One problem of growing importance in indoor environments is the location of people, mobile terminals and equipment. Indoor radio channels suffer from extremely serious multipath and non-line of sight (NLOS) conditions that have to be modeled and analysed to enable the design of radio equipment for geolocation applications. Since telecommunication and geolocation applications have different objectives, existing radio channel models developed for telecommunications are not appropriate, and different models and techniques have had to be developed to provide adequate and accurate localisation.
The prior art reveals wireless geolocation applications where the location system gathers parametric information, for example the received signal strengths (RSS), angles of arrival (AOA), times of arrival (TOA) or time differences of arrival (TDOA) and processes this information to form a location estimate. In indoor environments where signal propagation is very complex, these existing parametric geolocation techniques (and combinations thereof) provide only limited location accuracy, as they depend largely on Line of Sight (LOS) to ensure accuracy, an element which of course is largely not present in indoor environments. The major errors in measurement are introduced during the extraction of the location dependent metrics, and are due primarily to the indoor environment. As a result, the lines of position (LOP) do not intersect due to these errors, thereby resulting in large estimation errors. Additionally, multiple measurements are invariably needed in order to obtain a two-dimensional position.
Geolocation based on a received signals' fingerprint have proven more accurate at determining location in indoor environments. Due to interference introduced by natural or man-made objects, which tend to cause a transmitted signal to break up into a number of different paths, each transmitted signal has a unique signature, or fingerprint, by the time it reaches a given receiver dependant on the location of the transmitter and the receiver.
The process of geolocation based on the received signals' fingerprint is composed of two phases, a phase of data collection (off-line phase) and a phase of locating a user in real-time (real-time phase).
The first phase consists of recording a set of fingerprints as a function of the user's location, covering the entire zone of interest. During the second phase, a fingerprint is measured by a receiver and compared with the recorded fingerprints of the database. A pattern matching algorithm (positioning algorithm) is then used to identify the closest recorded fingerprint to the measured one and hence to infer the corresponding user's location.
To constitute a “signature” pattern or a fingerprint, several types of information can be used such as, received signal strength (RSS), received angular power profile (APP) and received power delay profile (PDP) or channel impulse response (CIR).
On the other hand, several types of pattern-matching algorithms may be used in the fingerprinting technique, which have the objective to give the position of the mobile station with the lowest location error. The most popular algorithms are based on the:                nearest neighbour(s) in signal space (location estimate defined as the lowest Euclidean, Box-Cox or statistical metric in signal space); or        cross-correlation between signal vectors (location estimate defined as the highest correlation coefficient between signal vectors).        
It has to be noted that the accuracy of the method is primarily a function of the reproducibility and uniqueness of the estimated set of fingerprint information. Reproducibility means the achievement of almost the same estimated set of fingerprint information in one location for different observation times. Uniqueness means that the set of fingerprint information in one location is relatively different from the one in another location (no aliasing in the signature patterns).
Several geolocation systems, using fingerprinting techniques, have been deployed in both indoor and outdoor environments. The main differences between these systems are the types of fingerprint information and the types of pattern matching algorithms.
RADAR™, is an RF network-based system for locating and tracking users inside buildings and uses RSS (narrowband measurements) fingerprint information gathered at multiple receiver locations to determine the user's co-ordinates. The system, operating with WLAN technology, has a minimum of three access points (fixed stations) and covers the entire zone of interest.
The pattern-matching positioning algorithm consists of the nearest neighbour(s) in signal space. The minimum Euclidean distance (in signal space), between the observed RSS measurements and the recorded set of RSS measurements, computed at a fixed set of locations, gives the estimated user's location.
DCM™, is an RF handset-based system for locating and tracking users in a metropolitan outdoor environment. The mobile terminal that needs to be located performs measurements of signal strength (narrowband measurements) received from the serving cell and six strongest neighbours. The gathered information is then sent to a location server, where the location of the user is estimated and this estimate is sent back to the application server. Other types of signal information (cell ID, propagation time delay) can also be used within the network. The system, operating with the GSM Cellular technology, has several fixed stations and covers the entire zone of interest.
A simple correlation algorithm is used to estimate the user's location. A best match search, between the observed RSS measurements by the mobile station and the recorded set of RSS measurements in the location server, is computed at a fixed set of locations and the MS's location is estimated.
