Data fusion has been widely studied in the last decades, especially its military applications. Multi-sensor data fusion (MSDF) technology plays a more and more significant role for the fighting demand. How to fuse the sensor data is still an open issueGarcia et al. (2017); Ozanyan (2015); Jiang et al. (2016); Zebin et al. (2015). Due to the powerful ability of handling uncertain information, DS evidence theory is widely used in MSDFBasir and Yuan (2007); Dallil et al. (2013); Maherin and Liang (2015); Weeraddana et al. (2015). However, Lots of interference exist in the complex practical situation. The information provided by a sensor report is likely to be disturbed and incorrect. In this case, strong conflict may exist among evidences and lead to a wrong fusion result. Handling conflict is crucial in data fusionMoenks et al. (2016); Perez et al. (2016); Zha (2016, 2017). To address it, many approaches have been proposedWang et al. (2016); Yager et al. (2016); Jiang and Zhan (2017).
To deal with conflictive information, most previous methods handle evidences based on the relationship among the data collected by sensorsDeng et al. (2015); Liu (2006); Murphy (2000); Guo et al. (2006). In this letter, however, an method which bases on the properties of a sensor itself is proposed. To evaluate the reliability of sensor reports, a confidence coefficient curve is determined based on a quantum mechanical approach. Interest in quantum approach to classical fuzzy logic has increased over the last decadesAbd Ali et al. (2015); Bolotin (2001); Dubois and Toffano (2016); Pykacz (2015)
. In classical mechanics, a particle is located in an exact place. If a particle is known to be in M, then it can never in any other places, like in N. In quantum mechanics, however, a particle can never be exactly located due to the well-known Heisenberg’s uncertainty relation. Only the probability of finding the particle in a given area like M or N can be determined (shown as FigureLABEL:particle). This interesting property of quantum mechanics is used to describe the reliability degree of a sensor report as it is hard to assert that one sensor report is totally reliable or unreliable. Then we use the curve to calculate the credibility of evidences. The fusion results of the modified evidences show the effectiveness of our method.
Dempster-Shafer evidence theory was proposed by Dempster in 1967Dempster (1967) and modified by Shafer in 1978Shafer (1978). In evidence theory, the basic set , called the frame of distribution, consists of a set of mutually exclusive and exhaustive hypotheses, symbolized by Let denote the power set composed of elements of .
Basic probability assignment (BPA) is a mapping from to , defined by:
satisfying the following conditions:
The mass function represents a supporting degree to . The elements of that have a non-zero mass are called focal elements. A body of evidence (BOE) is the set of all the focal elementsJousselme2001A:
is a subset of , and each of has a fixed value. The classical Dempster’s combining rule of two BOE and is defined as following:
where is called conflict coefficient:
3 Quantum mechanical modelling of the sensor reliability in data fusion
Radar plays an important role in the modern battlefield. Usually, to obtain the overall information, data from several radars need to be fused. Aiming to do a more reasonable fusion, we propose an method based on quantum mechanics to determine the confidence coefficient curve of radar sensor reports. We assume that the reliability of sensor reports relates to the distance between object and sensor in some degrees. For each distance , the sensor has an according confidence coefficient whose maximum value is 1. Hence, confidence coefficient curve is defined as a function to describe this relationship.
The signal of the object is received by radars. The transmit power of the object is , the antenna gain of the object is , the antenna gain of the reconnaissance radar is , the distance between object and a radar is denoted as . The signal power received by radar is:
where is the wavelength and is Radar Cross-Section which is the product of geometric cross-section, reflection coefficient and direction coefficient.
If the sensitivity of a radar is , the maximal reconnaissance distance is calculated as follows. If the object is far beyond this distance, it will not be effectively reconnoitred.
According to the quantum-mechanical rules of quantification, we should write an operator which corresponds to the received signal power:
where is a scale factor. is a quasi-potential function to model the received power.
