A belief propagation (BP) based cooperative integrity monitoring (BP-CIM) algorithm is proposed in this work. BP is widely adopted as the cooperative positioning (CP) estimator, however, the corresponding integrity problem is not solved which restricts its practical application. To guarantee the reliability of a BP-based CP system, our proposed BP-CIM can detect the faulty observations in a distributed approach. Meanwhile, error analysis for BP is performed to derive the CP estimation error bound, which is subsequently used to determine the protection level (PL) of BP-CIM. The simulation and experimental results show that BP-CIM outperforms many existing fault-tolerant CP algorithms in the sides of accuracy and robustness, and the calculated PL can provide a conservative error bound for the estimated CP states. BP-CIM framework can be further extended to many other multi-sensor CP systems to improve the system reliability.

This paper proposes a hybrid robust Gaussian belief propagation (HRGBP) as a fault-tolerant cooperative positioning (CP) system that can be used to support cooperative intelligent transportation applications. For fault-tolerant state estimation, it is well known that fault detection and exclusion (FDE) based methods and Huber’s M-estimation based methods have their own drawbacks when facing different forms of observation outliers, or faults. To solve this problem, our proposed HRGBP uses an interactive multiple model (IMM) framework to fuse these two strategies, which combines the advantages of both methods without their drawbacks. HRGBP can fully exploit the message passing process to mitigate the biased estimates, which further improves the system’s fault-tolerant robustness. HRGBP can be further adapted to fuse more fault-tolerant strategies to improve the robustness of the CP system, and be extended to other factor graph-based methods. Here, we evaluate HRGBP for observations from visual landmark range and bearing, neighboring vehicle range and bearing, and odometer. Our evaluations show that HRGBP outperforms other state-of-the-art CP methods.

Compared with simultaneous localization and mapping (SLAM) problems based on Lidar or visual sensors, range-only SLAM (RO-SLAM) is lacking bearing information. It brings challenges to particle sampling and SLAM estimation. This article proposes an efficient distributed particle filter (EDPF) for RO-SLAM problems. To overcome the difficulties of sampling in a high-dimensional state space, EDPF is decomposed into a set of subparticle filters (sub-PFs) with low dimensionality. Each sub-PF corresponds with an observed beacon, which directly reduces the sampling complexity. A joint weight update method is proposed to exploit the correlation among sub-PFs. It reweighs the particles via an auxiliary distribution after each sub-PF’s local filtering and embodies a better proposal distribution for distributed filtering. In addition, a beacon diagnosis method is proposed, it can detect and reinitialize the wrong converged beacon position estimates, which further reduces the SLAM error accumulation problem. We consider a RO-SLAM system with an odometer and ultrawideband (UWB) to verify the proposed EDPF. Results show that EDPF outperforms many existing RO-SLAM methods, which obtains the best performance with the acceptable computational load.

This letter proposes a message passing enhanced distributed Kalman filter (MP-KF) for cooperative localization (CL). By simplifying the factor graph (FG) model of the traditional belief propagation (BP) algorithm, MP-KF replaces part of the message passing (MP) process in BP with the distributed Kalman filtering. According to the analysis, the computational complexity of MP-KF is lower than that of the traditional BP estimator. The results based on the experimental data set verify the effectiveness and advantages of MP-KF, it outperforms KF-based methods by fully exploiting the correlation inside a CL system, and is better than the BP-based methods by avoiding the performance loss caused by data smoothing. Results also show that MP-KF is a cost-effective approach for CL systems with an acceptable real-time performance, which is suitable for practical CL systems.

Spaceborne global navigation satellite system reflectometry (GNSS-R) techniques have been developed for sea surface wind speed retrieval in recent years. In order to utilize both the leading edge slope (LES) and normalized bistatic radar cross-section (NBRCS), the minimum variance estimator (MVE) is used in the cyclone GNSS (CYGNSS) algorithm for wind retrieval. However, due to the high correlation of two observables, the root mean square error (RMSE) of the MVE estimated winds is not improved significantly. In this article, a new method by combining retrievals from delay-Doppler map (DDM) observables based on particle swarm optimization (PSO) is proposed. LES and NBRCS observables from CYGNSS V2.1 products are used, and then wind retrievals from them are combined by PSO. In order to validate the performance, European Center for Medium-Range Weather Forecasts (ECMWF) and cross-calibrated multi-platform (CCMP) ocean surface wind vector analysis product 10-m ocean surface wind products are used as ground truth. The results show that, when using ECMWF winds, the RMSE of MVE retrievals is 2.21 m/s, while that of PSO is 1.95 m/s: an improvement of 12%; when using CCMP winds, the RMSE of MVE retrievals is 2.15 m/s, while that of PSO is 1.92 m/s: an improvement of 11%. Therefore, we conclude that the PSO algorithm is an improvement on the state-of-the-art MVE GNSS-R-based wind speed retrieval techniques. However, the PSO-based wind retrievals show the dependence on the GPS block type and the CYGNSS satellite identifier that the MVE-based techniques suffer from.