It has to be noted that, since DCM™ is a handset-based location system, its implementation involves some software modifications of the mobile terminal in order to enable the retrieval of received signal characteristics.
In the framework of the WILMA project, RSS fingerprinting techniques are used to locate users in a building with a WLAN infrastructure. The pattern-matching algorithm involved is an artificial neural network, which consists of a multi-layer perceptron (MLP) architecture with 3, 8 and 2 neurones in the input, hidden and output layers respectively to achieve the generalisation needed when confronted with new data, not present in the training set.
RadioCamera™ is an RF network-based system for locating and tracking users in a metropolitan outdoor environment. It uses multipath angular power profile (APP) information gathered at one receiver to locate the user's coordinates. The system, operating with cellular technology, has one-antenna array per cell (fixed station) and covers the entire zone of interest. The pattern-matching algorithm, used to estimate the user's location, consists of the nearest neighbour(s) in signal space. The minimum statistical (Kullback-Liebler) distance (in signal space), between the observed APP measurements and the recorded set of APP measurements, computed at a fixed set of locations, gives the estimated user's location (see, for example, U.S. Pat. No. 6,112,095 for Signature Matching for Location Determination in Wireless Communication Systems which is incorporated herein by reference).
DCM™, operating with UMTS technology and using CIR as fingerprint information, is the second RF handset-based system for locating and tracking users in a metropolitan outdoor environment. It has several fixed stations and covers the entire zone of interest. To form the database, a set of fingerprints is modeled by computing the radio channel impulse responses (CIR) with a ray-tracing tool. The magnitudes of these impulse responses or the power delay profiles (PDP) are calculated (after setting a threshold value in order to reduce contributions of noise power and interference from other codes) from each fixed station to each receiving point corresponding to the user's location. The mobile terminal that needs to be located performs measurements of channel's impulse responses (wideband measurements).
The magnitude of the impulse response from the strongest fixed station is correlated with the content of its database (pattern-matching algorithm) at the location server. The receiving point with the highest correlation coefficient is taken to represent the co-ordinates of the mobile station.
Measured channel impulse responses are used for database collection and for location estimation algorithm. The system performs an outdoor geolocation using GSM and UMTS technologies.
The pattern-matching algorithm involved is based on the nearest neighbour in signal space. The minimum Box-Cox distance between the observed CIR measurements and the CIR measurements contained in the database gives the estimated user's location.
The accuracy and coverage of the geolocation systems, using the fingerprinting technique, depend on the resolution and the size of the database. Calibration measurement and database maintenance are essential in the operation of these systems. Moreover, the search methodology, involved in the pattern-matching algorithm should be efficient to minimise the time needed for the localisation.
Systems, using RSS fingerprinting technique (RADAR™ and WILMA for indoor, DCM™ for outdoor), require the involvement of several fixed stations to compute the user's location. Moreover, RSS yield a great amount of variation for a small location deviation implying a reproducibility concern, which may degrade the location accuracy.
The system, using APP fingerprinting technique, requires the use of an antenna array with high angular resolution for indoor geolocation since the scatterers are around both the transmitter and the receiver.
Systems, using CIR or PDP fingerprinting technique, have the advantage of being reproducible and respecting the uniqueness property, especially when the localisation is done on a continuous basis (user's tracking).
A signature based on the impulse response of the channel appears to give the best location accuracy for an indoor geolocation. However, its implantation involves the use of a wideband receiver.
On the other hand, the pattern-matching algorithm used in RADAR™, DCM™ and RadioCamera™ systems may show a lack of generalization (an algorithm that gives an incorrect output for an unseen input), a lack of robustness against noise and interference, a lack of pattern match in some situations (i.e. the Euclidean distance can be minimized without having the match of the two patterns) and a long search time needed for the localization (done during the real-time phase) especially when the size of the environment or the database is large. Hence, the use of an artificial neural network (ANN), as the pattern-matching or positioning algorithm, is essential to the enhancement of the geolocation system.
As a measure of performance, the median resolution of the location estimation for indoor and outdoor geolocation systems, using fingerprinting techniques, is reported to be in the range of 2 to 3 meters and 20 to 150 meters respectively.