In wireless networks, radio-map based locating techniques are commonly used to cope the complex fading feature of radio signal, in which a radio-map is built by calibrating received signal strength (RSS) signatures at training locations in the offline phase. However, in severe hostile environments, such as in ship cabins where severe shadowing, blocking and multi-path fading effects are posed by ubiquitous met allic architecture, even radio-map cannot capture the dynamics of RSS. In this paper, we introduced multiple feature radio-map location method for severely noisy environments. We proposed to add low variance signature into radio map. Since the low variance signatures are generally expensive to obtain, we focus on the scenario when the low variance signatures are sparse. We studied efficient construction of multi-feature radio-map in offline phase, and proposed feasible region narrowing down and particle based algorithm for online tracking. Simulation results show the remarkably performance improvement in terms of positioning accuracy and robustness against RSS noises than the traditional radio-map method.
A more accurate approach is to utilize the speed difference of signal propagation to measure distances from transmitters to receivers, so as to conduct indoor locating more accurately @cite_10 @cite_3 . Ultrasound and acoustic signals are the generally exploited low speed signals. In the case of measuring time of arrival, the transmitter broadcasts low-speed signal (ultrasound or acoustic) and RF signal simultaneously. The receiver receives the RF signal to synchronize timer with the transmitter and then measures the traveling time of the low-speed signal to estimate distance from the transmitter. Only when a receiver @math is within the communication range of the low speed signal (denoted by @math , and generally small) of the transmitter @math , can a distance @math be measured. When a set of distances, which is denoted by @math are obtained, least square estimation or multilateration is applied for position calculation. TOA-based positioning can provide centimeter level positioning accuracy @cite_6 . But because the short transmission range of the low speed signals (ultrasound and acoustic), and the requirement of more than three non-collinear distances for location estimation, TOA-based positioning requires dense deployment of TOA beacons, which poses high cost to the positioning system.
@cite_10 @cite_6 @cite_3
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We propose and investigate a compressive architecture for estimation and tracking of sparse spatial channels in millimeter (mm) wave picocellular networks. The base stations are equipped with antenna arrays with a large number of elements (which can fit within compact form factors because of the small carrier wavelength) and employ radio frequency (RF) beamforming, so that standard least squares adaptation techniques (which require access to individual antenna elements) are not applicable. We focus on the downlink, and show that “compressive beacons,” transmitted using pseudorandom phase settings at the base station array, and compressively processed using pseudorandom phase settings at the mobile array, provide information sufficient for accurate estimation of the two-dimensional (2D) spatial frequencies associated with the directions of departure of the dominant rays from the base station, and the associated complex gains. This compressive approach is compatible with coarse phase-only control, and is based on a near-optimal sequential algorithm for frequency estimation which approaches the Cramer Rao Lower Bound. The algorithm exploits the geometric continuity of the channel across successive beaconing intervals to reduce the overhead to less than 1 even for very large ( @math ) arrays. Compressive beaconing is essentially omnidirectional, and hence does not enjoy the SNR and spatial reuse benefits of beamforming obtained during data transmission. We therefore discuss system level design considerations for ensuring that the beacon SNR is sufficient for accurate channel estimation, and that inter-cell beacon interference is controlled by an appropriate reuse scheme. A robust and accurate positioning solution is required to increase the safety in GPS-denied environments. Although there is a lot of available research in this area, little has been done for confined environments such as tunnels. Therefore, we organized a measurement campaign in a basement tunnel of Linkoping university, in which we obtained ultra-wideband (UWB) complex impulse responses for line-of-sight (LOS), and three non-LOS (NLOS) scenarios. This paper is focused on time-of-arrival (TOA) ranging since this technique can provide the most accurate range estimates, which are required for range-based positioning. We describe the measurement setup and procedure, select the threshold for TOA estimation, analyze the channel propagation parameters obtained from the power delay profile (PDP), and provide statistical model for ranging. According to our results, the rise-time should be used for NLOS identification, and the maximum excess delay should be used for NLOS error mitigation. However, the NLOS condition cannot be perfectly determined, so the distance likelihood has to be represented in a Gaussian mixture form. We also compared these results with measurements from a mine tunnel, and found a similar behavior. An asynchronous position measurement system is proposed for indoor localization in this paper. The demonstrated system consists of a UWB transmitter and several energy detection receivers whose positions are known. The position measurement process starts with the locator emitting a UWB pulse. Upon arrival, the pulse is amplified and retransmitted by the target to be located. Signals from both the locator and the target are captured by the receivers. No synchronization mechanism is implemented. Instead, the proposed system measures the differential TOA between the direct coupling signal of the locator and the target signal. Together with the knowledge of the locator transmitter and receiver positions, the absolute range that the pulse travels can be calculated. The sum of transmitter-target range and target-receiver range defines an ellipse and the target resides on the intersections of several such ellipses. It is shown via the Cramer-Rao lower bound that the absolute-range-based elliptical localization is potentially more accurate than the relative-range-based hyperbolic localization. Our proposed system is able to achieve positioning error bound comparable to synchronous absolute-range-based localization systems while eliminating the cost of synchronization. We consider the joint optimization of beamformers and linear receivers in a MIMO interference network. Each transmitter transmits a single beam corresponding to a rank-one precoder. When the number of users K is greater than the number of antennas at each terminal N, the maximum degrees of freedom is achieved via spatial interference alignment. Interference alignment is feasible for up to K = 2N−1 users, in which case there is a finite number of solutions to the alignment conditions. This number of solutions increases rapidly with N, and the solutions depend only on the cross-channel coefficients (i. e., they are independent of the direct channels). To maximize the achievable sum rate at high SNRs we therefore wish to select an aligned solution which is best matched to the direct channels. We evaluate the performance of this scheme for large K and N, assuming that the solution is the best out of a random subset of aligned solutions. We then compare numerically this performance with the performance of previously proposed numerical (e. g., forward-backward) techniques for optimizing beams, and a new technique which tracks the local optimum as the SNR is incrementally increased, similar to a homotopy method for improving convergence properties. We observe that the incremental technique typically achieves better performance than the previously proposed methods.