EURASIP Journal on Advances in Signal Processing

We investigate the problem of bearings-only tracking of manoeuvring targets using particle filters (PFs). Three different (PFs) are proposed for this problem which is formulated as a multiple model tracking problem in a jump Markov system (JMS) framework. The proposed filters are (i) multiple model PF (MMPF), (ii) auxiliary MMPF (AUX-MMPF), and (iii) jump Markov system PF (JMS-PF). The performance of these filters is compared with that of standard interacting multiple model (IMM)-based trackers such as IMM-EKF and IMM-UKF for three separate cases: (i) single-sensor case, (ii) multisensor case, and (iii) tracking with hard constraints. A conservative CRLB applicable for this problem is also derived and compared with the RMS error performance of the filters. The results confirm the superiority of the PFs for this difficult nonlinear tracking problem.

The Doppler shift of low-speed targets is frequently disturbed by the reverberation Doppler spread clutter under the shallow sea. The clutter is generated by underwater scatterers, which increases the difficulty of Doppler estimation. To solve this problem, a reverberation target resolution function based on the Doppler spread clutter statistical model is proposed in this paper. Through the width of reverberation Doppler clutter, this function adjusts the waveform parameters by determining whether the target is discriminable. In addition, the reverberation Doppler spread clutter is time-spatial varying and affected by grazing angle, waves, wind speed, fish and other effects. Thus, the sonar waveform parameters need to be adjusted constantly. Therefore, this paper combines the cognitive sonar based on reinforcement learning with the reverberation target resolution function to evaluate different waveforms in different environments. Consequently, the sonar can adjust the waveform parameters in real-time and obtain the optimal waveform in different environments. Meanwhile, in this paper, the action selection strategy of Dyna-Q reinforcement learning is optimized, and the model-based maximum action selection Dyna-Q algorithm (Dyna-Q-Max-Action) is proposed. Compared with the traditional Dyna-Q and Q-learning algorithms, the proposed algorithm needs fewer episodes. Finally, numerical simulation verified the effectiveness of the proposed algorithm.

Two approaches are presented in this paper to improve the quality of digital images over the sensor resolution using super-resolution techniques: iterative super-resolution (ISR) and noniterative super-resolution (NISR) algorithms. The results show important improvements in the image quality, assuming that sufficient sample data and a reasonable amount of aliasing are available at the input images. These super-resolution algorithms have been implemented over a codesign video compression platform developed by Philips Research, performing minimal changes on the overall hardware architecture. In this way, a novel and feasible low-cost implementation has been obtained by using the resources encountered in a generic hybrid video encoder. Although a specific video codec platform has been used, the methodology presented in this paper is easily extendable to any other video encoder architectures. Finally a comparison in terms of memory, computational load, and image quality for both algorithms, as well as some general statements about the final impact of the sampling process on the quality of the super-resolved (SR) image, are also presented.

Supporting multiple active speakers in automotive hands-free or speech dialog applications is an interesting issue not least due to comfort reasons. Therefore, a multi-channel system for enhancement of speech signals captured by distributed distant microphones in a car environment is presented. Each of the potential speakers in the car has a dedicated directional microphone close to his position that captures the corresponding speech signal. The aim of the resulting overall system is twofold: On the one hand, a combination of an arbitrary pre-defined subset of speakers’ signals can be performed, e.g., to create an output signal in a hands-free telephone conference call for a far-end communication partner. On the other hand, annoying cross-talk components from interfering sound sources occurring in multiple different mixed output signals are to be eliminated, motivated by the possibility of other hands-free applications being active in parallel. The system includes several signal processing stages. A dedicated signal processing block for interfering speaker cancellation attenuates the cross-talk components of undesired speech. Further signal enhancement comprises the reduction of residual cross-talk and background noise. Subsequently, a dynamic signal combination stage merges the processed single-microphone signals to obtain appropriate mixed signals at the system output that may be passed to applications such as telephony or a speech dialog system. Based on signal power ratios between the particular microphone signals, an appropriate speaker activity detection and therewith a robust control mechanism of the whole system is presented. The proposed system may be dynamically configured and has been evaluated for a car setup with four speakers sitting in the car cabin disturbed in various noise conditions.

