## 2 Related Works
A standard PMBM or PHD (CPHD) filter assumes that the target birth intensity is known a priori, but newborn targets (including spontaneous newborns and spawned newborns) may be randomly located in the coverage range with adaptive newborn density in actual applications, which leads to primitive missed detection and inaccurate estimation. An improved GM-CPHD filter was developed in [18] to estimate target cardinality distribution of the time-varying newborns at each processing step adopting a discrete kernel estimator in conjunction with an exponential weighted moving average scheme. Target birth intensity could be updated according to the outputting estimated birth cardinality distribution, and predicted birth intensity and cardinality distribution could be adopted by a tracker based on GM-CPHD to adjust its filtering strength for target tracking. Novel extensions were derived to distinguish between the persistent and the newborn targets in both the prediction and the update step in [19], allowing the PHD/CPHD filter to adapt the target birth intensity at each processing step using the received measurements. This measurement-driven birth intensity is practical because it removes the need for the prior specification of birth intensities and eliminates the restriction of target appearance volumes within state space. As described in [16], BGGIW was adopted to approximate target birth intensity and potential target intensity. The target detection probability depends on the sensor, target, environment, and features used for detection. A priori knowledge of target detection probability is of critical importance in a PMBM or PHD (CPHD) filter, while the detection probability is always time-varying and space-varying. Target amplitude and SNR information were exploited to present the detection probability in [20], and the IGGM distribution was used to implement the PHD and CPHD filters to address the limitation of nonnegativity of the target amplitude and SNR and non-Gaussian noise. A variational Bayesian approximation method was brought into the trajectory PHD (VB-TPHD) filter to obtain measurement noise covariance adaptively in [21]. The random matrix, following the IW distribution, was introduced to model the unknown measurement covariance in this filter. The VB-TPHD filter minimized the KLD and estimated the series of states for multiple targets, taking noise covariance matrices into account simultaneously. An R-PMBM recursion that estimates the unknown detection probability was proposed in [4], in which the state of each object was coupled with a variable representing the detection probability such that the standard PMBM filtering process evolved into an R-PMBM filter which could jointly estimate the state of objects and detection probability. A beta function was proposed to represent the detection probability for a computationally feasible implementation of the R-PMBM filter. The beta function was also adopted to describe the unknown detection probability in [16]. Other tracking methods exist, such as energy-based autoregressive neural network [22; 23], deep learning method [24; 25; 26], visual method [27; 28], and genetic algorithm [29]. A novel transfer learning algorithm with an SMC-PHD filter and GM-PHD filter was proposed in [30] to automatically adapt the YOLO network with unlabeled target sequences. The frames of the unlabeled target sequences were automatically labeled. The detection probability and clutter density of the SMC-PHD filter and GM-PHD were applied to retrain the YOLO network for occluded targets and clutter. A novel likelihood density with the confidence probability of the YOLO detector and visual context indications was implemented to choose target samples. A simple resampling strategy was proposed for SMC-PHD YOLO to address the weight degeneracy problem. A novel consensus CPHD filter based on GM implementation was presented for distributed MTT over a sensor network [31]. A fusion robust fusion method was proposed to perform fusion via fully distributed means.