This paper provides an adaptive hybrid robust filter (AHRF) for multi-sensor relative navigation systems that can be used to support cooperative intelligent transport systems. It is known that Huber’s M-estimation based robust filter and the fault detection and exclusion (FDE) based RAIM filter each has its own drawbacks, depending on the nature of the observation error biases. Based on the interactive multiple model (IMM) framework, our proposed AHRF in this paper can take advantage of both filters in a complementary sense. A new adaptive IMM (AIMM) algorithm with Markov transition probability prediction is proposed to allow AHRF to switch efficiently between the two filters. We consider the relative navigation system with Global Navigation Satellite System (GNSS) and ultra-wideband (UWB) as observations to verify AHRF in three cases of possible failure modes and multipath-induced errors. Our results show that AHRF outperforms both the FDE and robust filter in all cases. AHRF framework can be further adapted to include many other fault-tolerant filters to improve the robustness of multi-sensor relative navigation system even further.

A cooperative integrity monitoring (CIM) algorithm is proposed in this work. Under the CIM architecture, the algorithm can fully exploit the global navigation satellite system (GNSS) data and inter-vehicle measurements data to improve the detection and isolation of faulty measurements due to multipath or non line of sight (NLOS). Taking the advantages of cooperative scheme, a residual decomposition method is used to model the measurement errors into common and specific parts, a greedy search strategy is used to exclude the faulty measurements based on its sub-statistics. Simulation results show that CIM has better detection of GNSS fault than traditional receiver autonomous integrity monitoring (RAIM). Also, CIM is capable of detecting the faulty outliers in inter-vehicle measurements. The results indicate that CIM can be applied to many existing multi-sensor cooperative positioning algorithms.

Conventional GNSS-R uses both coherent and noncoherent integration to obtain the Delay-Doppler map (DDM). In the field of phased array angle-of-arrival estimation, this is akin to the conventional beam-forming technique. It is also well known that super-resolution techniques such as MUSIC can obtain higher accuracy than conventional beamforming especially in the case of multiple signals. In this paper, we explore how this technique can be adapted to GNSS-R to obtain high resolution DDM representations. Its primary application will be to improve upon conventional GNSS-R altimetry. Further extrapolation of this result can also be used to isolate the DDM contributions of sea targets from sea clutter that reside within the same iso-delay region of the DDM. Airborne field trials conducted on 4th November 2011 are processed using both conventional GNSS-R and our proposed method that shows a clear improvement in its peak gradient, an important criterion for estimating a signal component's code delay. We further show a real-world example of a DDM formed by two distinct contributions that otherwise would not be detectable using the conventional DDM.

The motion of any potential sea-targets in a sequence of delay Doppler maps (DDMs) collected by a spaceborne global navigation satellite system-reflectometry (GNSS-R) receiver can be computed as a priori information due to the receiver velocity dominating that of any realistic sea-targets. This can be exploited to design a matched filter to detect sea-targets in a DDM sequence without the requirement for any (pre)detection and can be considered a track-before-detect technique. In this article, such a filter is developed and applied to DDMs collected by TechDemoSat-1, and the detection of sea-ice is demonstrated. A frequency analysis of simulated and actual DDM sequences containing a sea-target is carried out. We show that the stationary sea-clutter is bound to the zero time-frequency plane in the 3-D frequency response of the DDM time series, while only $\approx $ 13.8% of the target's energy is located in this plane. This finding motivates the application of an adaptive whitening filter to blindly suppress the temporally correlated clutter in the DDM sequence before applying the matched filter to the prewhitened sequence containing a spatially fixed point-like response arising from the edge of an ice sheet, which is equivalent to a point-like sea-target such as a ship. This results in a signal-to-clutter ratio of $\approx $ 12 dB being achieved when processing only the weak target responses at nonspecular locations originating from the spatially fixed edge of the ice sheet. This is the first technique to exploit the delay and Doppler dynamics of a target to obtain a coherent integration gain.

Location information claimed by devices will play an ever-increasing role in future wireless networks such as wireless vehicular networks, 5G, and the Internet of Things (IoT). Against this background, the verification of such claimed location information will be an issue of growing importance. A formal information-theoretic Location Verification System (LVS) can address this issue to some extent, but such a system usually operates within the limits of idealistic assumptions on a-priori information on the proportions of genuine and malicious users in the field. In this work, we address this critical limitation by using a Neural Network (NN) showing how such a NN based LVS is capable of efficiently functioning even when the proportions of genuine and malicious users are completely unknown a-priori. We demonstrate the improved performance of this new form of LVS based on Time of Arrival measurements from multiple verifying base stations within the context of vehicular networks, quantifying how our NN-LVS outperforms the stand-alone information-theoretic LVS in a range of anticipated real-world conditions. We also show the efficient performance for the NN-LVS when the users' signals have added Non-Line-of-Sight (NLoS) bias in them. This new LVS can be applied to a range of location-centric applications within the domain of the IoT.

Recently, the use of spaceborne GNSS-R for sea-target detection has been demonstrated using DDMs collected by SGR-ReSI onboard TDS-1. These DDMs are windowed around the forward scattering specular point. At this point, the sea-surface response is strongest and it is well suited for remote sensing of Earth's oceans. However, it has been recently shown that the back scattering configuration results in much stronger returns from sea-targets. Combined with a weaker sea-surface response, back scattering is the optimal GNSS-R configuration for target detection. In this paper, we present a method to determine the point on the Earth's surface where the receiver and target appear to be at the same point in the sky so that monostatic / back scattering mechanisms occur. This pseudo monostatic point is then shown to provide complementary scattering statistics and signal dynamics when compared to the forward scattering specular point.

