On the divergence of information filter for multi sensors fusion

• Multi sensor bearing only target tracking problem is presented here.
• The paper addresses the divergence in information filter after initial convergence for this case.
• The information update equations of the information filter are modified using fuzzy functions to control divergence.
• The divergence in the modified filter is much smaller compared to conventional information filter.
• The velocity estimate converges to the desired values and remains stable.

The paper deals with the divergence of information filter in multi sensor target tracking problem using bearing only measurements. Information filter has a number of advantages in terms of computational requirements over Kalman filter for target tracking applications. Compared to Kalman filter it also has the advantage that one can start estimation even without an initial estimate. But this filter is seen to diverge after tracking for a short period of time, even when the target is moving at a constant velocity. A technique to overcome this problem has been discussed in this paper. The information update equations of the conventional information filter are modified in terms of fuzzy function of error and change of error, and the results have been found to be encouraging. The efficacy of the technique in preventing divergence is demonstrated in the context of tracking a maneuvering target also.