In multiple sensor distributed detection system

conventional fusion rule algorithm need to know the error probabilities of each sensor

and it is difficult to optimize the sensors and the fusion center simultaneously in the system. A least squares fusion rule（ LSFR） algorithm is proposed

LSFR algorithm does not rely on any stability of the noise environment and false alarm and detection probabilities of the sensors. Fusion center combines the hard decisions of each sensor to make global hard decision

and acquire the optimal global performance when the processing of the sensors and fusion center are optimal. The simulation results show that

compare to likelihood ratio fusing decision-making algorithm and Neyman Pearson fusion rule（ NPFR） algorithm

the global detection probability of LSFR algorithm is significantly improved

and LSFR algorithm has preferable compatibility in the distributed detection system of different scale of sensor and more types.