Using spaceborne platforms, stare processing of Delay Doppler Maps (DDM) is a powerful technique that enables up to 20 contiguous DDMs to be combined to estimate the sea state at a single point on the ocean surface. This technique avoids over 100km of blurring in the spatial domain that would occur if the specular window in these contiguous DDMs were averaged. In this paper, we show that due to the spatially variant nature of Woodward's ambiguity function, an adaptive window in the delay Doppler domain can improve the spatial efficiency of the sampling scheme. The nature of the ambiguity free line and the notion that the receiver velocity and incidence angle determine the iso Doppler lines are explored which enable us to derive a window which compensates for the non linear delay field. This window is shown to reduce the variance in the observed stare profile which is expected to improve the accuracy of mean surface slope retrieval algorithms.

Global navigation satellite system reflectometry (GNSS-R) receivers produce delay-Doppler maps (DDMs) by incoherently integrating coherent integration results. Due to system dynamics, during incoherent integration, the receiver aligns each coherent result by tracking the delay and Doppler of the specular point. This is known to cause a blurring of the spatial footprint of the Woodward ambiguity function (WAF) on the reflecting surface. In this paper, we demonstrate that the blurring of the WAF varies over the glistening zone (GZ), and even if a fixed point on the ground is tracked, blurring still occurs. We derive the expressions for the delay and Doppler change rates over the GZ and then predict the error introduced by range walk for typical GNSS-R scatterometry configurations. We find that ≈6 dB of loss is expected for a point scatterer near the edge of the GZ when a fixed point on the surface is tracked. The incoherent range walk compensation (IRWC) method is then presented for GNSS-R receivers to mitigate this loss. The IRWC method focuses the power in the DDM to the isodelay and iso-Doppler configuration occurring at the midpoint of the integration time. DDMs produced by tracking a fixed point with and without IRWC are simulated, and errors are found to be in agreement with those predicted. Spatial domain GNSS-R products will be improved with IRWC. Target detection will benefit from a larger usable swath, allowing longer tracking and detection times as a result of the increased target to clutter and noise ratio. At the same time, imaging applications will no longer suffer from a spatially variant blurring of the WAF, which limits the resolution of the estimated products. IRWC is shown to mitigate the range migration losses and improve the SNR of an imaging GNSS-R receiver by ≈6 dB near the edge of the GZ.

On TechDemoSat -1, the attitude uncertainty can be significant during eclipse, leading to large errors in the estimated antenna gain towards the specular point and the geophysical parameter inversion methods that rely on this knowledge. DDMs produced by spaceborne GNSS-R receivers are typically asymmetrical due to the projection of the receivers antenna pattern over the glistening zone. Thus, an asymmetric DDM contains information about the satellite attitude. In this paper, we investigate the potential use of asymmetric DDMs in attitude determination.

Due to lack of GPS signals' indoor coverage, the implementation of Signals of Opportunity (SoOp) such as FM signals, Wi-Fi, Bluetooth, RFID, and Cellular Networks for indoor localization has been considered. Not all SoOp are good performers because of some constraints; for instance, lack of accuracy, higher cost in deployment, additional hardware requirements. In this research, a new method of indoor localization is developed using AM radio signalsand thereceived signal strength fingerprinting technique. The fingerprinting technique in this study uses the deterministic approach and three algorithms, Nearest Neighbour, K-Nearest Neighbour, and K-Weighted Nearest Neighbour to measure the mean distance error as the indicator of positioning accuracy. The outcome shows that the minimum mean distance error at the ground and first floor is less than 3m and the Nearest Neighbour algorithm performed best with K=1.

Global navigation satellite systems (GNSS) and, in particular, the global positioning system (GPS) have become ubiquitous in safety critical infrastructure. Vulnerability of GNSS to radio frequency interference (RFI) from either intentional (jamming) or unintentional sources is an ever growing concern. Hence, GNSS itself has become critical infrastructure which must be protected and its vulnerability to interference alleviated. As the RFI source is unknown a priori, passive localization systems are required; this adds an order of difficulty when compared with transmitter location systems with known and cooperative sources. The need for rapidly localizing the RFI leads to sensor network techniques which consist of spatially distributed sensor nodes (SNs). The localization systems typically use the received signal strength (RSS), source angle of arrival (AOA)/ direction of arrival (DOA), time difference of arrival (TDOA) or a combination of AOA(DOA)/TDOA or frequency difference of arrival (FDOA) measurements to estimate the RFI position. This paper provides an overview of existing systems from the literature and a comparison of these different interference geo-localization techniques.

