## 2 Related Works
Tracking information (including location and other tracking information) of multiple targets measured by separate nodes was fused for the practically more relevant case of unknown correlations between nodes. The fusion method was implemented on only two agents or sensors; one fusion weight was \(w\) and the other was \(1-w\). A sequential GCI fusion means was proposed in [32] after applying a GGIW-CPHD filter to each sensor, which also assumed that there were two agents or sensors to be fused. A pairwise means was adopted if there were more than two sensors; hence, if there were more than two sensors, every two sensors were coupled to one pairwise function to fuse the tracking information using the fusion means of [19]. This pairwise fusion could substantially decrease the computational burden compared to the batch fusion, as well as bring about some problems. One is that it needed to establish location correlations before coupling these sensors, which consumed much energy for the uncertainty location of mobile sensors. The other is that one residual sensor could not be coupled with any other sensor after coupling with its corresponding sensor. As described above, a Bayesian recursion adopted PMBM filter can largely improve the tracking behaviors by eliminating the clutter or noise originating from measurements, but there remain several challenging tasks. Firstly, a standard PMBM filter assumes that the birth density is a priori known density or homogenous density [19], while new targets can birth or die at arbitrary positions or at random process period in real-world application scenarios, leading to PMBM becoming inapplicable. Secondly, the PMBM filter assumes that target detection probability is a priori known, while detection probability can arise with uncertain spatial and uncertain temporal distribution, leading to PMBM becoming incomplete and inaccurate. Thirdly, many measurements detected by a mass of sensors can bring about a sharp increase in computation and processing burden, which is a pending problem for sensor networks. Lastly, tracking initialization is not included in most PMBM filters, leading to tracking error and increased energy consumption in tracking, or leading to increased cumulative tracking errors in subsequent steps. In order to solve these existing problems of the standard PMBM filter, an improved multitarget tracking method based on a GCI-IGGM-PMBM filter is proposed in this work for our aquaculture tracking sensor network. Firstly, a GAM distribution is introduced to present the augmented state of unknown and changing target detection probability. Secondly, the intensity of newborn targets is adaptively derived presented using the IG distribution based on this augmented state. Then, the measurement likelihood is presented as a gamma distribution for the augmented state. On these bases, the detailed recursion and closed-form solutions to the proposed filter are 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, whose target states are measured by multiple sensors according to their respective fusion weights, is applied to a large-scale aquaculture tracking network. Lastly, the target states are initialized by adopting the hidden terminal couple (HTC) scheme [33] in the proposed system. Furthermore, comprehensive experiments are presented to verify the effectiveness of the GCI-IGGM-PMBM tracking method. Moreover, comparisons with other multitarget tracking schemes also demonstrate that the tracking behavior is largely improved; in particular, the tracking energy consumption is reduced sharply, and the tracking accuracy is relatively enhanced.