The detector is an integral part of the device for receiving and processing radio signals. Signals that have passed through the ionospheric channel acquire an unknown Doppler shift and are subject to dispersion distortions. It is necessary to carry out joint detection and parameter estimation to improve reception quality and detection accuracy. Modern hardware base developing makes it possible to implement a device for joint detection and evaluation of signals based on standard processors (CPU) and graphic processors (GPU). The article discusses the implementation of a signal detector that allows for real-time operation. A comparison of implementations of algorithms for estimating the Doppler frequency shift through multiplication by a complex exponent and the fast Fourier transform (FFT) is performed. A comparison of computational costs and execution speed on the CPU and GPU is considered.

/pdf/computationally-efficient-implementation-of-joint-detection-64py6kz4.pdf

Computationally Efficient Implementation of Joint Detection and Parameters Estimation of Signals with Dispersive Distortions on a GPU

Integral imaging is a promising technology for 3-D TV and 3-D display. In this paper, a theoretical analysis of 3-D integral imaging systems is performed in the frame of the Wigner distribution formalism. It is shown that the entire intensity distribution in the pick-up image plane of these systems can be obtained from a single 2-D Wigner distribution function of a single lenslet pupil. This result reveals the Wigner distribution function as a powerful tool for analysis of 3-D integral imaging systems with different pupil functions. As an example, the extension of the depth of field of an integral imaging system with lenslets having amplitude modulation (central obscuration) is proposed

Analysis of 3-D integral imaging displays using the Wigner distribution

The use of spatial filtering is considered to isolate a single propagation mode of an unknown narrowband signal received as a superposition of multipath components by a sensor array for the purpose of source waveform estimation. In the problem addressed, multipath propagation from source to receiver occur due to diffuse scattering from a number of distinct but extended regions of an irregular medium rather than ideal specular reflection. The combined presence of diffuse scattering and array calibration uncertainties may cause the spatial signatures of the incident distributed signal modes to deviate significantly from the plane-wave array manifold. Furthermore, the temporal signature of the waveform to be recovered may also be quite arbitrary, with no known deterministic or statistical properties that can be utilised for multipath separation. The lack of knowledge regarding the propagation channel, array manifold and source waveform poses a major challenge for the task at hand. This study proposes a new blind spatial filtering technique, referred to as the generalised estimation of multipath signals (GEMS) algorithm, which can effectively separate distributed signal modes for accurate source waveform estimation. This algorithm actually exploits the presence of multipath propagation and wavefront distortions as the basis for signal separation. The performance of GEMS is compared with two benchmark signal-copy procedures using real data from an experimental finite impulse response single-input multiple-output high frequency (HF) system.

An adaptive filtering algorithm for blind waveform estimation in diffuse multipath channels

A novel method based on pre-weighted Zoom FFT with cascaded filter algorithm is proposed for minimizing the processing burden of cross-ambiguity functions without affecting performance. In our work, Zoom FFT technique is employed and the weighting process is performed previously to get rid of the redundancy of the computation. The multistage filtering technique is adopted to reduce the complexity and gain high performance of the system. A segment processing method is also proposed, which is tailored for calculation of the ambiguity function when the input data frames are overlapped. By regarding the calculation of cross-ambiguity functions of overlapped data frames as the calculation of FFT of overlapped data, the redundancy of computation are eliminated.

Research on Fast Computation of Ambiguity Function

According to the characteristics of non-cooperative bistatic pulse radar systems and the issue that the doppler mismatched loss of the cross-correlation detection of moving targets is serious, two fast methods for joint time delay and doppler shift estimation of moving targets, based on segment correlation-FFT processing and modified partial segment correlation-FFT processing, are proposed, after analyzing the common methods for cross-ambiguity function processing such as direct calculation, FFT, Zoom-FFT method The processing performance of direct calculation and partial segment correlation-FFT processing of LFM pulse-series is analyzed in theory and by simulation And the simulation shows that the weak moving target can be detected effectively and the time delay and doppler shift can be fast estimated by the methods presented in this paper

Time delay and doppler shift estimation accuracy analyses of moving targets in non-cooperative bistatic pulse radar

This paper addresses the problem of fast computation of the ambiguity function using a new method based on pre-weighted zoom FFT algorithm, which employs zoom FFT technique and performs the weighting process previously and thus gets ride of the extra computation. The computational complexity of the presented algorithm is compared with other methods and the simulation results are given. Both the theory and the simulations show that, pro weighted zoom FFT method has lower computational complexity, less memory need and negligible error, and can meet the need of real-time processing.

http://ieeexplore.ieee.org/iel5/9834/30995/01442195.pdf

Yong-feng Ma

Papers

Fast computation of the ambiguity function