This paper assesses the drawbacks in reconfigurability and resource consumption of the conventional baseband signal processing circuitry for modern Global Navigation Satellite Systems (GNSSs) signals. The involvement of new GNSSs leads to the requirement of designing a programmable multi-GNSS baseband receiver that can be reconfigured across GNSS signals. Paralleling baseband circuits is the easiest approach, but resource consumption will significant. Hardware time multiplexing is more effective technique. It, however, requires an efficient memory hierachy and an efficient code generator design. These challenges are tackled in the proposed architecture which utilises as 3.2%, 6.6%, 12.5% and 50% resources as the conventional baseband circuitry consisting of 16 GPS L1 C/A channels, 8 BEIDOU B1I channels, 4 GALILEO E1 channels, and 1 GPS L5 channel, respectively. Results are also justified by mathematical analysis.

GNSS are now being used for more than just location and navigation. Using GNSS remote sensing technology such as GNSS radio occultation and GNSS reflectometry, advanced GNSS receivers could not only precisely calculate position, but also study the Earth's atmosphere, oceans, land surface, ice, etc. Also, CubeSats attract a wide range of applications for scientific use, including GNSS remote sensing. The paper discusses the possibility and research challenges of using miniaturised space-borne GNSS receivers accommodated and operated on a CubeSat bus to achieve those scientific observations. Efforts by ACSER, including its “UNSW EC0” 2U CubeSat and space qualified GNSS receiver design, are also presented.

This paper describes a novel remote sensing technique for deformation monitoring. This system consists of two parts: a set of radio beacons and a telemetering receiver. The radio beacons are installed apart on the large structure. These beacons have the same carrier modulated by the same PN codes with different delays that exceed one chip period. The distance between any two PN codes is unique so as to distinguish the beacons from each other. The telemetering receiver receives the mixed signal from all the beacons, and uses de-correlation to recover every beacon's carrier. Phase detectors measure the phase difference between all other carriers and the reference carrier. The micro-deformation value Δd is proportional to the phase difference Δθ, and then the deformation curve can be drawn out. The simulation result has proved that the technique proposed by this paper is correct.

Before the occurrence of the landslide, there's always some micro displacement. This paper puts forward a novel kind of landslide monitoring system based on the spread-spectrum continuous wave radar. This system consists of two parts: corner reflector and radar instrument. The continuous carrier signal is modulated by pseudo noise code and radiated to the corner reflector and then returned to the radar instrument in the radical direction inversely. After de-spreading the signal, we can recover the echo carrier signal. The phase difference variance between these two carrier signals is proportional to the radical displacement of the corner installed on the land slope. The simulation has proved that compared with traditional continuous wave micro displacement monitoring radar, it has better performance with the very simple structure.

This study investigates identifying forest condition changes using Global Navigation Satellite System (GNSS) signal measurements obtained in a recently conducted airborne experiment. Received signal strength (i.e. the peak correlation power) of the reflected signal is used to distinguish ground surfaces or forest conditions from each other. A simple threshold based method is developed to perform the identification of forest condition change. Through processing the logged airborne experimental data, it is observed that GNSS signal power can be used to reliably and accurately identify abnormal conditions in a forest.

Position information is a fundamental requirement for many vehicular applications such as navigation, intelligent transportation systems (ITSs), collision avoidance, and location-based services (LBSs). Relative positioning is effective for many applications, including collision avoidance and LBSs. Although Global Navigation Satellite Systems (GNSSs) can be used for absolute or relative positioning, the level of accuracy does not meet the requirements of many applications. Cooperative positioning (CP) techniques, fusing data from different sources, can be used to improve the performance of absolute or relative positioning in a vehicular ad hoc network (VANET). VANET CP systems are mostly based on radio ranging, which is not viable, despite being assumed in much of the literature. Considering this and emerging vehicular communication technologies, a CP method is presented to improve the relative positioning between two vehicles within a VANET, fusing the available low-level Global Positioning System (GPS) data. The proposed method depends on no radio ranging technique. The performance of the proposed method is verified by analytical and experimental results. Although the principles of the proposed method are similar to those of differential solutions such as differential GPS (DGPS), the proposed technique outperforms DGPS with about 37% and 45% enhancement in accuracy and precision of relative positioning, respectively.

Intelligent transportation systems (ITSs) are increasingly being considered to mitigate the impacts of road transportation, including road injuries, energy waste, and environmental pollution. Vehicular positioning is a fundamental part of many ITS applications. Although global navigation satellite systems (GNSSs), e.g., Global Positioning System (GPS), are applicable for navigation and fleet management, the accuracy and availability of GNSSs do not meet the requirements for some applications, including collision avoidance or lane-level positioning. Cooperative positioning (CP) based on vehicular communications is an approach to tackle these shortcomings. The applicability of vehicular CP techniques proposed in the literature is questionable due to viability issues, including internode distance estimation, which is an important part of many CP techniques. Conventional CP systems such as differential GPS (DGPS) and other augmentation systems are also effectively incapable of addressing the given ITS applications. In this paper, modern and conventional CP systems are discussed, and the viability of radio ranging/range rating and constraints of vehicular communications as main pieces of modern CP systems are investigated. The general performance boundaries for modern CP systems are explained, as is the gap existing between the positioning accuracy required for crucial ITS applications and what modern CP can provide. This is followed by introduction of a novel trend for vehicular CP research, which is a potential reliable solution using a modified concept of real-time kinematic (RTK) GPSs for vehicular environments.