Based on quantum-mechanical rules, a quasi time-independent Schrdinger equation can be obtained.
where relates to the level of the radar sensitivity .
The solution of Eq. (10) is a quasi-amplitude distribution . When the object is within the maximal reconnaissance distance , we can obtain:
where and are the Bessel function of the first kind and the second kind respectively. is their order:
Then let us consider the other situation, when the object is beyond , the value of is infinite. According to quantum mechanics, it is impossible for a particle to penetrate the well wall if it is within a infinite well potential. Hence, we can conclude that in this case.
Then we can obtain the probability distribution, which is illustrated graphically in Figure 2.
By amplifying Eq. (13), we can obtain the confidence coefficient curve .
Seen from Figure 3, the curve rises rapidly when is smaller than and comes to its maximum when equals to . Then it declines slowly until comes to , which is reasonable. In practical situation, due to precision and some other intricate issues, a radar do not work well when it is too close to the object. There exists an optimal distance for a radar to work. Then the performance of a radar becomes poorer as it is located further. When the distance is further than the maximal reconnaissance distance, the radar can not reconnoitre the object effectively. With the basis of this curve, we can evaluate the reliability of radar reports effectively. For different types of radars, we can obtain their according confidence coefficient curves as Figure 4. The parameters of these curves are in Table 1.
In the following, the curves are used in combining evidences. Assume we have pieces of BOEs: , collected from radar sensors. By using confidence coefficient curves, each BOE corresponds to one confidence coefficient: . The credibility degree of BOE is defined as:
It is easy to find that . Hence, the credibility degree reveals the relatively importance of the collected evidence. After determining the credibility of each BOE, we do a modified average for all pieces of BOEs to obtain a new evidence .
Then we can combine with itself for times by using classical combining rule (Eq. (4)), which is same as Murphy’s approachMurphy (2000). Obviously, if a BOE is collected from a sensor with high reliability, it will have more effect on the final combination results. On the contrary, if a BOE is collected from a sensor with relatively low reliability, it will matter little in the final combination results.
4 Numerical example
In this section, a numerical example is illustrated to show the effectiveness of our method. In a target recognition system, five radar sensors have collected five pieces of BOEs shown as follows:
The reliability of these sensor reports is 0.55, 0.6, 0.25, 0.45 and 0.5 respectively, which is obtained based on their confidence coefficient curves. Then fusion results and comparison are shown in Table 2.
Four evidences prefer to recognizing the target as . Hence, data from the third sensor is probable to be interfered and incorrect. As can be seen from Table 2, in this situation, our method works better than Murphy’s while the classical combining rule does not work. The target can be effectively recognized with our method.
In summary, we propose a new method to model the reliability of sensor reports. Unlike previous methods, we focus on the properties of a sensor itself. The confidence coefficient curve of a radar sensor is obtained by solving a a quasi time-independent Schrdinger equation. The method is used in combining of evidences. The result shows the efficiency of our method.
The work is partially supported by National Natural Science Foundation of China (Grant No. 61671384), Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2016JM6018), Aviation Science Foundation (Program No. 20165553036), the Fund of SAST (Program No. SAST2016083)
- Garcia et al. (2017) F. Garcia, D. Martin, A. de la Escalera, J. M. Armingol, Sensor fusion methodology for vehicle detection, IEEE Intelligent Transportation Systems Magazine 9 (2017) 123–133.
- Ozanyan (2015) K. B. Ozanyan, Tomography defined as sensor fusion, in: 2015 IEEE SENSORS, pp. 1–4.
- Jiang et al. (2016) W. Jiang, C. Xie, M. Zhuang, Y. Shou, Y. Tang, Sensor data fusion with z-numbers and its application in fault diagnosis, Sensors 16 (2016) 1509.
- Zebin et al. (2015) T. Zebin, P. Scully, K. B. Ozanyan, Inertial sensing for gait analysis and the scope for sensor fusion, in: 2015 IEEE SENSORS, pp. 1–4.
