## 6 Conclusions and Future Work  
The proposed GCI-IGGM-PMBM filter in this work is aimed at point target tracking, in which multiple targets (e.g., crabs) can be considered as small independent objects or smart points without any spatial extent. The target states and cardinality of multitargets were estimated using the IGGM-PMBM filter. Firstly, the GAM distribution was introduced to present the augmented state of unknown and changing target detection probabilities. Secondly, the intensity of newborn targets was adaptively derived using an inverse gamma (IG) distribution on the basis of this augmented state. On these bases, the detailed recursion and closed-form solutions to the proposed filter were derived by means of approximating the intensity of target birth and potential targets to an IGGM form and the density of existing Bernoulli components to a single IGGM form. Moreover, the associated distributed fusion strategy GCI was applied to a large-scale aquaculture tracking network. Comprehensive experiments were presented to verify the effectiveness of the GCI-IGGM-PMBM tracking method. Comparisons with other multitarget tracking schemes also demonstrated that the tracking behaviors were largely improved; in particular, the tracking energy consumption was reduced sharply, and the tracking accuracy was relatively enhanced.  
Figure 10: Performance comparisons of GCI–IGGM–PMBM and BGGIW–PMBM with newborn distribution: (**a**) GOSPA; (**b**) LE; (**c**) ME; (**d**) FE.  
In our future work, the tracking scheme will take into account not only the positions of objects as in this work, but also the appearance or spatial extent of objects [6]. In addition to crabs, extracting the shape of other farming objects in the farming environment is an important task in tracking multiple targets, which is nearly impossible using point tracking. We will devote ourselves to studying an efficient tracking scheme to resolve this problem, taking various underwater factors into account for multitarget tracking [5]. Moreover, developing smart aquaculture is a general trend for the future, and smart aquaculture can be combined with smart agriculture to form a large agriculture IoT system [37], with many underwater smart devices such as trackers, monitors, or controllers.  
Conceptualization, C.L. and J.Z.; methodology, N.X.; software, C.L.; validation, C.L. and J.Z.; formal analysis, C.L. and J.Z.; investigation, Z.T.; resources, Z.T.; data curation, C.L.; writing--original draft preparation, C.L.; writing--review and editing, J.Z.; visualization, C.L.; supervision, N.X.; project administration, Z.T.; funding acquisition, J.Z. All authors read and agreed to the published version of the manuscript.  
This research work was supported by the National Natural Science Foundation of China (No. 61362017 and No. 61365007).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
This research work was supported by the Starting Foundation of Shanghai Ocean University (No. A2-0203-00-100344 and No. A2-0203-00-100343). This research work was partly supported by the National Natural Science Foundation of China (No. 61362017 and No. 61365007).  
The authors declare no conflict of interest.