1:00, SAM-L1.1
SENSOR ARRAY CALIBRATION VIA TRACKING WITH THE EXTENDED KALMAN FILTER
V. CEVHER, J. MCCLELLAN
Starting with a randomly distributed sensor array with unknown sensor orientations, array calibration is needed before target localization and tracking can be performed using the classical triangulation methods. In this work, we assume that the sensors are only capable of accurate direction of arrival (DOA) estimation. The calibration problem cannot be completely solved given the DOA estimates alone, since the problem is not only rotationally symmetric but also includes a range ambiguity. Our approach to calibration is based on tracking a single target moving at a constant velocity. In this case, the sensor array can be calibrated from target tracks generated by an extended Kalman filter (EKF) at each sensor. A simple algorithm based on geometrical matching of similar triangles will align the tracks and result in the sensor positions and orientations relative to a reference sensor. Computer simulations show that the algorithm performs well even with noisy DOA estimates at the sensors.

1:20, SAM-L1.2
ON THE IMPLEMENTATION OF PARTICLE FILTERS FOR DOA TRACKING
J. REILLY, J. LAROCQUE, W. NG
This paper addresses practical issues for the implementation of sequential Monte Carlo sampling schemes, also known as particle filtering, for application to tracking problems. The discussion focusses on ways to improve on previous resampling schemes, resulting in significantly improved performance. These conclusions are demonstrated and supported by examples of application of the particle filter to a sequential tracking of a known number of directions of arrival.

1:40, SAM-L1.3
CLUTTER ADAPTIVE MULTIFRAME DETECTION/TRACKING OF RANDOM SIGNATURE TARGETS
M. BRUNO, J. MOURA
This paper develops the two-dimensional (2D) clutter adaptive, multiframe Bayes detector/tracker for targets with random signature. We model the background clutter and the target signature as samples of two independent, spatially correlated, 2D noncausal Gauss-Markov random fields (GMrfs). The target's motion is modeled by a 2D hidden Markov model (HMM). We study, through Monte Carlo simulations, the performance of the adaptive multiframe detector/tracker, and show that the performance of adaptive tracker is very close to the performance of the tracker when the clutter model is perfectly known.

2:00, SAM-L1.4
TRACKING OF MOBILE PHONE USING IMM IN CDMA ENVIRONMENT
J. LEE, H. KO
This paper proposes an effective method to localize mobile phones in CDMA environment. This is to remedy the performance limitation inherent in the traditional localization algorithms, which make use of the present information only. If a Kalman filter is used that includes the previous information of location of a mobile unit, then the location error can be significantly reduced. Since the actual movement of a user is difficult to be represented by only one motion model, it will show better error performance if an interacting multiple model (IMM) that uses several Kalman filter models is applied in place of just one Kalman filter. Performance analysis of the location error between Kalman filter and IMM implementation confirm our postulation that IMM significantly reduces location error.

2:20, SAM-L1.5
STOCHASTIC OBSERVABILITY AND FAULT DIAGNOSIS OF ADDITIVE CHANGES IN STATE SPACE MODELS
F. GUSTAFSSON
We derive a Kalman filter based on data from a sliding window. This is used for a new approach to fault detection and diagnosis, where the state estimate from past data is compared to the state estimate of some of the future data. We suggest a method to judge the quality of diagnosis in a simple way. For fault estimation in the diagnosis, the general concept of stochastic observability in linear systems is introduced. Its role on the design step is illustrated on a problem of estimating the true velocity of a car.