Multi-sensor Target Recognition Research Articles

Intelligent Transportation Application and Analysis for Multi-Sensor Information Fusion of Internet of Things

During the driving of the smart car, due to the complexity of the environment, a single sensor or multiple homogeneous sensors cannot fully perceive the traffic environment around the smart car. Therefore, it is necessary to study the information fusion scheme of heterogeneous sensors, use the advantages of heterogeneous sensors to make up for the shortcomings of a single sensor, to realize the function of cooperation and mutual compensation among multiple heterogeneous sensors. Therefore, this paper proposes a data fusion method based on discrete factor multi-sensor target recognition. The output data from multiple sensors acquired over too many periods and multiple regions give the discrete factor of the sensor corresponding to the target characteristic. The current weight of multi-sensor target recognition is given according to the discrete factor, and the relative consistency and the relativeness of multi-sensor target recognition is established. Weighted consistency and other functions; combined with the current weights of multi-sensor target recognition and related consistency functions, a data fusion support calculation model for multi-sensor target recognition is constructed. The test results show that the scheme is more reliable and has a certain anti-interference ability, which can make up for the shortcomings of a single sensor and improve the target recognition rate.

Multi-sensor detection and control network technology based on parallel computing model in robot target detection and recognition

Robotics technology has become an interdisciplinary comprehensive information processing technology, and the analysis ability of unmanned robots has become an important factor to measure the intelligence of robots. On the basis of introducing the theory of multi-sensor information and the correlation of robot target detection and recognition, this paper focuses on the application of support vector machine in multi-sensor target recognition. A fusion method based on evidence theory and support vector machine is applied to target recognition, and the computational efficiency is improved by parallel computing model. The recognition performance of the traditional BP neural network and the algorithm is studied by simulation. The simulation results show that the improved algorithm has a high recognition rate under arbitrary noise, especially in large noise. The effectiveness of the improved algorithm in target detection and recognition is illustrated. At the same time, the simulation results also show that the parallel computing mode has been effectively improved.

Airborne multi-sensor target recognition method based on weighted fuzzy reasoning network and improved DS evidence theory

Difficult obtainment of basic probability assignment (BPA) and inaccurate recognition due to conflicting data are the problems in applying Dempster-Shafer (DS) evidence theory to airborne multi-sensor target recognition. In order to effectively deal with them, an airborne multi-sensor target recognition method based on weighted fuzzy reasoning network (WFRN) and improved Dempster-Shafer (IDS) evidence theory is proposed in this paper. First, the feature vector consisting of 5 feature components is constructed. And then a 4-layer WFRN consisting of 3 kinds of basic units is established to obtain BPA of the feature vector. Finally, conflict data is processed through IDS evidence theory to obtain the final fusion recognition result. The simulation results indicate that the proposed airborne multi-sensor target recognition method is able to obtain BPA reasonably and deal with conflict information.

A Self-Adaptive Combination Method in Evidence Theory Based on the Power Pignistic Probability Distance

Existing methods employed for combining temporal and spatial evidence derived from multiple sources into a single coherent description of objects and their environments lack versatility in various applications such as multi-sensor target recognition. This is addressed in the present study by proposing an adaptive evidence fusion method based on the power pignistic probability distance. This method classifies evidence sets into non-conflicting and conflicting evidence sets based on the maximum power pignistic probability distance obtained between evidence pairs in the evidence set. Non-conflicting evidence sets are fused using Dempster’s rule, while conflicting evidence sets are fused using a weighted average combination method based on the power pignistic probability distance. The superior evidence fusion performance of the proposed method is demonstrated by comparisons with the performances of seven other fusion methods based on numerical examples with four different evidence conflict scenarios. The results show that the method proposed in this paper not only can properly fuse different types of evidence, but also provides an excellent focus on the components of evidence sets with high confidence, which is conducive to timely and accurate decisions.

Automatic target recognition algorithm based on affine invariant feature of line grouping

In order to improve the accuracy of the multi-sensor automatic target recognition algorithm,based on line group matching, an image matching algorithm using linear group geometric primitives was proposed. Line group was used to represent the target. Every three lines in line group can compose a triangle. An affine invariant, which consisted of the ratio of these triangles' area, was considered as a distinct feature to describe the line group. The best matching line group was found among the candidate line groups in real-time infrared image. The corresponding points between target and real-time image can be obtained through the matching lines. Then the location of the target can be computed through affine transformation. Finally, the position of the target in the real-time map was detected by the same-named point; in addition, a method of linear purification in the real-time infrared image was proposed. The extracted primary lines provided a condition which ensured the effectiveness and efficiency of the proposed recognition algorithm. Experimental result shows that the proposed algorithm has a high matching accuracy in multi-sensor target recognition.

Safety for pedestrian recognition in sensor networks based on visual compressive sensing and adaptive prediction clustering

Aiming at the imbalance between energy use and tracking accuracy in multi-sensor target recognition, a pedestrian target recognition method based on visual compressed sensing and adaptive predictive clustering is proposed to track multiple pedestrians simultaneously. After acquiring the pedestrian target image, the scale invariant features of the pedestrian face in the image are extracted firstly, and the target is sparsely represented by the feature dictionary. Then adaptive prediction clustering is used to capture the change of pedestrian behavior attributes. Then, the sensor is selected by Region method, and the sensor contributing to the pedestrian area is activated to realize the pedestrian tracking. In the simulation scenario, 500 sensors are randomly deployed in a given square area. Because of fewer sensors and shorter computation time, the network lifetime has been significantly improved.

