^{1}

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In this paper, we study about a method to optimize the fused track quality in intelligence network of radar target fusion system, considering the role of people in the fusion system; we start to find ways to optimize the quality of the fused track, and adaptive smoothing method is proposed based on fuzzy theory. Tests show that this method can greatly improve the quality of the fused track system for battlefield reconnaissance provides high-quality, high-reliability battlefield.

Battlefield reconnaissance system handles all the information level by level, and the information includes radar reconnaissance, UAV reconnaissance, battlefield television reconnaissance, infrared reconnaissance, white reconnaissance, signal detection, human detection, and then fusing step by step, comprehensive analysis of the formation of a unified battlefield situation. Finally we assess the threat by the situation analysis assistant. In reconnaissance systems, through multi-sensor information fusion, acquisition integration of information can greatly improve the combat effectiveness of battlefield reconnaissance.

Multi-sensor information fusion system as an important part of battlefield reconnaissance system is highly valued and widely used in the military. One of its main tasks is, within the observational data from multiple sensors transferred to a treatment center or a master through integrated intelligence or data fusion, to establish clear goals of high-quality composite track report. In this system, due to a large amount of information to be used to detect, the reliability of the system is improved greatly. Because of the different detection properties of different sensors, such as different sensor’s random error, and measurement system error, it comes to the poor quality of smoothness. This problem can be solved in two ways: first, before the integration of information quality assessment, the assessment results are used to guide the information fusion strategies to get high-quality fused track; second, a feedback mechanism for fusion track adaptive optimization is use. In this paper, the second approach, the adaptive fuzzy theory is used to optimize the parameters of the track after fusion adaptive optimization process, greatly improving the quality of the integration of information.

Contents of this paper are organized as follows: first, the fuzzy theory and track smoothing methods outlined, followed by adaptive smoothing method which was based on fuzzy theory to design, and then simulation was used to validate the method; the final summary was given at last.

Fuzzy theory [

Information integration is the utilization of multiple sources of information to extract higher quality than any single integrated information process information [

Track Smoothing [

In the data fusion system, factors that affect the quality of the fused track are complex, these factors including performance indicators of different sensors, collaborative performance and environmental factors such as collaborative performance between multi-sensor, so we chose to depart from the results were fused track smoothing. Moving average method to improve, to get the second smoothing methods, and artificial intelligence (fuzzy inference) into its parameter adjustment, and to improve the quality of the fused track.

Right simple moving average smoothing method of heavy elements are equal, which is calculated as follows:

In this formulation,

The value of smooth at time k is:

By the Formula (1) and a moving average smoothing Equation (2) can be obtained in the form of recursive:

Recursive form using Equation (3), twice the moving average, and seek a weighted average, you can get the second smoothing method. Formula of this method is simple, easy to implement. It retains the advantages of moving average method, but also reduce the amount of data storage; and because it is able to fully repair all historical data points evenly, to reflect changes in the trend data point is more accurate, and therefore get better smoothing effect. But the lack of a clear, choose the smoothing parameter-free method for determining the presence of which in the course of the actual workers can only work with a personal experience to choose. And the smoothing parameter is established, it cannot change the characteristics of time series based on the stage. Such smoothing model cannot truthfully, dynamically reflect the time sequence. In response to these problems, this paper designs a fuzzy adaptive smoothing model. The model implements adaptive dynamic smoothing of time series data with engineering practicality.

The basic block diagram of the fuzzy adaptive smoothing system is shown in

Analysis of multi-sensor data fusion system, the integration of track quality requirements: First, the fusion track as close to the real target track, which requires the integration of track with high accuracy; secondly the fusion track as smooth as possible, which requires integration track jitter of less high smoothness.

Therefore, the error of the mean

First converting the input conventional fuzzy controller [

The fusion track

probability of corresponding points describe other triangle membership function MF, basically described as follows.

Obtained in accordance with the following basic rules of mathematical principles and engineering experience:

1) If the error is the mean

2) If the error is the mean

3) If the error variance

4) If the error variance

Note: The size of

According to the basic rules, designed to adjust the smoothing parameter of fuzzy rules, as described below, If

By fuzzy logic inference method [_{1}, R_{2}, ..., R_{n}, resulting in the total system of fuzzy relation matrix for the synthesis algorithm fuzzy rules

Then for any

Adjust the amount of blur

The smoothing method previously designed for a fusion system, for real-time integration of the target track adaptive smoothing, 4 and 5 give the results of FIG.

NL | NM | NS | Z | PS | PM | PL | |
---|---|---|---|---|---|---|---|

NL | PL | PM | PS | Z | PS | PM | PL |

NM | PL | PM | PS | Z | PS | PM | PL |

NS | PM | PS | PS | Z | PS | PM | PL |

Z | PM | PS | PS | Z | PS | PM | PL |

PS | PM | PS | Z | NS | Z | PS | PM |

PM | PS | Z | NS | NM | NS | Z | PS |

PL | Z | NS | NM | NL | NM | NS | Z |

track before smoothing, yellow fused track smoothed. Quality indicators were compared before and after the track and calculate the smoothed mean accuracy error which representative track, smoothness representative track of the error variance.

From

Target NO. | Mean of error (km) | Variance of error (km) | ||||
---|---|---|---|---|---|---|

Before | After | Rising rate (%) | Before | After | Rising rate (%) | |

11 | 0.6026 | 0.3888 | 35.48 | 0.2195 | 0.0983 | 55.23 |

15 | 1.1863 | 0.5476 | 53.84 | 0.5956 | 0.1802 | 69.74 |

19 | 1.2393 | 0.7019 | 43.37 | 0.3544 | 0.1936 | 45.39 |

28 | 0.7124 | 0.4035 | 43.36 | 0.1862 | 0.0854 | 54.14 |

Target NO. | Mean of error (km) | Variance of error (km) | ||||
---|---|---|---|---|---|---|

Before | After | Rising rate (%) | Before | After | Rising rate (%) | |

31 | 1.2486 | 0.7835 | 37.24 | 0.5792 | 0.2626 | 54.66 |

42 | 1.0235 | 0.5769 | 43.63 | 0.5956 | 0.1802 | 69.74 |

51 | 0.8699 | 0.5781 | 33.54 | 0.1014 | 0.0827 | 18.44 |

62 | 0.9781 | 0.5564 | 43.11 | 0.4836 | 0.1977 | 59.12 |

65 | 1.0538 | 0.6715 | 36.28 | 0.4472 | 0.1252 | 72.00 |

66 | 1.5846 | 0.8244 | 47.97 | 0.4912 | 0.2010 | 59.07 |

has greatly improved the quality of the fused track for battlefield reconnaissance battlefield provides high quality, high reliability.

The track filter smoothing method breaks through the concept of target modeling, and overcomes the high sensitivity of the sampling by track filter. Filtering divergence phenomenon caused by the physical environment and data model mismatch which has fundamentally overcame. Compared with other smoothing methods (such as fitting straight lines, moving mean, weighted mean, etc.), it takes full advantage of the historical information of target track and the trends of target location, eliminating the time-sensitivity of the algorithm. It greatly reduces the amount of storage (compared with the straight line fitting and the moving mean), and improves the computing speed. Meanwhile fuzzy theory is introduced to achieve the parameters of smooth which can be adjusted online intelligently, and it can better improve the precision of smooth.

Yao Wan,Changlin Hu, (2015) Adaptive Smoothing Method Based on Fuzzy Theory Study and Realization. Journal of Computer and Communications,03,38-43. doi: 10.4236/jcc.2015.35005