Future weather radar systems will need to provide rapid updates within a flexible multifunctional overall radar network. This naturally leads to the use of electronically scanned phased array antennas. However, the traditional multifaced planar antenna approaches suffer from having radiation patterns that are variant in both beam shape and polarization as a function of electronic scan angle; even with practically challenging angle-dependent polarization correction, this places limitations on how accurately weather can be measured. A cylindrical array with commutated beams, on the other hand, can theoretically provide patterns that are invariant with respect to azimuth scanning with very pure polarizations. This paper summarizes recent measurements of the cylindrical polarimetric phased array radar demonstrator, a system designed to explore the benefits and limitations of a cylindrical array approach to these future weather radar applications.

Digital beamforming (DBF) has long been heralded as the next frontier in phased array technology, and not without reason. The digitization of transmit and receive signals at the element level opens the door to new processing and beamforming schemes and promises to deliver maximum flexibility and unprecedented dynamic range in large systems. However, it is not without inherent technological risks and practical challenges associated with the amount of data to process and the use of less sophisticated transceivers. This paper provides broad overviews of several interrelated aspects of the resulting DBF trade spaces for these systems. In particular, emerging concepts are highlighted for the roles and interconnection of distributed beamforming/processing for fixed and adaptive beamforming. These are then related to digital array calibration mechanisms that can potentially reduce the need for data- and processing-intensive beamforming algorithms.

With the decreasing cost of phased array antennas, their use for weather surveillance is becoming more practical. A significant advantage of phased arrays that can be applied to weather surveillance is spatial filtering. Using adaptive nullforming to spatially filter clutter is a novel approach to clutter mitigation, which is not possible with conventional parabolic reflector antennas. Moreover, spatial filtering is also applicable to phased-array-specific techniques such as beam multiplexing and adaptive scanning when only a few pulses are available for processing; this situation is particularly challenging for conventional ground clutter filters. The National Weather Radar Testbed Phased Array Radar (NWRT PAR) provides an opportunity to test some of these new capabilities. In this paper, a linearly constrained minimum power algorithm with an additional quadratic constraint is applied to weather data collected using the NWRT PAR and its multichannel receiver. Both the original algorithm and a recursive least squares version are utilized to show reflectivity and velocity data where both weather and ground clutter are present. Doppler spectra from selected range gates are examined to illustrate the performance of adaptive nullforming. Issues such as the number of samples needed to estimate the covariance matrix are explored. As far as we know, this is the first time that these types of techniques have been used to mitigate ground clutter contamination on a weather surveillance radar.

The second generation Digital Beamforming SAR (DBSAR-2) is a state-of-the-art airborne L-band radar being developed at the NASA Goddard Space Flight Center (GSFC). The instrument employs a 16-channel radar architecture characterized by multi-mode operation, software defined waveform generation, digital beamforming, and configurable radar parameters. The instrument has been design to support several disciplines in Earth and Planetary sciences. This technology seeks to establish the Next Generation SAR as a science instrument while setting a path future airborne and spaceborne SAR missions.

This paper formulates and solves a multiple real-constant multiplication (MRCM) problem, where the goal is to find a multiplierless shift-add network that implements the multiplication of a signal by real constants, in a way which minimizes hardware cost subject to error constraints. Unlike our previous work, here we consider an improved hardware cost model, whereby instead of simply counting the number of adders needed, we count the number of bits required, which is more realistic. We also introduce two algorithms for solving the MRCM problem, namely: (i) a greedy algorithm without backtracking that heuristically traverses an exponentially growing search tree and rapidly produces a suboptimal solution; and (ii) an exact algorithm that exploits known cost upper bounds to determine whether and how to expand the search tree, leading to an optimal solution at the expense of longer execution time. Finally, we present a set of experimental results that compares the two algorithms with the Hcubalgorithm.

Targets of interest in video acquired from imaging infrared sensors often exhibit profound appearance variations due to a variety of factors, including complex target maneuvers, ego-motion of the sensor platform, background clutter, etc., making it difficult to maintain a reliable detection process and track lock over extended time periods. Two key issues in overcoming this problem are how to represent the target and how to learn its appearance online. In this paper, we adopt a recent appearance model that estimates the pixel intensity histograms as well as the distribution of local standard deviations in both the foreground and background regions for robust target representation. Appearance learning is then cast as an adaptive Kalman filtering problem where the process and measurement noise variances are both unknown. We formulate this problem using both covariance matching and, for the first time in a visual tracking application, the recent autocovariance least-squares (ALS) method. Although convergence of the ALS algorithm is guaranteed only for the case of globally wide sense stationary process and measurement noises, we demonstrate for the first time that the technique can often be applied with great effectiveness under the much weaker assumption of piecewise stationarity. The performance advantages of the ALS method relative to the classical covariance matching are illustrated by means of simulated stationary and nonstationary systems. Against real data, our results show that the ALS-based algorithm outperforms the covariance matching as well as the traditional histogram similarity-based methods, achieving sub-pixel tracking accuracy against the well-known AMCOM closure sequences and the recent SENSIAC automatic target recognition dataset.

