Bearings-only tracking and Doppler-bearing tracking with inequality constraint

This paper aims to find an appropriate approach to improve estimation accuracy of bearings-only tracking (BOT) and Doppler bearing tracking (DBT) by making use of the constraint on target speed. Targets usually travel within a valid speed zone so this contextual information (speed inequality constraint) should hypothetically help tracking algorithms (filters) achieve better accuracy. However the inequality constraint filters, usually implemented using the rejection sampling approach, have high computational cost. This paper will study the accuracy improvement brought by the inequality constraint as well as the computational cost introduced in the BOT and DBT problems. Furthermore, we will also propose cost effective approach, which is the speed and range parameterized multiple model (MM) filter with different initial states in the valid range and speed zone. This MM-BOT/DBT, inspired from the range parameterized BOT (RP-BOT), applies the speed inequality constraint at track initial stage. Simulation test results show that the MM approach outperforms others in terms of estimation accuracy and computational efficiency.

[1]  C. Jauffret,et al.  Observability in passive target motion analysis , 1996 .

[2]  N. Peach,et al.  Bearings-only tracking using a set of range-parameterised extended Kalman filters , 1995 .

[3]  Dennis S. Bernstein,et al.  Unscented filtering for interval-constrained nonlinear systems , 2008, 2008 47th IEEE Conference on Decision and Control.

[4]  P. Fearnhead,et al.  Improved particle filter for nonlinear problems , 1999 .

[5]  K. Gong,et al.  Position and Velocity Estimation Via Bearing Observations , 1978, IEEE Transactions on Aerospace and Electronic Systems.

[6]  Michael Mertens,et al.  Context Exploitation for Target Tracking , 2016, Context-Enhanced Information Fusion.

[7]  Julien Clavard,et al.  Target Motion Analysis of a Source in a Constant Turn from a Nonmaneuvering Observer , 2013, IEEE Transactions on Aerospace and Electronic Systems.

[8]  Yaakov Bar-Shalom,et al.  Track formation with bearing and frequency measurements in clutter , 1990 .

[9]  V. Aidala Kalman Filter Behavior in Bearings-Only Tracking Applications , 1979, IEEE Transactions on Aerospace and Electronic Systems.

[10]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[11]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..