Sensor management based on fisher information gain

Multi-sensor system is becoming increasingly important in a variety of military and civilian applications. In general, single sensor system can only provide partial information about environment while multi-sensor system provides a synergistic effect, which improves the quality and availability of information. Data fusion techniques can effectively combine this environmental information from similar and/or dissimilar sensors. Sensor management, aiming at improving data fusion performance by controlling sensor behavior, plays an important role in a data fusion process. This paper presents a method using fisher information gain based sensor effectiveness metric for sensor assignment in multi-sensor and multi-target tracking applications. The fisher information gain is computed for every sensor-target pairing on each scan. The advantage for this metric over other ones is that the fisher information gain for the target obtained by multi-sensors is equal to the sum of ones obtained by the individual sensor, so standard transportation problem formulation can be used to solve this problem without importing the concept of pseudo sensor. The simulation results show the effectiveness of the method.

Game Theory Based Sensor Management in Reducing Target Threat Level Assessment Risk

Sensor management schemes are calculated to reduce target threat level assessment risk in this paper.Hidden Markov model and risk theory are combined to build the target threat level model firstly.Then the target threat level estimation risk is defined.And the sensor management schemes are optimized with the smallest target threat level assessment risk.What’s more,the game theory is applied to calculate the optimal sensor management scheme.Some simulations are conducted to prove that the proposed sensor management method is effective.

Performance evaluation of sensor allocation algorithm based on covariance control

The covariance control capability of sensor allocation algorithms based on covariance control strategy is an important index to evaluate the performance of these algorithms. Owing to lack of standard performance metric indices to evaluate covariance control capability, sensor allocation ratio, etc, there are no guides to follow in the design procedure of sensor allocation algorithm in practical applications. To meet these demands, three quantified performance metric indices are presented, which are average covariance misadjustment quantity （ACMQ）, average sensor allocation ratio （ASAR） and matrix metric influence factor （MMIF）, where ACMQ, ASAR and MMIF quantify the covariance control capabili- ty, the usage of sensor resources and the robustness of sensor allocation algorithm, respectively. Meanwhile, a covariance adaptive sensor allocation algorithm based on a new objective function is proposed to improve the covariance control capability of the algorithm based on information gain. The experiment results show that the proposed algorithm have the advantage over the preceding sensor allocation algorithm in covariance control capability and robustness.