Chong Meng Samson See

Validation of the IRI-2012 model with GPS-based ground observation over a low-latitude Singapore station

The ionospheric total electron content (TEC) in the low-latitude Singapore region (geographic latitude 01.37° N, longitude, 103.67° E, geomagnetic latitude 8.5° S) for 2010 to 2011 was retrieved using the data from global positioning system (GPS)-based measurements. The observed TEC from GPS is compared with those derived from the latest International Reference Ionosphere (IRI)-2012 model with three options, IRI-Nequick (IRI-Neq), IRI-2001, and IRI-01-Corr, for topside electron density. The results showed that the IRI-Neq and IRI-01-Corr models are in good agreement with GPS-TEC values at all times, in all seasons, for the year 2010. For the year 2011, these two models showed agreement at all times with GPS-TEC only for the summer season, and for the period 11:00 to 24:00 UT hours (19:00 to 24:00 LT and 00:00 to 08:00 LT) during the winter and equinox seasons. The IRI-2012 model electron density profile showed agreement with constellation observing system for meteorology, ionosphere, and climate (COSMIC) radio occultation (RO)-based measurements around 250 to 300 km and was found to be independent of the options for topside density profiles. However, above 300 km, the IRI-2012 model electron density profile does not show agreement with COSMIC measurements. The observations (COSMIC and GPS) and IRI-2012-based data of TEC and electron density profiles were also analyzed during quiet and storm periods. The analysis showed that the IRI model does not represent the impact of storms, while observations show the impact of storms on the low-latitude ionosphere. This suggests that significant improvements in the IRI model are required for estimating behavior during storms, particularly in low-latitude regions.

An efficient sub-Nyquist receiver architecture for spectrum blind reconstruction and direction of arrival estimation

Spectrum blind reconstruction and direction-of-arrival (DOA) estimation of multiple narrow-band signals spread over wide spectrum sampled at sub-Nyquist sampling rates are considered in this paper. A new sub-Nyquist sampling receiver architecture which requires minimal hardware along with an efficient algorithm for estimation of the parameters and spectrum reconstruction is presented. We further show with the proposed approach, that a minimum average sampling rate of 2(N + 1)B would be sufficient in order to reconstruct the spectrum as well as estimate their corresponding DOA of N narrow-band signals of maximum bandwidth B. Simulation results are also provided which shows the performance very close to the Cramer Rao bound.

Target Tracking in Mixed LOS/NLOS Environments Based on Individual Measurement Estimation and LOS Detection

In this paper, a new method based on estimation and line-of-sight (LOS) detection of individual time-of-arrival (TOA) measurement and Kalman filter (KF) is proposed to track a moving target in mixed line-of-sight and non-line-of-sight (LOS/NLOS) environments. In the proposed tracking algorithm, TOA measurements collected by multiple stationary sensors in a wireless sensor network are used. First, a pseudo-measured position is calculated by choosing the point along the circle defined by a given TOA measurement which has the shortest distance to the predicted position of a moving target. The pseudo-measured position is shown to be an approximately unbiased estimate of the true position of the target. Second, each pseudo-measured position calculated is passed to a detector to be identified as either LOS or NLOS. The average of all the selected LOS pseudo-measured positions is then used as a new pseudo-measurement for the KF to track the moving target. Unlike all the existing target tracking algorithms in mixed LOS/NLOS environments, the proposed tracking algorithm is able to perform target tracking even with just one LOS TOA measurement at a given time instance without prior information of the NLOS noise which may be difficult to obtain in practice. Another advantage of the proposed tracking algorithm is its computational efficiency. Simulation results show that the proposed tracking algorithm performs better than some recent tracking algorithms, particularly in severe mixed LOS/NLOS environments.

Joint Navigation and Synchronization Using SOOP in GPS-Denied Environments: Algorithm and Empirical Study

We consider the problem of tracking a receiver using signals of opportunity (SOOP) from beacons and a reference anchor with known positions and velocities, and where all devices have asynchronous local clocks or oscillators. Based on an extended Kalman filter, we propose a sequential estimator to jointly track the receiver location, velocity, and its clock parameters using time- difference-of-arrival and frequency-difference-of-arrival measurements obtained from the SOOP samples collected by the receiver and reference anchor. Field experiments are carried out using a software defined radio testbed, and Iridium satellites as the SOOP beacons. Experiment demonstrate that our measurement model has a good fit, and our proposed estimator can successfully track both the receiver location, velocity, and the relative clock offset and skew with respect to the reference anchor with good accuracy.