- Basir and Yuan (2007) O. Basir, X. Yuan, Engine fault diagnosis based on multi-sensor information fusion using dempster cshafer evidence theory, Information Fusion 8 (2007) 379–386.
- Dallil et al. (2013) A. Dallil, M. Oussalah, A. Ouldali, Sensor fusion and target tracking using evidential data association, IEEE Sensors Journal 13 (2013) 285–293.
- Maherin and Liang (2015) I. Maherin, Q. Liang, Multistep information fusion for target detection using uwb radar sensor network, IEEE Sensors Journal 15 (2015) 5927–5937.
- Weeraddana et al. (2015) D. M. Weeraddana, C. Kulasekere, K. S. Walgama, Dempster-shafer information filtering framework: Temporal and spatio-temporal evidence filtering, Sensors Journal IEEE 15 (2015) 5576–5583.
- Moenks et al. (2016) U. Moenks, H. Dorksen, V. Lohweg, M. H bner, Information fusion of conflicting input data., Sensors 16 (2016) 1798.
Perez et al. (2016)
A. Perez, H. Tabia,
D. Declercq, A. Zanotti,
Using the conflict in dempster cshafer evidence theory as a rejection criterion in classifier output combination for 3d human action recognition,Image & Vision Computing 55 (2016) 149–157.
- Zha (2016) A novel combination method for conflicting evidence based on inconsistent measurements, Information Sciences 367-368 (2016) 125 – 142.
- Zha (2017) Perceiving safety risk of buildings adjacent to tunneling excavation: An information fusion approach, Automation in Construction 73 (2017) 88 – 101.
- Wang et al. (2016) X. Wang, J. Zhu, Y. Song, L. Lei, Combination of unreliable evidence sources in intuitionistic fuzzy mcdm framework, Knowledge-Based Systems 97 (2016) 24–39.
- Yager et al. (2016) R. R. Yager, P. Elmore, F. Petry, Soft likelihood functions in combining evidence, Information Fusion 36 (2016) 185–190.
- Jiang and Zhan (2017) W. Jiang, J. Zhan, A modified combination rule in generalized evidence theory, Applied Intelligence 46 (2017) 630–640.
Deng et al. (2015)
X. Deng, D. Han,
J. Dezert, Y. Deng,
Evidence combination from an evolutionary game theory perspective.,IEEE Transactions on Cybernetics 46 (2015) 2070.
- Liu (2006) W. Liu, Analyzing the degree of conflict among belief functions, Artificial Intelligence 170 (2006) 909–924.
- Murphy (2000) C. K. Murphy, Combining belief functions when evidence conflicts, Decision Support Systems 29 (2000) 1–9.
- Guo et al. (2006) H. Guo, W. Shi, Y. Deng, Evaluating sensor reliability in classification problems based on evidence theory, IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society 36 (2006) 970–81.
- Abd Ali et al. (2015) J. Abd Ali, M. A. Hannan, A. Mohamed, A novel quantum-behaved lightning search algorithm approach to improve the fuzzy logic speed controller for an induction motor drive, Energies 8 (2015) 13112–13136.
- Bolotin (2001) A. Bolotin, Quantum mechanical approach to fuzzy logic modelling, Mathematical & Computer Modelling 34 (2001) 937–945.
- Dubois and Toffano (2016) F. Dubois, Z. Toffano, Eigenlogic: A Quantum View for Multiple-Valued and Fuzzy Systems, Springer International Publishing, 2016.
- Pykacz (2015) J. Pykacz, Quantum physics, fuzzy sets and logic, Springerbriefs in Physics (2015).
- Dempster (1967) A. P. Dempster, Upper and lower probabilities induced by a multivalued mapping, Annals of Mathematical Statistics 38 (1967) 325–339.
- Shafer (1978) G. Shafer, A mathematical theory of evidence, Technometrics 20 (1978) 242.