Tracking with distributed sets of proximity sensors using geometric invariants

We propose a new approach to forming an estimate of a target track in a distributed sensor system using very limited sensor information. This approach uses a central fusion system that collects only the peak energy information from each sensor and assumes that the energy attenuates as a power law in range from the source. A geometrical invariance property of the proximity of the distributed sensors relative to a target track is used to generate potential target track paths. Numerical simulation examples are presented to illustrate the practicality of the technique.

[1]  P. Antsaklis,et al.  Stability of the pseudo-inverse method for reconfigurable control systems , 1991 .

[2]  Rick S. Blum,et al.  On some unresolved issues in finding optimum distributed detection schemes , 2000, IEEE Trans. Signal Process..

[3]  A. Cuevas,et al.  Estimating the number of clusters , 2000 .

[4]  Marc Bodson,et al.  Multivariable adaptive algorithms for reconfigurable flight control , 1997, IEEE Trans. Control. Syst. Technol..

[5]  Feng Zhao,et al.  Distributed state representation for tracking problems in sensor networks , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[6]  Nirwan Ansari,et al.  Adaptive decision fusion for unequiprobable sources , 1997 .

[7]  Colin H. Hansen,et al.  Active Structural-Acoustic Control of a Rocket Fairing Using Proof-Mass Actuators , 2001 .

[8]  David Antin,et al.  100 great problems of elementary mathematics : their history and solution , 1966 .

[9]  Hirohito Funato,et al.  Fuzzy logic based vibration suppression control of flexible structures , 2000, 6th International Workshop on Advanced Motion Control. Proceedings (Cat. No.00TH8494).

[10]  Rick S. Blum,et al.  Distributed detection with multiple sensors I. Advanced topics , 1997, Proc. IEEE.

[11]  Eric H. Anderson,et al.  Active structural-acoustic control for composite payload fairings , 1999, Smart Structures.

[12]  A. Raftery,et al.  Nearest-Neighbor Clutter Removal for Estimating Features in Spatial Point Processes , 1998 .

[13]  Hong Wang,et al.  On the use of adaptive updating rules for actuator and sensor fault diagnosis , 1997, Autom..

[14]  Jovan D. Boskovic,et al.  A multiple model-based reconfigurable flight control system design , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[15]  Ming Xiang,et al.  On the performance of distributed Neyman-Pearson detection systems , 2001, IEEE Trans. Syst. Man Cybern. Part A.

[16]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[17]  Hans-Peter Kriegel,et al.  Incremental Clustering for Mining in a Data Warehousing Environment , 1998, VLDB.

[18]  Frank L. Lewis,et al.  Applied Optimal Control and Estimation , 1992 .

[19]  N.S.V. Rao Distributed decision fusion using empirical estimation , 1996, 1996 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems (Cat. No.96TH8242).

[20]  Ramanarayanan Viswanathan,et al.  Optimal distributed decision fusion , 1989 .

[21]  Pramod K. Varshney,et al.  Distributed detection with multiple sensors I. Fundamentals , 1997, Proc. IEEE.

[22]  Eric H. Anderson,et al.  VIBROACOUSTIC MODELING OF A LAUNCH VEHICLE PAYLOAD FAIRING FOR ACTIVE ACOUSTIC CONTROL , 1998 .

[23]  Selahattin Ozcelik,et al.  Active vibration control of a multi-degree-of-freedom structure by the use of direct model reference adaptive control , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[24]  J. Lam,et al.  Simultaneous linear‐quadratic optimal control design via static output feedback , 1999 .

[25]  Rick S. Blum,et al.  Distributed signal detection under the Neyman-Pearson criterion , 2001, IEEE Trans. Inf. Theory.