Cooperative positioning (CP) is an approach for positioning and/or positioning enhancement among a number of participants, which communicate and fuse their position-related information. Due to the shortcomings of Global Navigation Satellite Systems (GNSSs), modern CP approaches are considered for improving vehicular positioning where the GNSS cannot address the requirements of the specific applications such as collision avoidance or lane-level positioning. An inertial navigation system (INS) has not been considered for CP in the literature. The hybrid INS/GNSS methods used for positioning enhancement in standalone nodes cannot be classified as CP because the position-related data are not communicated between at least two independent entities. In this paper, we present a novel CP technique to improve INS-based positioning in vehicular networks. This cooperative inertial navigation (CIN) method can be used to enhance INS-based positioning in difficult GNSS environments, such as in very dense urban areas and tunnels. In the CIN method that is proposed, vehicles communicate their inertial measurement unit (IMU) and INS-based position data with oncoming vehicles traveling in the opposite direction. Each vehicle fuses the received data with those locally observed and the carrier frequency offset (CFO) of the received packets to improve the accuracy of its position estimates. The proposed method is analyzed using simulations and is also experimentally verified. The experimental results show up to 72% improvement in positioning over the standalone INS-based method.

Relative positioning among vehicles is a fundamental parameter for advanced applications of intelligent transportation systems such as collision avoidance and road safety. However, the level of positioning accuracy achievable using Global Navigation Satellite Systems does not meet the requirements of these applications. Cooperative positioning (CP) techniques can be used for improving the performance of absolute or relative positioning in a vehicular ad hoc network (VANET). The tight integration of Global Positioning System (GPS) data among communicating vehicles has already been introduced by the authors as a tight CP approach with specific advantages over differential GPS (DGPS) for relative positioning. In this paper, we propose an enhanced tight CP technique adding low-cost inertial navigation sensors and GPS Doppler shifts. Based on analytical and experimental results, the new method outperforms its predecessor and DGPS by 10% and 24%, respectively.

Spatial synchronization and its importance in the imaging performance of bistatic synthetic aperture radar (SAR) systems has been explored in several recent studies. In this paper, attitude measurement errors (AME) are studied in the context of motion measurement errors (MME) since the transmitting and receiving antennas are attached to rigid bodies that undergo both translational and rotational motion. As long as the antenna phase center (APC) is not colocated with the body's center of mass or along the axis of rotation, AMEs induce APC position errors and subsequently phase errors. The primary parameters that determine the magnitude and impact of these phase errors on imaging performance are the radar carrier frequency, imaging duration, types of AMEs, and antenna configuration. As such, certain system parameters and antenna configurations minimize the resultant phase errors from AMEs.

In this paper we investigate near sea surface wind speed estimation using GNSS (Global Navigation Satellite System) signals. A low-altitude airborne experiment was conducted recently using a UNSW-owned light aircraft over the coast of Sydney. Both direct and reflected signals were captured via a zenith-looking antenna and a nadir-looking antenna respectively. The logged IF data bits were processed to generate delay waveforms and delay-Doppler waveforms. Using the measured waveforms and the theoretical ones, the wind speed can be estimated through waveform fitting. The processed waveforms associated with eight satellites were employed to estimate the wind speed. A four-step method is proposed to perform the waveform fitting. The results show that similar estimation accuracy can be achieved using signals transmitted from satellites with low or high elevation angles. It is demonstrated that the estimation accuracy of the wind speed is around 1 m/s. Further the incorrect encoding of some quantized data bits in a software receiver is investigated.

Cooperative Positioning (CP) techniques are used to enhance the performance of positioning through sharing position-related data among a number of agents. These agents are usually users with mobility and, possibly, the infrastructure node(s). In CP systems, position-related data are shared among the participating nodes using a communication medium. Inter-node distance is a common parameter considered in CP techniques, especially for those in the environments with limited Global Navigation Satellite System (GNSS) visibility including dense urban areas and indoor environments. Radio ranging based on Received Signal Strength (RSS) is popular among researchers for its simplicity. However, the accuracy of this method is far beyond the requirements of CP systems. Here, we introduce Carrier Frequency Offset (CFO) as a potential observable to improve RSS ranging. Regardless of the content of the data communicated among the agents, RSS and CFO always exist in the communication signal. Therefore, the results of this work can be applied to improve the performance of any other CP method. Here, a hybrid CFO-RSS ranging method is presented to improve the accuracy of RSS ranging. The experimental results show up to 80% accuracy improvement over RSS using the proposed hybrid CFO-RSS technique. Although the examples used here are for outdoor situations, the outcomes are directly applicable indoors or any situation where GNSS signals are not available.

There are over 1.2 million Australians registered as having vision impairment. In most cases, vision impairment severely affects the mobility and orientation of the person, resulting in loss of independence and feelings of isolation. GPS technology and its applications have now become omnipresent and are used daily to improve and facilitate the lives of many. Although a number of products specifically designed for the Blind and Vision Impaired (BVI) and relying on GPS technology have been launched, this domain is still a niche and ongoing R&D is needed to bring all the benefits of GPS in terms of information and mobility to the BVI. The limitations of GPS indoors and in urban canyons have led to the development of new systems and signals that bridge the gap and provide positioning in those environments. Although still in their infancy, there is no doubt indoor positioning technologies will one day become as pervasive as GPS. It is therefore important to design those technologies with the BVI in mind, to make them accessible from scratch. This paper will present an indoor positioning system that has been designed in that way, examining the requirements of the BVI in terms of accuracy, reliability and interface design. The system runs locally on a mid-range smartphone and relies at its core on a Kalman filter that fuses the information of all the sensors available on the phone (Wi-Fi chipset, accelerometers and magnetic field sensor). Each part of the system is tested separately as well as the final solution quality.

