Performance evaluation of multi-sensor data fusion technique for test range application

Abstract

We have adopted the state-vector fusion technique for fusing multiple sensors track data to provide complete and precise trajectory information about the flight vehicle under test, for the purpose of flight safety monitoring and decisionmaking at Test Range. The present paper brings out the performance of the algorithm for different process noise and measurement noise using simulated as well as real track data.

Doppler Information Optimization through Fusion Algorithms

“…Two commonly employed multi sensor information fusion [10] techniques are (i) state-vector fusion and (ii) measurement fusion [3,5]. The state-vector fusion method uses covariance of the filtered output of individual noisy sensor data to obtain an improved joint state estimate.…”

“…The state-vector fusion method uses covariance of the filtered output of individual noisy sensor data to obtain an improved joint state estimate. On the other hand, the measurement fusion method directly fuses the sensor measurements to obtain a weighted or combined measurement and then uses a single Kalman Filter to obtain the final state estimate based on the fused measurement [3]. In the present case, state-vector fusion methodology has been employed.…”

“…Papic et al, [1] estimated the velocity through window width adaptation, based on the estimates of target acceleration and signal-to-noise ratio. Banerjee et al, [9] and Bhattacharya et al, [3] used the state-vector fusion technique for estimating the position of the target. Hedrick [5] in his research report used adaptive Kalman estimation but used PDA method to obtain fused velocity estimates.…”

“…In this paper an attempt is made to compare the performance of the state vector fused adaptive Kalman velocity estimation [ fig.5] with the adaptive fused Kalman velocity estimation [ fig.6]. Here the estimated velocity from Doppler filter is optimized using five different approaches namely 1) Kalman filter [10] 2) Adaptive Kalman filter [1,5] 3) Kalman filter with state vector fusion [2,3,9] 4) Adaptive Kalman filter with state vector fusion [1,3,5,9] 5) State vector fused adaptive Kalman filter and are simulated through MATLAB and compared.…”

“…The problem formulation of state vector fusion [2,3], in Kalman filter structure and the adaptive Kalman filter structure along with the importance of multi sensor data fusion are explained in sections 2 to 6. In section 7 results of velocity estimation through these algorithms is simulated.…”