This article proposes a joint estimate approach in terms of target states and target cardinality as well as detection probability based on the Poisson multi-Bernoulli (PMB) filter. For the situations, where the detection probability is unknown or unreliable, the usual tracking methods often fail to work, so online estimation of the detection probability is indispensable.To depict the unknown detection probability, an inverse gamma Gaussian mixture (IGGM) implementation is adopted to propagate nonnegative features, including signal amplitude and signal-to-noise ratio (SNR). The IGGM-based PMB filter, abbreviated as IGGM-PMB, is proposed to solve the multitarget tracking (MTT) along with the unknown and time-varying detection profile. Specifically, the Poisson random finite set (RFS) intensity is approximated as an IGGM, while the density of Bernoulli RFS is approximated as a single inverse gamma Gaussian component (IGGC). Then, the proposed IGGM-PMB filter is applied to distributed multisensor fusion, wherein the estimated detection probabilities are used as a choice of the fusion ordering. Simulation results demonstrate the effectiveness and superiority of the proposed approach via comparisons with state-of-the-art approaches.