The current trend to develop low cost, miniature unattended ground sensor (UGS) will enable a cost-effective, covert means for surveillance in both urban and remote border areas. Whereas the functionality (e.g., sensing range and life in the field) of these individual UGS (i.e., acoustic, seismic, magnetic, chemical or biological) are limited due to size and cost constraints, a network of these sensors working cooperatively together can provide an effective surveillance capability. A key factor is the ability of these sensors to work cooperatively to achieve a `collective' functionality that can meet the surveillance objective. For example, a realistic mission objective would be to use the minimum number of sensors necessary (i.e., preserve the life of the network) to detect, identify and track vehicles in a desert canyon area that has variable wind and temperature conditions. The network would have to assess the effect of the wind direction and temperature on the sensing range of its acoustic sensors, turn on those sensors that can initially detect the target and dynamically activate other appropriate sensors (e.g., seismic, acoustic or imaging sensors) that can identify and track the vehicle as it moves into and across the canyon area covered by the sensor network. To achieve this type of functionality requires system algorithms that are capable of optimizing the utilization of the sensors. This paper describes results that show improved target tracking accuracy by optimizing the selection of acoustic sensors that measure bearing angles to the target. Also, recent results are described from testing the tracking algorithm with real data.