Location fingerprinting in WiFi positioning has been widely used in indoor environments. The key issue of the fingerprinting technology is the fingerprint database. The disadvantages of this technology are the database generation and maintenance requirements. The conventional method to create the database is that people carry out the survey manually (that is what the commercial products are doing). When the environment changes significantly (such as after a building renovation, or moving of furniture), the database has to be rebuilt. This paper proposes a new method to build and maintain the database in an efficient manner. This method only requires persons to carry a specific device which consists of a Radio Frequency Identification (RFID) reader and WiFi scanner to log the coordinates and the WiFi signal strengths. The coordinates are provided by some pre-deployed medium range (1–2 meters) RFID tags in the building with a location technique based on ‘cell ID’. As the persons conducting the survey are moving around the area of interest for purposes other than the fingerprint survey (such as a security guard who regularly patrols the whole building anyway), so the fingerprint database can be generated “automatically”. Also, the database can be refined as the data are being accumulated. When the environment changes, it can be detected by the self-refining database. A preliminary test was carried out for the proposed method. The results show that it works well.

The use of magnetic field variations for positioning and navigation has been suggested by several researchers. In most of the applications, the magnetic field is used to determine the azimuth or heading. However, for indoor applications, accurate heading determination is difficult due to the presence of magnetic field anomalies. Here location fingerprinting methodology can take advantage of these anomalies. In fact, the more significant the local anomalies, the more unique the magnetic “fingerprint”. In general, the more elements in each fingerprint, the better for positioning. Unfortunately, magnetic field intensity data only consists of three components. Since true north (or magnetic north) is generally unknown, even with help of the accelerometer to detect the direction of the gravity, only two components can be extracted, i.e. the horizontal intensity and the vertical intensity (or total intensity and inclination). Furthermore, moving objects containing ferromagnetic materials and electronic devices may affect the magnetic field. Tests were carried out to investigate the feasibility of using magnetic field alone for indoor positioning. Possible solutions are discussed.

With increasing user demands on Location-based Services (LBS) and Social Networking Services (SNS), indoor positioning has become more crucial. Because of the general failure of GPS indoors, non-GNSS navigation technologies are essential for such areas. Wireless Local Area Networks (WLAN) have widely been employed for indoor localisation based on the Received Signal Strength (RSS)-based location fingerprinting technique. The fingerprinting technique stores the location-dependent characteristics of a signal collected at known locations ahead of the system's use for localisation in a database. When positioning, the user's device records its own vector(s) of signal strength and matches it against the pre-recorded database of vectors by applying pattern matching algorithms. Location is then calculated based on the best matches between the new and stored vectors. We examined the relationship between the measured Manhattan Distance (MD), Euclidean Distance (ED), and other vector distances over the geometric distance between Reference Points (RPs) in a fingerprint database. The correlation between geometric and vector distance was poor. However, because “nearest neighbor” algorithms are used, only short vector distances are important. Furthermore, the measured RSSs varied much more as a function of distance (due to fast fading) than it did as a function of time at a single test point. Hence, the difference between variances measured at two test points was not a good indicator of the measured difference in signal strength. This led to the current investigation of very short geometric distances. In this paper, a new algorithm is applied to examine data from locations at very short ranges from each other and to investigate the relationship between vector distance and the geometric distance in closer areas in order to observe the nature of the relationship between short-range fingerprints. The experimental test bed was carried out in a large furnished office. Two west-east and south-north lines in a cross shape with 4m length are considered. We find that even at short distances, variation due to fading dominates and using other vector distances instead of MD or ED can help decrease the effect of such variation in positioning.

Despite over a decade of intensive research and development, the problem of delivering an effective indoor navigation system to the blind and vision impaired (BVI) remains largely unsolved. In an attempt to strengthen and improve future research efforts, we define a set of criteria for evaluating the success of a potential navigation device. In order to give complete coverage, the Requirements Analysis has been broken down into the subcategories of positioning accuracy, robustness, seamlessness of integration with varying environments and the nature of information that is outputted to a BVI user. We then apply this framework to a number of existing navigation solutions for the BVI, drawing upon the notable achievements that have been made thus far and also the crucial issues that remain unresolved or are yet to receive attention. It was found that these key issues, which existing designs fail to overcome, can be attributed to the need for a new focus and user centred design attitude - one which incorporates universal design, recognises the uniqueness of its audience and understands the challenges associated with the systems/devices intended environment.

