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摘要:
机动目标的高精度协同定位是协同打击的关键，通过分布式弹群来实现协同定位是目前研究的热点。提出了一种分布式弹群在线协同定位的策略，解决弹群通信受限条件下协同定位的实时性问题；针对目标状态估计中模型非线性、噪声非高斯分布等特点，提出了容积卡尔曼粒子滤波算法；设计了自适应转弯速率的匀速转弯模型，并将现有的二维匀速转弯模型扩展至三维，解决了现有匀速转弯模型先验转弯速率固定，导致定位精度不高的问题；设计了自适应模型转移概率的交互多模型方法，实时修正马尔可夫转移概率，解决了单模型滤波定位精度不高的问题。通过仿真验证了所提策略和方法的有效性和准确性。
Abstract:High-precision co-location of maneuvering targets is the key to coordinated strikes, and co-location through distributed bomb swarms is a current research hotspot. This paper proposes a distributed online co-location strategy for swarms to solve the real-time problem of co-location under the condition of limited swarm communication. Aiming at the characteristics of model nonlinearity and non-Gaussian distribution of noise in target state estimation, a volumetric Kalman is proposed. Particle filter algorithm, a constant-speed turning model (constant turn, CT) with adaptive turning rate is designed, and the existing 2D CT model is extended to 3D, which solves the problem of inconsistency in positioning accuracy caused by the fixed turning rate of the existing CT model. An interactive multi-model method of adaptive model transition probability is designed, and the Markov transition probability is corrected in real time, which solves the problem of low positioning accuracy of single-model filtering. The effectiveness and accuracy of the method proposed in this paper are verified by simulation.
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图 4 非全连通网络弹群信息流向示意图
Figure 4. Schematic diagram of information flow of non-fully connected network
图 12 改进IMM算法轨迹误差对比
Figure 12. Comparison of trajectory errors of improved IMM algorithm
图 13 改进IMM算法定位效果局部比较
Figure 13. Local comparison of positioning effect of improved IMM algorithm
表 1 弹群协同定位仿真参数
Table 1. Missile swarm simulation parameters
仿真参数 数组/范围 目标初始状态$ {{\boldsymbol{X}}_T} $ $ {\left[ {\begin{array}{*{20}{c}} 0&0&0&0&0&0&0&0&0 \end{array}} \right]^{\mathrm{T}}} $ 弹群节点数 5 导弹初始位置范围/km $ - 20 < x_m^i,y_m^i,{\textit{z}}_m^i < 20 $ 采样步长/s 1 
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表 2 仿真结果
Table 2. Simulation result table
横向对比约束 算法 平均RMSE/m 平均运行时间/ms 相同运行时间 PF 902.86 6.13 CPF 310.81 5.43 CKF 387.52 0.32 相同定位精度 PF 412.65 48.70 CPF 394.56 3.96 CKF 387.52 0.32
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表 3 IMM算法定位性能对比
Table 3. Comparison of positioning performance of IMM algorithms
算法 平均RMSE/m 平均运行时间/s 标准IMM 133.05 21.78 改进IMM 99.78 22.05
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