Time-varying filtering and separation of nonstationary FM signals in strong noise environments

Motivated by the existing time-frequency peak filtering (TFPF) algorithm, herein a robust time-varying filtering (RTVF) algorithm is proposed for filtering and separating multicomponent frequency modulation (FM) signals. The performance of the TFPF based on windowed Wigner-Ville distribution is limited by the linear constraint on the waveform of the received signal. The proposed RTVF significantly improves the filtering performance with low complexity by applying a sinusoidal time-frequency distribution, which allows a sinusoidal constraint on the signals waveform. The RTVF can successfully decompose a multicomponent signal into individual components based on an initial instantaneous frequency (IF) estimate of each component. Unlike existing time-varying filters, the RTVF is much less sensitive to the accuracy of the IF estimate, which can be gradually refined by performing an iterative RTVF procedure.

Improved spectrum-blind reconstruction of multi-band signals

This paper considers spectrum-blind reconstruction (SBR) of multi-band signals (MBS) which are sampled with multi-coset sampling (MCS) architecture. A new SBR algorithm which we refer to as Khatri-Rao SBR (KR-SBR) is presented. With this new KR-SBR algorithm, the average MCS rate can be reduced by 50% whilst attaining the same performance as that of the existing state-of-art SBR algorithms. Under certain conditions we will also show that the proposed KR-SBR algorithm has the capacity to achieve SBR when the average MCS rate approaches the Landau sampling rate. Simulation results are also presented to demonstrate the advantages of the proposed KR-SBR algorithm.

Carrier frequency and direction of arrival estimation with nested sub-nyquist sensor array receiver

Carrier frequency and its corresponding direction of arrival (DOA) estimation, at sub-Nyquist sampling rates of narrowband (bandwidth not exceeding B Hz) sources is considered in this paper. We assume M physical sensors arranged in a two dimensional nested sensor array configuration and propose to modify the receiver architecture by inserting an additional delay channel to only the dense sensor array. An efficient subspace based estimation algorithm to estimate the carrier frequencies and their DOAs is also presented. With this proposed approach we show that a minimum ADC sampling frequency of B Hz is sufficient and O(\M/4]2) carrier frequencies and their DOAs can be estimated despite all the carrier frequencies exactly aliased to the same frequency. Furthermore, simulations indicate that when used for spectrum estimation, in addition to carrier frequencies and their DOA estimation, it shows better performance compared to an existing approach using the same M element uniform two dimensional sensor array.

GPS-free Localization Using Asynchronous Beacons

We consider the problem of localizing two sensors using their TDOA measurements of signals from beacons with known positions. Beacons and sensors have asynchronous local clocks or oscillators with unknown clock skews and offsets. We analyze the effect of the clock skews and offsets on the TDOA estimations, and introduce a novel approach using the difference in TDOA (DTDOA) measurements to mitigate the effects of clock skews and offsets on the location estimates. We propose an algorithm for location and velocity estimation based on DTDOA, and perform simulations to verify the robustness of our algorithm to asynchronous local clocks in the beacons and sensors.

Joint frequency and direction of arrival estimation with space-time array

Joint frequency and DOA estimation of more sources than the number of sensors is considered in this paper. We assume a simple uniform linear array (ULA) and propose to employ a multiple delay channel network at every sensor, which can easily be realized by sampling at a slightly higher rate. By combining the outputs of the ULA and the delay network, we show that the manifold matrix is analogous to that of a uniform rectangular array, and hence we appropriately refer to this array as the space-time array. Further, estimation of parameters via the rotational invariance technique (ESPRIT) based algorithm referred to as space-time (ST)-Euler-ESPRIT is proposed. ST-Euler-ESPRIT, similar to well known unitary-ESPRIT provides automatically paired frequencies and their DOAs. We further show that with the proposed approach for a M element ULA and with N-1 delay channel, O(MN) frequencies and their DOAs can be estimated. The performance of ST-Euler-ESPRIT is verified by simulations, where it shows consistently better performance than the unitary-ESPRIT algorithm under noisy conditions.

TDOA-FDOA based multiple target detection and tracking in the presence of measurement errors and biases

This paper considers joint detection and tracking of multiple targets using a sequence of time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements obtained from a pair of receivers. The number of targets, as well as the position and velocity of each target are assumed to be unknown and time-varying. A particle filtering based data association (PFDA) method is employed to detect and track multiple targets. The birth, survival and death models together with the motion model are defined to describe the target dynamics, and different hypotheses on the measurement-target associations are generated and evaluated by using a particle filtering (PF). Since errors and biases can be present in the measurements due to sensor synchronization errors and non line of sight wave propagation, a bank of random walk modes are included to model the biases and errors. The likelihood of the particles are thus computed by combining all these modes so that the biased measurement can also be associated with the target track. Empirical experiments are conducted to demonstrate the performance of our proposed approach.