷达的目标进行有效性检验和提取,并提出改进的基于密度的含噪声空间聚类应用算法,以减少毫米波雷达噪音干扰。以YOLOv4网络为基础,引入MobileNetv2主干网络,在网络训练过程中运用迁移学习方法,同时对红外数据样本进行扩充,解决了红外图像训练样本少的问题。试验结果表明,相较于仅基于红外相机的算法,融合检测算法在烟雾环境下的精度显著提升,且算法实时性高,实现了烟雾环境下毫米波雷达与红外相机融合的目标检测与跟踪,提高了无人驾驶车辆目标检测与跟踪系统的抗烟雾干扰能力。

XIONG G M , LUO Z , SUN D , et al . Object detection and tracking for unmanned vehicles based on fusion of infrared camera and MMW radar in smoke-obscured environment [J ] . Acta Armamentarii , 2024 , 45 ( 3 ): 893 - 906 . (in Chinese) DOI: 10.12382/bgxb.2022.0602 http://doi.org/10.12382/bgxb.2022.0602 In the battlefield environment, the perception system of unmanned vehicle is susceptible to the influence of weather such as smoke and dust. The ability to detect and track key objects is greatly reduced under harsh weather conditions, resulting in serious consequences, such as object miss-detection, object misdetection and object missing. To address this problem, a fusion system of MMW radar and infrared camera is developed. The object-level fusion method is adopted to establish simple and effective fusion rules, extract and combine the dominant information from each sensor, and finally output stable objective perception results. The objects of MMW radar are checked and extracted. And an improved DBSCAN clustering algorithm is proposed to reduce the noise of MMW radar. The MobileNetv2 backbone network is introduced based on the YOLOv4 network. In the process of network training, the transfer learning method is used to expand the infrared data samples, which solves the problem of fewer training samples of infrared images. The experimental results show that the fusion algorithm has significantly better accuracy and high real-time performance in the smoke environment compared with the algorithm based on infrared camera only, which realizes the object detection and tracking of the fusion of MMW radar and infrared camera in the smoke environment, and improves the anti-interference ability of the object detection and tracking system of unmanned vehicles.

何友 , 修建娟 , 刘瑜 , 等 . 雷达数据处理及应用 [M ] . 第4版 . 北京 : 电子工业出版社 , 2022 : 402 - 404 .

HE Y , XIU J J , LIU Y , et al . Radar data processing with application [M ] . 4th ed. Beijing : Publishing House of Electronics Industry , 2022 : 402 - 404 . (in Chinese)

YANG Y B , LIU Z G , QIN Y M , et al . Maximum correntropy two-filter smoothing for nonlinear systems with non-Gaussian noises [J ] . EEE Transactions on Systems,Man,and Cybernetics:Systems , 2024 , 54 : 3796 - 3808 .

杨家铭 , 潘悦 , 王强 , 等 . 水下弱目标跟踪的深度学习方法研究 [J ] . 兵工学报 , 2024 , 45 ( 2 ): 385 - 394 . DOI: 10.12382/bgxb.2022.0842 http://doi.org/10.12382/bgxb.2022.0842 针对水下弱目标跟踪受干扰和噪声影响容易出现量测丢失或偏差,导致传统Kalman滤波方法跟踪误差显著增加甚至出现发散的问题,为此提出一种基于神经网络的目标跟踪方法,利用深度神经网络解决不同运动模式下目标方位跟踪的问题。水下目标跟踪的神经网路模型可通过运动模型生成大量量测数据进行充分训练,有效解决水声目标数据少、标记样本不足的问题;在量测不连续条件下,提出一种新的损失函数用于增强目标跟踪模型的稳健性;对未学习的仿真数据及实测海试数据进行测试。研究结果表明:构建的卷积神经网络(Convolutional Neural Network, CNN)模型适用于3种不同运动模式下的目标,能在平台静止和运动两种情况下稳定跟踪目标;CNN模型较传统Kalman滤波方法跟踪误差分别降低了7.75°和1.41°,验证了该模型的稳健性和可推广性。

YANG J M , PAN Y , WANG Q , et al . Research on deep learning method for underwater weak target tracking [J ] . Acta Armamentarii , 2024 , 45 ( 2 ): 385 - 394 . (in Chinese)

JIANG W , REN Y H , LIU Y , et al . Artificial neural networks and deep learning techniques applied to radar target detection:a review [J ] . Electronics , 2022 , 11 ( 1 ): 156 .

段艳杰 , 吕宜生 , 张杰 , 等 . 深度学习在控制领域的研究现状与展望 [J ] . 自动化学报 , 2016 , 42 ( 5 ): 643 - 654 .

DUAN Y J , LÜ Y S , ZHANG J , et al . Deep learning for control:the state of the art and prospects [J ] . Acta Automatica Sinica , 2016 , 42 ( 5 ): 643 - 654 . (in Chinese)

CUI Y Q , HE Y , TANG T T , et al . A new target tracking filter based on deep learning [J ] . Chinese Journal of Aeronautics , 2022 , 35 ( 5 ): 11 - 24 .

