When a sensor can resolve the members in a cluster, it is difficult to accurately track each target due to cooperative interaction among the targets. In this paper, we research the tracking problem of resolvable cluster targets with cooperative interaction. Firstly, we use the stochastic differential equation to model the cluster coordination rules, and the state equation of the single target in the cluster is derived. On this basis, a Bayes recursive filter tracking method based on the combination of the DBSCAN clustering algorithm and the $\delta $ -GLMB filter is proposed. In the $\delta $ -GLMB filter prediction stage, the DBSCAN algorithm is used to determine the cluster where the target is located in real time. Then, the collaborative noise of the target is estimated, which will be used as the input to correct the prediction state of the target. The simulation and experiment results demonstrate the effectiveness of the proposed algorithm when the cluster is splitting, merging, and in reorganization.

Sensor deployment is one of the major concerns in multisensor networks. This paper proposes a sensor deployment approach using improved virtual force algorithm based on area intensity for multisensor networks to realize the optimal deployment of multisensor and obtain better coverage effect. Due to the real‐time sensor detection model, the algorithm uses the intensity of sensor area to select the optimal deployment distance. In order to verify the effectiveness of this algorithm to improve coverage quality, VFA and PSOA are selected for comparative analysis. The simulation results show that the algorithm can achieve global coverage optimization better and improve the performance of virtual force algorithm. It avoids the unstable coverage caused by the large amount of computation, slow convergence speed, and easily falling into local optimum, which provides a new idea for multisensor deployment.

Multisensor scheduling, a multi-objective optimization problem, simultaneously aims to find the optimal matching between sensors and targets in a certain period, that is, to generate the optimal multisensor scheduling scheme. Several sensor scheduling literatures proposed multisensor scheduling schemes incorporating various algorithms, but the majority of them dealt with sensors to targets in two dimensions. First, the visual relationship between sensors and UAV cluster targets is investigated, and a three-dimensional correspondence between sensors, targets, and time is designed while dividing the scheduling time. Secondly, the detection advantage of sensors to UAV cluster, sensor load, and sensor handover numbers in the scheduling time interval are summed to create multisensor scheduling objective functions for UAV cluster. Furthermore, for the UAV cluster optimization problem, a hybrid optimization algorithm is proposed, which integrates the golden sine mechanism and adaptive weight factor in Harris hawks optimization algorithm. Finally, extensive experimental and statistical analyses show that the models and algorithms proposed in this paper outperform the existing ones. It has better convergence, does not need additional computational cost, and generates better multisensor scheduling schemes.

This paper offers a new tracking algorithm for clustered targets of focal plane unmanned aerial vehicles (UAVs) by integrating the random hypersurface model (RHM) model into the δ-generalized label multi-Bernoulli (δ-GLMB) filter to guarantee a robust UAV cluster target tracking on such focal planes. We first investigated the infrared imaging features of UAV cluster targets by emphasizing the target imaging size and dispersion. Next, we designed an elliptical RHM-based measurement model to map the measurement, state parameters, scaling factors, and errors to pseudo-measurement "0", followed by establishing a pseudo-measurement equation to reflect the target’s extended shape size. Then, in order to enhance measurement estimation accuracy, the RHM model was implemented with the δ-GLMB filter and Gamma-Gaussian mixture, which can perform a real-time estimate of the targets’ centroid motion state and extended state. We also used the grid-based fast density-based spatial clustering of applications with noise (DBSCAN) segmentation algorithm to overcome the distance-based segmentation method restrictions for infrared radiation (IR) measurement data and diminish the algorithm’s complexity even more in the measurement update. Extensive simulations demonstrated that this algorithm outperformed existing matching filtering algorithms in the target centroid motion and extended states. Our investigations also revealed that the algorithm was less vulnerable to clutter and more adaptive, making it more straightforward to accomplish reliable tracking of UAV cluster targets.

In target tracking, multi-target tracking is the focus of research. It is primarily concerned with the issue of collaboratively estimating the number, state, or trajectory of targets based on sensor measurements in the presence of data association uncertainty, detection uncertainty, false observation, and noise. The current research hotspot in multi-target tracking is nonlinear multi-target tracking based on the probability hypothesis density algorithm. Furthermore, there are several considerations with this algorithm’s multi-target tracking, such as low estimate accuracy, filter divergence, and poor real-time performance. Based on equivalent probability sampling and the embedded volume criterion, this article demonstrates an equal probability embedded cubature particle probability hypothesis density filter algorithm. In the sampling stage, the algorithm implements the equal probability sampling method, divides the entire sampling area into several areas with equal probability, extracts particles from each region using the established criteria, generates limited integral points using the third-order embedded volume criterion, filters each sampling particle, fits the important density function, and predicts and updates the probability hypothesis density of multi-target state. The simulation results demonstrate that the equal probability sampling strategy outperforms other multi-target position and number estimation methods. Simultaneously, it demonstrates that the equal probability embedded cubature particle probability hypothesis density filter algorithm can effectively track multiple targets. The equal probability embedded cubature particle probability hypothesis density filter algorithm performs better in real-time and has a more accurate target number and state estimate than other algorithms.