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This paper presents results from experiments, mathematical analysis, and simulations of a network of static and mobile sensors for detecting threats on city streets and in open areas such as parks. The paper focuses on the detection of nuclear radiation threats and shows how the analysis can be extended to other classes of threat. The paper evaluates algorithms that integrate methods of parametric and Bayesian statistics. A pure Bayesian approach is difficult because obtaining prior distributions on the large number of parameters is challenging. The results of analyses and simulations are compared against measurements made on a reduced scale testbed. A survey of background radiation in the city of Sacramento is used to quantify the efficacy of police patrols to detect threats. The paper also presents algorithms that optimize network parameters such as sensor placement.