A novel approach is proposed to enable vehicles to estimate their instantaneous position with lane-level accuracy, which is not achievable using Global Navigation Satellite Systems (GNSSs). This system can be used to enhance position-based applications such as Intelligent Transportation Systems (ITSs), including navigation and lane-level traffic guidance and warning. The system uses the carrier frequency offset (CFO) of the dedicated short-range communication (DSRC) signal, broadcast by two infrastructure beacons. The main advantages of the proposed method over other potential technologies such as radio-frequency identification (RFID) and buried loops are its simpler required infrastructure and functionality using vehicular communication platforms. Analysis of the proposed technique indicates acceptable reliability and performance. Empirical test results confirm the viability of the method.

Indoor positioning has become highly important because of the failure of GPS in such areas. Many Wireless Local Area Networks (WLAN) indoor localization studies use the fingerprinting technique. In this study, a new positioning system is proposed based on broadcast FM as a signal of opportunity, with significant benefits for indoor positioning. This localization system uses FM signal strength fingerprinting. The deterministic approach of fingerprinting is considered, and several algorithms are compared. The results demonstrate a minimum mean distance error of 2.96m for the K-Weighted Nearest Neighbors (KWNN) algorithm with K=6. The comparison between using fingerprinting for FM and Wi-Fi is also discussed.

Intelligent Transportation Systems (ITS) are developing to increase transportation efficiency and mitigate its negative impacts on society. Traffic flow monitoring, traffic guidance and traffic signal control are some of ITS applications relying on vehicle passage rate in the streets. There are already some conventional techniques for vehicle counting. In this work a novel method is proposed for vehicle passage and lane detection based on the relative acceleration between the vehicle and two anchor nodes which broadcast packets periodically using Dedicated Short Range Communication (DSRC), the nominated medium for vehicle-vehicle and vehicle-infrastructure communication. Depending on the speed of the vehicle, the performance of the proposed method is 95% or higher.

It is well known that a global positioning system (GPS) receiver needs to ‘see’ at least four satellites to provide a three-dimensional fix solution. But in difficult environments such as an urban canyon, the number of ‘visible’ satellites is often not enough. Wireless fidelity (WiFi) signals have been utilised for positioning mainly based on the fingerprinting technology. However, the accuracy of WiFi positioning outdoors is from several tens of metres to more than one hundred metres. This study proposes a new methodology to integrate WiFi positioning technology and GPS to improve positioning accuracy in urban canyons. When only two GPS satellites are visible, the pseudorange observations can be used to generate a time difference of arrival (TDOA) measurement. The TDOA generates a hyperboloid surface which can be intersected with the surface of the Earth and shows the possible location of the user on that line of position. Integrating this method with the WiFi fingerprinting technology can increase the positioning performance significantly. The test results show that the positioning accuracy can be improved by more than 50% if the new method can be applied.

The purpose of this paper is to describe a campus-wide indoor and outdoor positioning system developed at the School of Surveying and Spatial Information Systems at the University of New South Wales, Sydney, Australia. The system uses a Wi-Fi positioning technique known as `fingerprinting' to locate users indoors, and GPS to locate users outdoors. Fingerprinting first requires the building up of a database of signal strengths from different Wi-Fi access points taken at different points across the area of interest. Then, the user scans the signal strengths in the wireless network and sends the data to the database, which will find the closest match, and return the likeliest location of the user. Our work investigates different approaches to database generation and fingerprint matching, and their impact on system performance. We find that reducing the time for the survey phase by half does not impact significantly on the final accuracy of the system. The system is also tested outdoors to investigate whether Wi-Fi can challenge GPS for outdoor positioning, and we find that Wi-Fi can match, and sometimes outperform GPS in terms of accuracy.

This paper discusses the resource and power requirements of baseband signal processing circuitry to process new Global Navigation Satellite Systems (GNSS) signals. The large signal bandwidth, multiple longer spreading codes and the split-spectrum modulations demand wider registers and wider accumulators at higher operating frequencies compared to the baseband hardware of the existing Global Positioning System (GPS) L1 Coarse/Acquisition (C/A) signal. Some of the signals with the memory spreading codes have a completely new requirement of up to 0.5 M memory bits. Implementation of the core baseband signal processing blocks in FPGA hardware reveals up to three times the resource requirement and up to thirty seven times the power consumption for high-end signals compared to the core baseband module of the existing L1 C/A signal.

Global Navigation Satellite Systems (GNSS) can be used for navigation purposes in vehicular environments. However, the limited accuracy of GNSS makes it unsuitable for applications such as vehicle collision avoidance. Improving the positioning accuracy in vehicular networks, Cooperative Positioning (CP) algorithms have emerged. CP algorithms are based on data communication among vehicles and estimation of the distance between the nodes of the network. Among the variety of radio ranging techniques, Received Signal Strength (RSS) is very popular due to its simplicity and lower cost compared to other methods like Time of Arrival (TOA), and Time Difference of Arrival (TDOA). The main drawback of RSS-based ranging is its inaccuracy, which mostly originates from the uncertainty of the path loss exponent. Without knowing the environment path loss exponent, which is a time-varying parameter in the mobile networks, RSS is effectively useless for distance estimation. There are many approaches and techniques proposed in the literature for dynamic estimation of the path loss exponent within a certain environment. Most of these methods are not functional for mobile applications or their efficiency decreases dramatically with increasing mobility of the nodes. In this paper, we propose a method for dynamic estimation of the path loss exponent and distance based on the Doppler Effect and RSS. Since this method is fundamentally based on the Doppler Effect, it can be implemented within networks with mobile nodes. The higher the mobility of the nodes, the better performance of the proposed technique. This contribution is important because vehicles will be equipped with Dedicated Short Range Communication (DSRC) in the near future.

