## 1 Introduction  
Historically, wireless surveillance systems have used infrared, acoustic, seismic and magnetic signals for passive sensing, and optics and ultrasonic signals for active sensing. Radar has been conspicuously absent from wireless surveillance systems [1], particularly because radar systems are conventionally bulky and have high energy consumption. With the advent of the micro-power pulse radar in the 90's [2], low power radar becomes a possibility. Subsequently, technical progress in sensor networks has prompted efforts to integrate radar as one of the sensor modalities to autonomous sensor network platforms [1, 3]. An autonomous sensor network is a collection of sensor nodes with limited processing, power, and communication capabilities that monitor a real world environment through differing modalities. The nodes gather information about the local environment, preprocess the data, and transmit the output via wireless channels to a base station. The base station may broadcast commands to all or some of the sensor nodes in the network. Using intelligent framework design, the network can support decision-making and provide the capability to detect, track, and identify targets over a wide area. Recent research developments include designing systems using Mica2 mote and 2.4 GHz TWR-ISM-002 radar sensor from Advantaca [4]. The integration includes a custom designed power board that provides power required for the radar, onboard antenna, and an optional Mica sensor board. BumbleBee is another integrated radar mote sensor developed by the Samraksh Company [3], which includes a low-power Pulsed Doppler Radar (PDR) and a TelosB mote [5]. The key features of BumbleBee include: detection range between 1 m to 10 m that is controllable via software, on-board internal antenna, 60 degree conical coverage pattern, and detection of radial velocities up to 2.6 m/s. The radars used in the above-mentioned platform offer the detection of movement and the velocity of a moving target. BumbleBee has the capability to compute range with additional post-processing processing methods; however, it is not measured simultaneously with velocity. It is desirable to have low-cost radar-mote that measures the range and velocity simultaneously with simple signal processing techniques, and having more functionality, such as target classification and tracking. To achieve this goal, we introduce a sensor system consisting of a variety of sensor motes with radar, light, acoustic, and vibration sensors, which can be used for autonomous wireless surveillance and sensing. Compared with previous work on wireless radar motes [1, 3], our multi-modal sensor system not only detects targets, but also produces accurate range and velocity estimates of the targets simultaneously with simple signal processing techniques. Moreover, our system can use various complementary sensors to classify targets based on such characteristics as shape, material, and weight [6]. In addition, our radar can produce continuous estimates of range and velocity, thus enabling us to track target movements. Each sensor modality has its advantages and disadvantages. Acoustic sensors can be used to distinguish objects with different sound signatures, e.g., acoustic sensor systems have been used to classify vehicles. To a certain extent, acoustic sensors can also provide information related to direction of motion, speed and location. However, they are highly susceptible to background noise and wind conditions. Vibration sensors can distinguish objects with different weights and speeds, but these sensors are also susceptible to environmental disturbances. Radars can distinguish different materials and shapes, while providing estimations of speed and location. However, radar cannot differentiatebetween similar objects with different weights or acoustic signatures. By combining the data from acoustic, vibration and radar sensors, we can increase the accuracy of speed and location estimation.