Cooperative integrated sensing and communication (ISAC) can support shared situational awareness for unmanned aerial vehicles (UAVs), road vehicles, and industrial mobile robots such as automated guided vehicles (AGVs). Yet multipath can make nodes agree on a virtual target while useful weak echoes remain unexploited. This paper develops cooperative fusion that separates association confirmation, state refinement, and residual recovery. A cellular reference signal model accommodates arbitrary node counts, three-dimensional motion, monostatic and bistatic links, nuisance parameters, and correlated errors. We characterize report distinguishability under bounded bias and shared-reflector ambiguities that survive additional nodes. Minimax estimation yields directional weak-report refinement that preserves confirmed associations and limits bias-sensitive innovations; acquisition accounts for request and upload costs. Controlled experiments accompany UAV evaluations under a Third Generation Partnership Project (3GPP) stochastic channel model and a ray tracing environment representing San Francisco. In the latter environment, matched-budget fusion raises detection from observable site selection's 88.75% to 92.46% and reduces the false-output fraction from 11.06% to 0.40%. Against the strongest full-band fixed site, the 90th-percentile position error on commonly detected targets falls from 8.54 to 3.50 m. Association controls, local rejection ablations, and refinement studies distinguish the sources of these gains.

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