In order to solve the indoor pedestrian positioning and tracking problems under the condition of sparse anchor nodes, this paper presents a new tracking scheme which predicts the staff position under the condition of indoor location fingerprints based on particle filter. In the proposed algorithm, the indoor topology is adopted to constrain and correct the results. Simulation results show that the proposed algorithm can significantly improve the accuracy of indoor pedestrian positioning and tracking more than the Kalman filter and
With the improvement of communication technology, location-based service (LBS) [
Existing localization algorithms can be divided into the following three categories: range based, range-free, and event driven. Algorithms based on distance measurement need at least three sensors to locate trilateral using triangulation algorithm. Range-free algorithms are based on network connectivity information, which has lower location accuracy than the range-based algorithms. Event-driven localization makes use of localization events which are generated and propagated across the area where sensor networks are deployed. Although these algorithms are very effective, it is hard to employ them directly for the indoor positioning.
Indoor positioning and tracking order that sensors for locating should be deployed first. Increasing the density of coverage would increase the cost. In practice, the aim that the whole indoor area is covered by all anchors is often hard to achieve. Some dead angle inevitably exists. Considering the complexity of the indoor environment, the obstacle diffraction and reflection of the signal and the change of interior structure all have effects on the wireless signal for localization. Indoor localization and tracking should not only consider the normal usage, but also should consider the special cases, for example, the cases of fire or earthquake damage. Some anchor nodes or the order of the deployment of at least three sensors cannot be met [
The rest of the paper is organized as follows. Section
Many excellent schemes have been proposed for the indoor localization. Most of them can be categorized into three classes: range-based localization, range-free localization, and event-driven localization.
Range-based localization algorithms are built on top of distance or angle measurements among the nodes in the networks, which require expensive hardware devices to estimate the distance between the nodes or need careful environment profiling. The Time of Arrival [
To address the limitations of the range-based schemes, range-free localization schemes have been proposed, which attempt to locate sensors without costly ranging devices. The location of each node is estimated based on the knowledge of proximity to the anchor nodes. There are two kinds of localization schemes: anchor-based scheme and anchor-free distrusted localization scheme. Generally, range-free localization methods normally have low accuracy, highly depending on the density and distribution of the anchor nodes.
Recently, event-driven localization schemes have been proposed to simplify the node functionality and to provide high-quality localization. The key idea of these schemes is to use artificial events for localization. Although their effective range can reach hundreds of meters, it needs additional event generation devices and manual operations to generate artificial events.
In the tracking field, the location is often achieved through estimation and filtering like particle filters. It is a kind of method where Monte Carlo simulation is used to solve nonlinear and non-Gaussian problems of the Bayesian estimation [
In indoor wireless environments, various obstacles cause wireless signals irregular reflection and scattering. In addition, barrier properties such as metal, building materials, or human bodies could have different impacts on the propagation of wireless signals so that wireless signals in different buildings will have big gaps. Generally, positioning in indoor places requires the signals received from at least three anchor nodes, and receiving the signals from five or more anchor nodes can result in more accurate location (employing more than five or six nodes cannot further improve the positioning accuracy). Due to the specialty of indoor environments, wireless anchor node’s deployment is hard to cover with at least three anchor nodes in every place. If the number is less than three, signals would be weak or the damaged results cannot provide the anchor nodes with any usable information for positioning, hence, causing intermittent positioning failure.
First, according to the principle of RSSI ranging, we set up the offline indoor radio frequency maps. Each point’s signal strength is the average of several measurements, and the signal data format is
According to the number of anchor nodes in positioning and tracking environments, indoor location tracking process should be discussed for the following two different situations: (1) the situation for sufficient anchor node’s indoor positioning and (2) the situation for sparse anchor node’s indoor positioning. In the first case, the target node is covered by three or more anchor nodes with the initial location being obtained by the KNN algorithm. Then, one can use particle filter to determine the final location. In the second case, the target node is covered by two, one or zero anchor nodes (note that the case of three collinear anchor nodes is similar to that of two anchor nodes). In this case, the location of the target node cannot be directly located, which will cause intermittent positioning failures. As shown in [
Although sparse anchor nodes cannot directly locate the target nodes, some facts can be used for the positioning constraint. By setting the target node Set If the target only receives the signals from two anchor nodes If the target is not covered by any anchor, then we have
The aforementioned are the filtering conditions in the tracking process based on particle filter algorithm. If the particle cannot satisfy the above conditions, it should be filtered.
