Using EMD-FrFT filtering to mitigate high power interference in chirp tracking radars

This letter presents a new signal processing subsystem for conventional monopulse tracking radars that offers an improved solution to the problem of dealing with manmade high power interference (jamming). It is based on the hybrid use of empirical mode decomposition (EMD) and fractional Fourier transform (FrFT). EMD-FrFT filtering is carried out for complex noisy radar chirp signals to decrease the signal's noisy components. An improvement in the signal-to-noise ratio (SNR) of up to 18 dB for different target SNRs is achieved using the proposed EMD-FrFT algorithm

Precise motion descriptors extraction from stereoscopic footage using DaVinci DM6446

A novel approach to extract target motion descriptors in multi-camera video surveillance systems is presented. Using two static surveillance cameras with partially overlapped field of view (FOV), control points (unique points from each camera) are identified in regions of interest (ROI) from both cameras footage. The control points within the ROI are matched for correspondence and a meshed Euclidean distance based signature is computed. A depth map is estimated using disparity of each control pair and the ROI is graded into number of regions with the help of relative depth information of the control points. The graded regions of different depths will help calculate accurately the pace of the moving target and also its 3D location. The advantage of estimating a depth map for background static control points over depth map of the target itself is its accuracy and robustness to outliers. The performance of the algorithm is evaluated in the paper using several test sequences. Implementation issues of the algorithm onto the TI DaVinci DM6446 platform are considered in the paper

Kernel principal component analysis (KPCA) for the de-noising of communication signals

This paper is concerned with the problem of de-noising for non-linear signals. Principal Component Analysis (PCA) cannot be applied to non-linear signals however it is known that using kernel functions, a non-linear signal can be transformed into a linear signal in a higher dimensional space. In that feature space, a linear algorithm can be applied to a non-linear problem. It is proposed that using the principal components extracted from this feature space, the signal can be de-noised in its input space

Target tracking enhancement using a Kalman filter in the presence of interference

In this paper we present a new target tracking enhancement system that uses a Kalman filter in the presence of interference. If the radar (seeker) is affected by different types of interference, this will affect the missile trajectory towards the target and may cause inaccurate tracking. In the new system a six-state Kalman filter is utilized to perform the tracking task and to carry out smoothing to the corrupted trajectory. This also provides good information about the target velocity in three dimensions which is very important information about the target. A three dimensional scenario between target (with high manoeuvre) and missile is used to illustrate the performance of the system in the case when (i) no interference is present and (ii) interference is present. The performance of the filtered trajectory using the Kalman tracker will be assessed for different guidance methods: including (i) proportional navigation (ii) pure pursuit and (iii) constant bearing. The Kalman improvement for the tacking for the three guidance method will be analysed

Fractional fourier transform based monopulse radar for combating jamming interference

Monopulse radars are used to track a target that appears in the look direction beam width. The distortion produced when manmade high power interference (jamming). Jamming scenarios are achieved by introducing high power interference to the radar processor through the radar antenna main lobe (main lobe interference) or antenna side lobe (side lobe interference). This leads to errors in the target tracking angles that may cause target mistracking. A new monopulse radar structure is presented in this paper which offers a solution to this problem. This structure is based on the use of optimal Fractional Fourier Transform (FrFT) filtering. The proposed system configurations with the optimum FrFT filters is shown to reduce the simulated interfered signal and improve the signal to noise ratio (SNR) in the processors outputs in both processor using the proposed monopulse structure

Enhanced monopulse radar tracking using fractional Fourier filtering in the presence of interference

Monopulse radars are used to track a target that appears in the look direction beam width. Significant distortion is produced when manmade high power interference (jamming) is introduced to the radar processor through the radar antenna main lobe (main lobe interference) or antenna side lobe (side lobe interference). This leads to errors in the target tracking angles that may cause target mistracking. A new monopulse radar structure is presented in this paper which addresses this problem. This structure is based on the use of optimal Fractional Fourier Transform (FrFT) filtering. The improved performance of the new monopulse radar structure over the traditional monopulse processor is assessed using standard deviation angle estimation error (STDAE) for a range of simulated environments. The proposed system configurations with the optimum FrFT filters is shown to reduce the interfered signal and to minimize the STDAE for monopulse processors

A new fractional Fourier transform based monopulse tracking radar processor

Conventional monopulse radar processors are used to track a target that appears in the look direction beam width. The distortion produced when additional targets appear in the look direction beam width can cause severe erroneous outcomes from the monopulse processor. This leads to errors in the target tracking angles that may cause the target tracker to fail. A new signal processing algorithm is presented in this paper that is based on the use of optimal Fractional Fourier Transform (FrFT) filtering to solve this problem. The relative performance of the new filtering method over traditional based methods is assessed using standard deviation angle estimation error (STDAE) for a range of simulated environments. The proposed system configurations with the optimum FrFT filters succeeds in effectively cancelling additional target signals appearing in the look direction beam width

Delaunay triangulation based image enhancement for echocardiography images

A novel image enhancement approach for automatic echocardiography image processing is proposed. The main steps include undecimated wavelet based speckle noise reduction, edge detection, followed by a regional enhancement process that employs Delaunay triangulation based thresholding. The edge detection is performed using a fuzzy logic based center point detection and a subsequent radial search based fuzzy multiscale edge detection. The edges obtained are used as the vertices for Delaunay triangulation for enhancement purposes. This method enhances the heart wall region in the echo image. This technique is applied to both synthetic and real image sets that were obtained from a local hospital

Wavelet-based video codec using human visual system coefficients for 3G mobiles

A new wavelet based video codec that uses human visual system coefficients is presented. In INTRA mode of operation, wavelet transform is used to split the input frame into a number of subbands. Human Visual system coefficients are designed for handheld videophone devices and used to regulate the quantization stepsize in the pixel quantization of the high frequency subbands’ coefficients. The quantized coefficients are coded using quadtreecoding scheme. In the INTER mode of operation, the displaced frame difference is generated and a wavelet transform decorrelates it into a number of subbands. These subbands are coded using adaptive vector quantization scheme. Results indicate a significant improvement in frame quality compared to motion JPEG200

EMD-based filtering (EMDF) of low-frequency noise for speech enhancement

An Empirical Mode Decomposition based filtering (EMDF) approach is presented as a post-processing stage for speech enhancement. This method is particularly effective in low frequency noise environments. Unlike previous EMD based denoising methods, this approach does not make the assumption that the contaminating noise signal is fractional Gaussian Noise. An adaptive method is developed to select the IMF index for separating the noise components from the speech based on the second-order IMF statistics. The low frequency noise components are then separated by a partial reconstruction from the IMFs. It is shown that the proposed EMDF technique is able to suppress residual noise from speech signals that were enhanced by the conventional optimallymodified log-spectral amplitude approach which uses a minimum statistics based noise estimate. A comparative performance study is included that demonstrates the effectiveness of the EMDF system in various noise environments, such as car interior noise, military vehicle noise and babble noise. In particular, improvements up to 10 dB are obtained in car noise environments. Listening tests were performed that confirm the results