熊伟 , 朱洪峰 , 崔亚奇 . 在线学习的循环自适应机动目标跟踪算法 [J ] . 航空学报 , 2022 , 43 ( 5 ): 444 - 456 .

XIONG W , ZHU H F , CUI Y Q . Recurrent adaptive maneuvering target tracking algorithm based on online learning [J ] . Acta Aeronautica et Astronautica Sinica , 2022 , 43 ( 5 ): 444 - 456 . (in Chinese)

GAO C , LIU H W , ZHOU S H , et al . Maneuvering target tracking with recurrent neural networks for radar application [C ] // Proceedings of the 2018 International Conference on Radar.Brisbane,Australia:IEEE , 2018 : 1 - 5 .

JOUABER S , BONNABEL S , VWLASCO-FORERO S , et al . NNAKF:a neural network adapted Kalman filter for target tracking [C ] // Proceedings of the 2021 IEEE International Conference on Acoustics,Speech and Signal Processing.Toronto,Canada:IEEE , 2021 : 4075 - 4079 .

FORTI N , MILLEFIORI L M , BRACA P , et al . Model-based deep learning for maneuvering target tracking [C ] // Proceedings of the 2023 26th International Conference on Information Fusion. Charleston,SC,US:IEEE , 2023 : 1 - 6 .

GAO C , YAN J K , ZHOU S H , et al . Long short-term memory-based deep recurrent neural networks for target tracking [J ] . Information Sciences , 2019 , 502 : 279 - 296 .

LIU J X , WANG Z L , XU M . DeepMTT:a deep learning maneuvering target-tracking algorithm based on bidirectional LSTM network [J ] . Information Fusion , 2020 , 53 : 289 - 304 .

LIU Z G , WANG Z K , YANG Y B , et al . A data-driven maneuvering target tracking method aided with partial models [J ] . IEEE Transactions on Vehicular Technology , 2024 , 73 : 414 - 425 .

REVACH G , SHLEZINGER N , NI X Y , et al . KalmanNet:neural network aided Kalman filtering for partially known dynamics [J ] . IEEE Transactions on Signal Processing , 2022 , 70 : 1532 - 1547 .

REVACH G , NI X Y , SHLEZINGER N , et al . RTSNet:learning to smooth in partially known state-space models [J ] . IEEE Transactions on Signal Processing , 2023 , 71 : 4441 - 4456 .

BUCHNIK I , REVACH G , STEGER D , et al . Latent-KalmanNet:learned Kalman filtering for tracking from high-dimensional signals [J ] . IEEE Transactions on Signal Processing , 2023 , 72 : 352 - 367 .

ZHANG Y S , LI G , ZHANG X P , et al . Transformer-based tracking network for maneuvering targets [C ] // Proceedings of the 2023 IEEE International Conference on Acoustics,Speech and Signal Processing.Rhodes Island,Greece:IEEE , 2023 : 1 - 5 .

ZHANG Y S , LI G , ZHANG X P , et al . A deep learning model based on transformer structure for radar tracking of maneuvering targets [J ] . Information Fusion , 2024 , 103 : 102120 .

XU L F , LIANG Y , DUAN Z S , et al . Route-based dynamics modeling and tracking with application to air traffic surveillance [J ] . IEEE Transactions on Intelligent Transportation Systems , 2019 , 21 ( 1 ): 209 - 221 .

XU L F , LI X R , LIANG Y , et al . Modeling and state estimation of linear destination-constrained dynamic systems [J ] . IEEE Transactions on Signal Processing , 2022 , 70 : 2374 - 2387 .

HAO X H , LIANG Y , XU L F , et al . Mode separability-based state estimation for uncertain constrained dynamic systems [J ] . Automatica , 2020 , 115 : 108905 .

XU L F , LI X R , DUAN Z S , et al . Modeling and state estimation for dynamic systems with linear equality constraints [J ] . IEEE Transactions on Signal Processing , 2013 , 61 ( 11 ): 2927 - 2939 .

SIMON D . Kalman filtering with state constraints:a survey of linear and nonlinear algorithms [J ] . IET Control Theory & Applications , 2010 , 4 ( 8 ): 1303 - 1318 .

SIMON D , CHIA T L . Kalman filtering with state equality constraints [J ] . IEEE Transactions on Aerospace and Electronic Systems , 2002 , 38 ( 1 ): 128 - 136 .

ZHANG C , FU J . Multi-objective dynamic optimization of path-constrained switched systems [J ] . Automatica , 2024 , 159 : 111326 .

FU J , LI H . Dynamic optimization of state-dependent switched systems with free switching sequences [J ] . Automatica , 2023 , 148 : 110747 .

FU Q , LU K L , SUN C Y . Deep learning aided state estimation for guarded semi-Markov switching systems with soft constraints [J ] . IEEE Transactions on Signal Processing , 2023 , 71 : 3100 - 3116 .

FU J , ZHANG C . Optimal control of path-constrained switched systems with guaranteed feasibility [J ] . IEEE Transactions on Automatic Control , 2021 , 67 ( 3 ): 1342 - 1355 .

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