In weak GNSS signal environments, extending integration time is paramount to improving the GNSS receiver’s sensitivity. Furthermore, sufficient coherent integration can help to mitigate multipath and cross-correlation false locks and avoid squaring loss. However, extending integration time is limited by the navigation message data bit, if present. The maximum likelihood (ML) estimation method has been shown as the most effective way to estimate the navigation bit boundary locations (i.e., bit synchronization) and subsequently estimate the data bit values (i.e., bit decoding) in the presence of noise alone. In this paper, the performance of ML bit synchronization and decoding is systematically assessed as a function of the number of data bits, the effect of Doppler error and received signal power in different tracking modes (i.e., phase-locked mode and frequency-locked mode). In addition, the theoretical performance models of ML bit synchronization and decoding are developed based on statistical theory. The experimental validation of the developed performance models and analyses is reported. For GPS L1 C/A signals, it is shown that for ML bit synchronization, using 100 data bits, the successful synchronization rate (SSR) can reach to about 100% with C/N0 as low as 20 dB-Hz with no Doppler error. The performance degradation caused by Doppler error is not significant if the Doppler error is within 5 Hz, and with the maximum tolerance of 25 Hz, while for ML bit decoding, the successful decoding rate (SDR) of the 2-bit sequence can reach to about 100% with C/N0 as low as 25 dB-Hz with no Doppler error. The performance degradation caused by Doppler error is not significant if the Doppler error is within 2 Hz. Both theoretical and simulation results establish that the upper bound of Doppler error for a 2-bit sequence is 12.5 Hz.

We propose a new application of information hiding by employing the digital watermarking techniques to facilitate the data annotation in traffic surveillance videos. There are two parts in the proposed scheme. The first part is the object-based watermarking, in which the information of each vehicle collected by the intelligent transportation system will be conveyed/stored along with the visual data via information hiding. The scheme is integrated with H.264/AVC, which is assumed to be adopted by the surveillance system, to achieve an efficient implementation. The second part is a Region of Interest (ROI) rate control mechanism for encoding traffic surveillance videos, which helps to improve the overall performance. The quality of vehicles in the video will be better preserved and a good rate-distortion performance can be attained. Experimental results show that this potential scheme works well in traffic surveillance videos.

A novel approach is presented for synthetic aperture radar (SAR) image segmentation. By incorporating the advantages of maximally stable extremal regions (MSER) algorithm and spectral clustering (SC) method, the proposed approach provides effective and robust segmentation. First, the input image is transformed from a pixel-based to a region-based model by using the MSER algorithm. The input image after MSER procedure is composed of some disjoint regions. Then the regions are treated as nodes in the image plane, and a graph structure is applied to represent them. Finally, the improved SC is used to perform globally optimal clustering, by which the result of image segmentation can be generated. To avoid some incorrect partitioning when considering each region as one graph node, we assign different numbers of nodes to represent the regions according to area ratios among the regions. In addition, K-harmonic means instead of K-means is applied in the improved SC procedure in order to raise its stability and performance. Experimental results show that the proposed approach is effective on SAR image segmentation and has the advantage of calculating quickly.

An original low-voltage current-mode high-accuracy function synthesizer circuit will be presented, allowing to implement a multitude of continuous mathematical functions. The dynamic range is strongly extended as a result of the superior-order approximation of the implemented functions. The current-mode operation and the independence of the circuit performances on technological parameters are responsible for an additional improvement of structure accuracy. The advantages of reduced design costs per function represent an immediate consequence of the multiple functions realized by the proposed structure. The approximation error of the original function synthesizer circuit is 0.3% for an extended range of the input signal. The function synthesizer is designed for implementing in 0.18 μm CMOS technology and it is supplied at 1 V. An original application of the proposed function synthesizer circuit is represented by a new fourth-order approximation exponential function generator, having a dynamic range of approximately 33 dB, for an error smaller than 1 dB.