Novel multipath mitigation methods using a dual-polarization antenna

There are many methods for mitigating GNSS multipath errors. However, none of them completely eliminate the effects of multipath or suit all GNSS applications. A new class of multipath mitigation methods exploit new dual-polarization antenna technology. GNSS signals received direct from the satellites have right-handed circular polarization (RHCP), whereas (singly) reflected signals have left-handed circular polarization (LHCP) or an elliptical polarization that may be expressed as the sum of RHCP and LHCP components. Conventional GNSS user antennas are more sensitive to signals with RHCP, attenuating LHCP signals and reducing, but not eliminating, the multipath errors in the receiver. An antenna with the opposite polarization sensitivity will attenuate the direct signals more than the reflected signals. This can be used to characterizing the reflected signals and thus mitigate the effects of multipath interference.Experimental work using an Antcom dual-polarization antenna and dual geodetic receivers is presented. This verifies that carrier power to noise density, C/N-0, measurements obtained by separately correlating the RHCP and LHCP antenna outputs can be used to distinguish between a low-multipath and moderate-multipath environment. This may be used as the basis of a multipath detection technique.Three different multipath mitigation techniques that use a dual-polarization antenna are proposed. Measurement weighting estimates the code and carrier multipath error standard deviation from the RHCP-LHCP C/N-0 difference and elevation angle. This is used by the navigation processor to discard and reweight measurements. Range-domain multipath correction, uses the pseudo-range, carrier-phase and C/N-0 differences between the outputs of RHCP and LHCP receiver tracking channels, together with antenna calibration data, to estimate corrections to the code and carrier measurements. In tracking-domain multipath mitigation, the RHCP and LHCP correlator outputs are input to common acquisition and tracking algorithms which attempt to separate the direct line of sight and reflected signalsThe design of a novel dual-input GNSS front end, based on direct RF sampling, is presented This will be used, in conjunction with a software GNSS receiver, for future development and testing of multipath mitigation using a dual-polarization antenna

Error Correction For Automotive Telematics Systems

One benefit of data communication over the voice channel of the cellular network is to reliably transmit real-time high priority data in case of life critical situations. An important implementation of this use-case is the pan-European eCall automotive standard, which has already been deployed since 2018. This is the first international standard for mobile emergency call that was adopted by multiple regions in Europe and the world. Other countries in the world are currently working on deploying a similar emergency communication system, such as in Russia and China. Moreover, many experiments and road tests are conducted yearly to validate and improve the requirements of the system. The results have proven that the requirements are unachievable thus far, with a success rate of emergency data delivery of only 70%. The eCall in-band modem transmits emergency information from the in-vehicle system (IVS) over the voice channel of the circuit switch real time communication system to the public safety answering point (PSAP) in case of a collision. The voice channel is characterized by the non-linear vocoder which is designed to compress speech waveforms. In addition, multipath fading, caused by the surrounding buildings and hills, results in severe signal distortion and causes delays in the transmission of the emergency information. Therefore, to reliably transmit data over the voice channels, the in-band modem modulates the data into speech-like (SL) waveforms, and employs a powerful forward error correcting (FEC) code to secure the real-time transmission. In this dissertation, the Turbo coded performance of the eCall in-band modem is first evaluated through the adaptive white Gaussian noise (AWGN) channel and the adaptive multi-rate (AMR) voice channel. The modulation used is biorthogonal pulse position modulation (BPPM). Simulations are conducted for both the fast and robust eCall modem. The results show that the distortion added by the vocoder is significantly large and degrades the system performance. In addition, the robust modem performs better than the fast modem. For instance, to achieve a bit error rate (BER) of 10^{-6} using the AMR compression rate of 7.4 kbps, the signal-to-noise ratio (SNR) required is 5.5 dB for the robust modem while a SNR of 7.5 dB is required for the fast modem. On the other hand, the fading effect is studied in the eCall channel. It was shown that the fading distribution does not follow a Rayleigh distribution. The performance of the in-band modem is evaluated through the AWGN, AMR and fading channel. The results are compared with a Rayleigh fading channel. The analysis shows that strong fading still exists in the voice channel after power control. The results explain the large delays and failure of the emergency data transmission to the PSAP. Thus, the eCall standard needs to re-evaluate their requirements in order to consider the impact of fading on the transmission of the modulated signals. The results can be directly applied to design real-time emergency communication systems, including modulation and coding

Structured Compressed Sensing: From Theory to Applications

Compressed sensing (CS) is an emerging field that has attracted considerable research interest over the past few years. Previous review articles in CS limit their scope to standard discrete-to-discrete measurement architectures using matrices of randomized nature and signal models based on standard sparsity. In recent years, CS has worked its way into several new application areas. This, in turn, necessitates a fresh look on many of the basics of CS. The random matrix measurement operator must be replaced by more structured sensing architectures that correspond to the characteristics of feasible acquisition hardware. The standard sparsity prior has to be extended to include a much richer class of signals and to encode broader data models, including continuous-time signals. In our overview, the theme is exploiting signal and measurement structure in compressive sensing. The prime focus is bridging theory and practice; that is, to pinpoint the potential of structured CS strategies to emerge from the math to the hardware. Our summary highlights new directions as well as relations to more traditional CS, with the hope of serving both as a review to practitioners wanting to join this emerging field, and as a reference for researchers that attempts to put some of the existing ideas in perspective of practical applications.Comment: To appear as an overview paper in IEEE Transactions on Signal Processin

Riesz-Quincunx-UNet Variational Auto-Encoder for Satellite Image Denoising

Multiresolution deep learning approaches, such as the U-Net architecture, have achieved high performance in classifying and segmenting images. However, these approaches do not provide a latent image representation and cannot be used to decompose, denoise, and reconstruct image data. The U-Net and other convolutional neural network (CNNs) architectures commonly use pooling to enlarge the receptive field, which usually results in irreversible information loss. This study proposes to include a Riesz-Quincunx (RQ) wavelet transform, which combines 1) higher-order Riesz wavelet transform and 2) orthogonal Quincunx wavelets (which have both been used to reduce blur in medical images) inside the U-net architecture, to reduce noise in satellite images and their time-series. In the transformed feature space, we propose a variational approach to understand how random perturbations of the features affect the image to further reduce noise. Combining both approaches, we introduce a hybrid RQUNet-VAE scheme for image and time series decomposition used to reduce noise in satellite imagery. We present qualitative and quantitative experimental results that demonstrate that our proposed RQUNet-VAE was more effective at reducing noise in satellite imagery compared to other state-of-the-art methods. We also apply our scheme to several applications for multi-band satellite images, including: image denoising, image and time-series decomposition by diffusion and image segmentation.Comment: Submitted to IEEE Transactions on Geoscience and Remote Sensing (TGRS