This paper presents an enhanced least-squares positioning algorithm for locating and tracking within indoor environments where multipath and nonline-of-sight propagation conditions predominate. The ranging errors are modeled as a zero-mean random component plus a bias component that is assumed to be a linear function of the range. Through minimizing the mean-square error of the position estimation, an expression for the optimal estimate of the bias parameter is obtained. Both range and pseudo-range-based positioning are considered. Simulations and experimentation are conducted which show that a significant accuracy gain can be achieved for range-based positioning using the enhanced least-squares algorithm. It is also observed that the pseudo-range-based least-squares algorithm is little affected by the choice of the bias parameter. The results demonstrate that the experimental 5.8-GHz ISM band positioning system can achieve positional accuracy of around half a meter when using the proposed algorithm.

This paper presents a method of determining the statistical positional accuracy of a moving object being tracked by any 2D (but particularly radiolocation) positioning system without requiring a more accurate reference system. Commonly for testing performance only static positional errors are measured, but typically for radiolocation systems the positional performance is significantly different for moving objects compared with stationary objects. When only the overall statistical performance is required, the paper describes a measurement technique based on determining 1D cross-track errors from a nominal path, and then using this data set to determine the overall 2D positional error statistics. Comparison with simulated data shows that the method has good accuracy. The method is also tested with vehicle tracking in a city and people tracking within a building. For the indoor case, static and dynamic measurements allowed the degrading effect of body-worn devices due to signal blockage to be determined. Error modeling is also performed and a Rayleigh-Gamma model is proposed to describe the radial positional errors. It is shown that this model has a good match with both indoor and outdoor field measurements.

The performance of the peak and leading edge detection methods for time-of-arrival (TOA) estimation in band-limited systems is examined. Analytical expressions for the detection performance in the presence of both random noise and multipath interference are derived. A dimensionless performance factor is presented that allows simple comparisons of the TOA estimation algorithms. These equations allow the performance tradeoff analysis to be undertaken without the need for simulations. It is shown that the leading edge detection method has significantly better multipath mitigation characteristics than the peak detection one, but at the expense of inferior noise performance.

Thin paper presents the performance analysis of time-of-arrival (TOA) measurements by employing bandlimited radio signals. We choose one of the practical TOA estimation methods, peak tracking for study. First, two simplified models, i.e. the hyperbolic and Gaussian models are introduced to approximate the correlation diagram (correlogram) for ease of performance analysis. It is shown that the two models accurately approximate the true bandlimited correlogram especially around the peak. Concise expressions of the TOA estimation errors are derived for either Gaussian measurement noise or multipath interference when using bandlimited signals. The analytical results can be readily exploited to assist the design of TOA based positioning systems using peak tracking algorithm under bandwidth constraints.