For simplicity, the particle filter method refers to finding a random sample of groups in the state space transmission and, thereafter, to approximating the probability density function, where integral operation is replaced by a sample mean, and, hence, it achieves the minimum variance distribution process. The samples here refer to particles, while the number of samples Initialization, sampling from the initial distribution of the particle:
where Weight calculation is as follows:
The importance weights are calculated as follows:
And, the importance weights are normalized as
Resample According to the importance weights Output state estimation:
variance estimation:
Step (1) is performed only at the beginning of the algorithm, and the other steps are performed sequentially. Finally, the particle set
Many logic errors may occur in indoor locating cases, for example, positioning tracking information, jumping from one room to another room or to the corridor. In the process of fingerprint-based positioning, there may be a new position for penetrating a wall. Although the derived results are the optimum of the position, the actual route may be very long [
Indoor topology structure can be described as a connected graph, and the distance between the two fingerprints can be obtained by using the Dijkstra algorithm and getting the monophyletic shortest path. The fingerprint of each adjacent position is taken as a node of graph where the connection line for the edge and the weights of edge represent the distance of position fingerprint.
In Figure
Position fingerprint diagram and connected graph example.
The confirmed location of the interior topology constraints.
The indoor topological structure is basically the same. So, in the offline phase of the fingerprint procedure, one can calculate the shortest distance between two fingerprints’ position and store the results in the database. Thereafter, one can get the new location from the database by using the particle filter under the indoor topology constraints and can get a more accurate estimated position.
Algorithm
measured by target node
(1) (2) (3) The filtering condition is as follows:
% The topology constraints. (4) Until all times’ positioning and tracking is over, one can get the final positioning tracking curve.
To verify the effectiveness of the algorithm above, we simulated the algorithm using MATLAB platform by simulating an indoor corner in the building of the computer school of China University of Mining and Technology. We compare the proposed algorithm that combines the particle filter and topology constraints with the other indoor localization algorithms. The simulated environment and the AP distribution are shown in Figure
Simulation environment (● is an AP node).
Because the anchor node deployment is not dense, the changes of the complexity environment often cause indoor pedestrians’ nodes positioning not always receiving more than three anchor node’s signals. As shown in Figure
In order to evaluate the performance of the proposed algorithm, the algorithm was compared with the traditional KNN algorithm and Kalman filter algorithm. Experimental model parameters are specified as follows: the maximum rate of mobile target node
Accuracy comparison.
Accuracy comparison.
The statistical results of the test data are shown in Table
Tracking results comparison.
Algorithm | Percentage in 2 m error | Percentage in 3 m error | Average error (m) |
---|---|---|---|
KNN | 16.67 | 75.00 | 2.7011 |
Kalman | 38.89 | 88.89 | 2.2400 |
Proposed algorithm | 66.67 | 88.90 | 1.9476 |
Indoor localization is the research hotspot in location based on services. At present, most of the indoor positioning research focuses on anchor nodes deployed sufficiently without considering the change of indoor environment. This may lead to weaker signal that cannot be used to locate. Or anchor nodes’ fault can cause the sparse deployment, which leads to intermittent positioning failure problem. To this end, we put forward an indoor positioning algorithm under sparse anchor nodes by building an indoor radio-frequency fingerprint map and using KNN algorithm to obtain initial position location under the condition of sufficient anchor nodes, while one gets optimal positioning location with sparse anchor node by a series of constraints measures and uses particle filter tracking algorithm to solve nonlinear state space problems. Simulation experiments show that our algorithm can achieve good positioning and tracking results.
The indoor target tracking is a huge and complex engineering, and many issues still remain to be explored. Our ongoing work is as follows: (1) because the anchor placement has direct influence on the tracking accuracy, we will further study the indoor placement problem of anchors; (2) in the indoor environment, there may be multiple targets; thus, we will extend the proposed algorithm for multiple targets tracking; and (3) in the future, we shall consider the detailed hardware implementation and extend this work into the real scenario.
This work was supported by the Fundamental Research Funds for the Central Universities under Grant 2013XK10.