## 1 Introduction
For further improvement, most studies are focused on improving particle resampling, usually based on traditional resampling mechanisms, such as hierarchical resampling [30], adaptive resampling [31; 32; 33], deterministic resampling [34; 35; 36], etc. Another way is to increase the diversity of samples by introducing intelligence optimization ideas, such as a genetic algorithm [37; 38], firefly algorithm [39], bat algorithm [40] and so on. The above methods could relieve the particle degeneracy to some extent, whereas if they are applied to three-dimensional maneuvering target tracking, the particle distribution would be sparse and hard to cover uniformly, due to an inconsistent maneuvering mode and intensity in different directions, which would intensify particle degeneracy and reduce the diversity of samples. Noting that the target motion could be decomposed into three mutually perpendicular directions in three-dimensional space, the maneuvering form and noises of the target in each direction do not interfere with each other. If these three directions are regarded as orthogonal independent subspaces, the reduction in dimensionality of the maneuver could be considered. The partitioned sampling (PS) method proposed by [PERSON] [41] increases the diversity of samples by some decomposition of system dynamics to reduce the sample size required for target tracking when the state space dimension is large [42]. It is mainly used for multi-feature visual target tracking, and is rarely used in other fields. Besides, the premise of using partitioned sampling is that the state space can be decomposed into several independent subspaces, but when there are correlative noises that are difficult to decompose in the system, this method cannot be used directly. Some decoupling methods are needed to remove the correlations in advance [3]. Another improved way that could effectively reduce the redundancy of particles is to adaptively adjust our sample size to avoid computational burden and poor real-time performance caused by fixed sample size. The adaptive particle filter with KL-sampling proposed by [PERSON] [43] limits the estimation error through adaptive changes of sample size, but it may bring high computational cost [44; 45]. [PERSON] et al. [46] proposed a particle filter based on an adaptive adjustment of likelihood distribution, whereas the weight variance of particles has a great influence on the sample size [47]. In Section 3, we will introduce an adaptive strategy based on the relationship between state variances and sample size, which is derived by introducing theories in Probability & Statistics. Based on the CS-jerk model, this paper proposes a three-dimensional high-maneuvering target tracking method, by combining the partitioned sampling and the adaptive strategy with UPF. During the tracking process, the target change motion is matched by an adaptive modification of the CS-jerk model, which involves the estimation of the motion state and the correction of the statistical characteristics of maneuver noises, and the UPF is chosen as the basic filtering algorithm to implement this modification. Then, by using adaptive strategy, particles in the UPF are adaptively adjusted to the appropriate amount according to the state variance of target motion for each time step, thereby maintaining an effective sample size and real-time tracking. Furthermore, according to the dynamic decomposability of the target motion, partitioned sampling is used to decompose the motion space so that particles could be independently sampled in each subspace, which reduces the computational complexity and increases the sample diversity, further mitigating the particle degeneracy. Finally, the proposed algorithm is compared to other classic tracking algorithms by experiments. The results verify that the proposed algorithm outperforms other algorithms, in terms of accuracy, and running time, with significant advantages in tracking performance.