CRLB with Pd<1 fused tracks

Integrated Sensor Systems and Data Fusion for Homeland Protection

“…In [60] this computation is reported in case of fusion of data from two sensors with an ideal unitary detection probability. In [61,62] the same computation has been proposed in case of detection probability less than one and false alarm probability higher than zero. Cooperative sensor data fusion: A cooperative sensor network uses the information provided by two independent sensors to derive information that would not be available from the single sensors.…”

Adaptive Kalman Estimation in Target Tracking Mixed with Random One-Step Delays, Stochastic-Bias Measurements, and Missing Measurements

“…In general, there are two ways to describe the missing phenomenon. One way is to model the uncertainty by using a stochastic Bernoulli binary switching sequence taking on values of 0 and 1 (see, e.g., [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] and the references therein). The suboptimal filtering algorithm [2] in the minimum variance sense with only missing measurements has been proposed and the robust filter [14] is designed.…”

“…The stochastic stability of the extended kalman filter with missing measurements is analyzed in [8], while stochastic stability of the unscented kalman filter with missing measurements is studied in [13]. For benchmarking the performance of any estimation algorithm with missing measurements in advance, the modified Cramer-Rao bound (CRLB) and modified Riccati equation have been studied in [1,[15][16][17][18]. Another way is to model the uncertainty as a Markovian jumping sequence (see [19][20][21] and the references therein).…”