Fingerprinting is a technique that records vectors of received power from several transmitters, and later matches these to a new measurement to position the new user. This paper examines data from locations at very short ranges from each other in order to observe the nature of the relationship between short-range fingerprints. We find that even at short distances, variation due to fading dominates and a suitable signal model has yet to be produced. We also conclude that fingerprints should be averaged over a range near the point of interest.

Code tracking linear range and the code phase tracking jitter have always been opposing ends of the design criteria of a code lock loop in direct-sequence spread-spectrum communication systems. New modulation techniques used in Global Navigation Satellite Systems (GNSS) offer better code tracking jitter and multipath performance by sharpening the correlation triangle. However the linear tracking range is reduced which directly results in poor dynamics and low signal strength performance of the tracking loop. The problem is most severe in the sophisticated Alternate Binary Offset Carrier (AltBOC) modulation. This paper proposes a method to extend a loop's tracking range without affecting the jitter performance in the existing linear range of the code tracking loop.

802.11 positioning systems are established as a low-cost solution to positioning within context aware computing. Prior research has mostly focused on either indoor positioning within commercial environments, where access points are prevalent, or outdoors positioning over large areas, using discovered networks to augment GPS. In this paper we test 802.11 positioning in a medium sized domestic house with only a small number of access points, examining its suitability in a domestic context aware computing system. We use a signal strength fingerprint map approach, in which empirical signal strength data is gathered over the area prior to use. Estimation then involves comparing the online input to the map, and selecting the best position, and direction, based on similarity. We compare different strategies for gathering signal strength, and implementations of the Nearest Neighbor and Bayesian methods. Our results demonstrate that with good placement, only two access points are sufficient to estimate position with an error of less than 4 meters 90% of the time.

This paper acts as the introduction to the Special Session "Circuits for Satellite Navigation Receivers" at ISCAS 2007. It summarises the challenges faced by circuit designers when trying to exploit the new signals available from GPS, Glonass and Galileo. These challenges occur from end to end of the receiver - from the antenna to the positioning software.

2006 17th British Machine Vision Conference, BMVC 2006 --

This letter concerns the design of multiplierless implementations of finite impulse response (FIR) filters to achieve minimum adder cost. Existing approaches include dependence-graph multiplier-block methods and Common Subexpression Elimination (CSE) techniques applied either to the Canonic Signed Digit (CSD) representation of the coefficients or to other Signed Digit (SD) representations. We introduce a new CSE algorithm, which searches a bounded number of Minimal Signed Digit (MSD) representations. The performance of existing algorithms and the new algorithm is compared. It is shown that the relative performance of different algorithms depends on filter length and wordlength and that the new algorithm gives significant improvements in some cases.

This letter presents an algorithm for generating all of the signed-digit representations of a given integer. There is an infinite number of representations, so the tree algorithm is terminated when a defined number of digits above the minimum possible is reached. It is shown that by having available these other representations, more efficient multiplication circuits can be designed than when using existing algorithms.

This work describes a part of a malarial image processing system for detecting and classifying malaria parasites in images of Giemsa stained blood slides in order to evaluate the parasitaemia of the blood. A major requirement of the system is an efficient method to segment cell images. This paper introduces morphological approach to cell image segmentation more accurate than the classical watershed-based algorithm. We applied grey scale granulometries based on opening with disk-shaped elements, flat and non-flat. We used a non-flat disk-shaped structuring element to enhance the roundness and compactness of the red cells improving the accuracy of the classical watershed algorithm, while we have used a flat disk-shaped structuring element to separate overlapping cells. These methods make use of knowledge of the red blood cell structure that is not used in existing watershed-based algorithms.

This work introduces a morphological method for detecting malaria parasites in images of Giemsa stained blood slides. Generally, blood images are made up of three different kinds of cells: red, white and blood platelets. These are distinguished by their dimensions and their colour. In malarial blood the red corpuscles of vertebrates are infected by malaria parasites. The aim of our system is to detect the parasites by means of an automatic thresholding based on a morphological approach, using granulometries to evaluate the size of the red cells and the nuclei of parasites, and the regional maxima to mark the nuclei of parasites.

This paper is concerned with low complexity (e.g. IC area or gate count) implementation of fixed-point IIR digital filters. Low-sensitivity structures, such as wave structures, require smaller fixed-point wordlengths. For this reason, they have been proposed for minimizing filter complexity. However, filter coefficient multipliers can be implemented using multiplier blocks. Traditional structures, such as the direct form, benefit greatly from the use of multiplier blocks, and a statistical study is reported showing that they then become comparable to (or better than) wave structures in complexity. The cascade structure is found to be the most efficient.

Methods that use multiplier blocks to evaluate the coefficients of an FIR filter are shown to be far superior, in terms of the number of adders required, to the use of binary or CSD representation. Results from two new algorithms, one of which is highly likely to be optimal under certain known conditions, are discussed.>