Grey Relational Analysis for Hesitant Fuzzy Sets and Its Applications to Multiattribute Decision-Making

Due to the superiority in expressing the uncertain and vague information, the hesitant fuzzy set (HFS) is regarded as an important tool to deal with multiattribute decision-making (MADM) problems. Quantitative and qualitative fuzzy measures have been proposed to solve such problems from different points. However, most of the existing information measures for HFSs are related to such fuzzy measures as distance, similarity, entropy, and correlation coefficients. The grey relational analysis is omitted. Besides, the existing grey relational analysis for HFSs only considers the range or distance between HFSs data which is only a partial measure of the HFSs. Therefore, in this paper, we improve the grey relational analysis for HFSs and explore a novel slope grey relational degree by considering another factor of HFSs data: the slope. Further, we combine both the distance and slope factors of HFSs data to construct a synthetic grey relational degree that describes the closeness and variation tendency of HFSs simultaneously, greatly enriching the fuzzy measures of HFSs. Furthermore, with the help of the TOPSIS method, we develop the grey relational based MADM methodology to solve the HFSs MADM problems. Finally, combining with two practical MADM examples about energy policy selection and multisensor target recognition, we obtain the most desirable decision results. Compared with the previous methods, the validity, comprehensiveness, and discrimination of the proposed synthetic grey relational degree for HFSs are demonstrated in detail.

A New Multi-Sensor Target Recognition Framework based on Dempster-Shafer Evidence Theory

A New Multi-Sensor Target Recognition Framework based on Dempster-Shafer Evidence Theory

Temporal evidence combination method for multi-sensor target recognition based on DS theory and IFS

Temporal evidence combination method for multi-sensor target recognition based on DS theory and IFS

Relative Entropy Evaluation Method for Multi-sensor Target Recognition

Relative Entropy Evaluation Method for Multi-sensor Target Recognition

Multi-Sensor Target Recognition Using VIKOR Combined with G1 Method

The aim of this paper is to propose a new multi-sensor target recognition method based on VIKOR method. Multi-sensor target recognition is an important part of information fusion. The multi-sensor target recognition problem contains many factors, and thus it is actually a multi-attribute decision problem. The new multi-sensor target recognition method combines the VIKOR method with G1 method. G1 method is an objectively determining weights method, and it does not need to test the consistency of judgment matrix, thus it is better than AHP. A practical example is studied to demonstrate the effectiveness and feasibility of the proposed method.

A Novel Information Fusion Method Based on Preference Selection Index

The aim of this paper is to propose a new information fusion method for the problem of multi-sensor target recognition. Multi-sensor information fusion problem contains many characteristic indexes, and thus it can be regarded as a multi-attribute decision making problem. The new fusion method is put forward based on preference selection index method. The new information fusion method is not necessary to assign relative importance between attributes, but overall preference value of attributes are calculated using concept of statistics. Thus the new method can overcome the subjective randomness of subjectively weighting method. An applied example proves that the method is both effective and exercisable.

Multi-sensor Target Recognition Based on Relative Ratio Method

The aim of this study is to propose a new target recognition method for the multi-sensor with multiple characteristics indexes. Coefficient of variation is used to determine the weights of characteristic indexes. The conceptions of ideal optimal and negative ideal vectors are given first and then normalized relative ratio is used to comprehensive evaluation value. Hence the rule of target recognition is given. The method can avoid the subjectivity of the weight of characteristic indexes and improve the objectivity and accuracy of target recognition. Finally, numerical simulation illustrates the effectiveness and feasibility of the proposed method.

A Novel Combination Method of Conflict Evidence in Multi-Sensor Target Recognition

A novel combination method is proposed in order to efficiently combine conflict evidence. The credibility of the evidence is derived by introducing a distance function, and the conflict probability is distributed to every proposition according to its credibility. Better performance is demonstrated by numerical examples compared with three other methods.

Target recognition based on modified combination rule

Evidence theory is widely used in the field of target recognition. The invalidation problem of this theory when dealing with highly conflict evidences is a research hotspot. Several alternatives of the combination rule are analyzed and compared. A new combination approach is proposed. Calculate the reliabilities of evidence sources using existing evidences. Construct reliabilities judge matrixes and get the weights of each evidence source. Weight average all inputted evidences. Combine processed evidences with D-S combination rule repeatedly to identify a target. The application in multi-sensor target recognition as well as the comparison with typical alternatives all validated that this approach can dispose highly conflict evidences efficiently and get reasonable recognition results rapidly.

Information-based approach to performance estimation and requirements allocation in multisensor fusion for target recognition

A novel methodology offering the potential for resolving two of the significant problems of implementing multisensor target recognition systems, i.e., the rational selection of a specific sensor suite and optimal allocation of requirements among sensors, is presented. Based on a sequence of conjectures (and their supporting arguments) concerning the relationship of extractable information content to recognition perfor- mance of a sensor system, a set of heuristics (essentially a reformulation of Johnson's criteria applicable to all sensor and data types) is devel- oped. An approach to quantifying the information content of sensor data is described. Coupling this approach with the widely accepted Johnson's criteria for target recognition capabilities results in a quantitative method for comparing the target recognition ability of diverse sensors (imagers, nonimagers, active, passive, electromagnetic, acoustic, etc.). Extension to describing the performance of multiple sensors is straightforward. The application of the technique to sensor selection and requirements allo- cation is discussed. © 1997 Society of Photo-Optical Instrumentation Engineers. Subject terms: sensor fusion; target recognition; information theory; Johnson cri- teria; requirements allocation; performance analysis; imaging sensors; nonimag- ing sensors.