Indoor Positioning System For Finding The Location And Predicting Missing Fingerprints

In recent years,the continuous advancement in positioning-based technology benefited people’s lives because it helps improve the way of life.After introducing the Global positioning system(GPS)and China’s Beidou satellite navigation system,people’s daily living effectively depends on positioning information because both provide high-precision positions.The demand for real scenes makes indoor positioning technology research still important;simultaneously,it puts forward high precision indoor positioning technology requirements.Several indoor positioning technologies are available,but Wi Fi Positioning Technology is getting more attention among researchers because of its moderate cost and easy implementation.When we implement Wi Fi Technology with specific methods based on the received signal strength,it has issues such as the high cost of building a fingerprint information database and people’s significant influence on the signal.Two types of signals in the frequency band of 2.4GHz and 5GHz available.The 2.4GHz signal has the advantage of the wide range and lower consumption of power,and the 5GHz signal has a better anti-interference ability.Which appropriate frequency band signal to choose has a factor that affects the positioning method’s accuracy.We have introduced an indoor Wi Fi positioning system with a multimodal deep Gaussian process based on the mentioned problems.Furthermore,our indoor positioning method proposes a new fingerprint information collection method to reduce the impact of crowd density on positioning accuracy in different periods.The proposed method is the multi-mode Wi Fi signal fusion algorithm based on principal component analysis,which selects Wi Fi signals in various frequency bands.It is the improved deep Gaussian process model that takes fused multi-frequency signal features as input.For different layers of the Gaussian process,we select different kernel functions,and it makes the multi-kernel depth Gaussian process better represent the structure between layers.