This article presents a joint framework for quantization and Multiple Constant Multiplication (MCM) optimization, which yields a computationally efficient implementation of multiplierless multiplication in hardware and software. Frameworks of this nature have been developed in the context of Finite Impulse Response (FIR) filters, where frequency response specifications are used to drive the design. In this work, we look at a general case, considering as given a vector of ideal, real constants, which may come from any application and do not necessarily represent FIR filter coefficients. We first formulate a joint optimization problem for finding a fixed-point vector and a shift-add network that are optimal in terms of quantization error and MCM complexity. We then describe ways to finitize and prune the search space, leading to an efficient algorithm called JOINT_SOLVE that solves the problem. Finally, via extensive randomized experiments, we show that our joint framework is notably more computationally efficient than a disjointed one, reducing the MCM cost by 15%--60% on moderate size problems.

The focus of this paper is to provide a framework for the joint optimization of both the coefficient quantization and multiple constant multiplication (MCM) problems. It is known that while the MCM problem is complete in the subspace of integer constants, it is incomplete and not optimal in the real world where the MCM constants are often noninteger. In these situations there is flexibility in how constants are quantized in digital circuits that can be leveraged. There is a gap in the current literature between the methods of optimally quantizing constants into integers and the methods of optimizing circuits when integer constants are given. Here we show a potential for tremendous benefit when both of these problems are solved using one unified problem framework.

Weather radar products from the U.S. National Weather Service (NWS) are used by the government and private sectors. Very high resolution radar data are increasingly being utilized in real time. However, the bandwidth needed to transmit these data (termed level-II super-resolution data) from the radar to the destination site is a limiting issue. General-purpose compression programs are not tuned to the properties of weather radar data. As the NWS continues to upgrade the capabilities of radar network, the amount of data will continue to increase. As a result, compression is of vital interest to keep down maintenance, storage, and transmission costs. A method for lossless compression of these data on a radial-by-radial basis focusing on the delta (difference) between range bins of super-resolution radar data is presented and is called super-resolution delta compression (SRDC). There are several specialized aspects of SRDC that are based on the properties of weather radar data. SRDC was tested on level-II reflectivity product data from several S-band Doppler weather radars in the NWS network and was compared with two general-purpose compression programs and a different weather-radar-specific compression approach. The results show that the newly developed SRDC yield is approximately 17% better than the next best approach and approximately 47% better than only preprocessed radials.

An image segmentation algorithm using an alternating erosion/dilation technique called strong point analysis (SPA) is introduced for general-purpose feature detection. The ability to associate and group pixels with the salient features of an image allows computers to consider images not as an array of values but as a collection of objects. This enables other algorithms to perform advanced tasks, such as tracking an object in a time series of images. The qualitative needs for proper tracking of storm cells in radar images are discussed. To test SPA for those qualities, radar reflectivity images from three S-band weather radars were used. The algorithm is demonstrated to identify features fairly consistently over a time series of images, as well as exhibiting well-behaved changes to its output with respect to changes to the algorithm's input parameters.

A major difficulty that plagues the practical use of Slepian-Wolf (SW) coding (and distributed source coding in general) is that the precise correlation among sources needs to be known a priori. To resolve this problem, we propose an adaptive asymmetric SW decoding scheme using particle based belief propagation (PBP). We explain the adaptive scheme for asymmetric setup in detail and then further extend it to the non-asymmetric setup based on the code partitioning approach. Moreover, we introduce a Metropolis-Hastings (MH) algorithm in the resampling step, which efficiently decreases the number of simulation iterations. We show through experiments that the proposed algorithm can simultaneously reconstruct the compressed sources and estimate the joint correlation among sources. Further, comparing to the conventional SW decoder based on standard belief propagation, the proposed approach can achieve higher compression under varying correlation statistics.

Belief propagation (BP) is a powerful algorithm to decode low-density parity check (LDPC) codes over additive white Gaussian noise (AWGN) channels. However, the traditional BP algorithm cannot adapt efficiently to the statistical change of SNR in an AWGN channel. This paper proposes an adaptive scheme that incorporates a particle filtering (PF) algorithm into the BP based LDPC decoding process. The proposed scheme is capable to perform online estimation of time-varying SNR at the bit-level and enhance the BP decoding performance simultaneously.

For the first time, we perform normalized correlation template tracking in the modulation domain. For each frame of the video sequence, we compute a multi-component AM-FM image model that characterizes the local texture structure of objects and backgrounds. Tracking is carried out by formulating a modulation domain correlation function in the derived feature space. Using visible and longwave infrared sequences as illustrative examples, we study the performance of this new approach relative to two basic pixel domain correlation template trackers. We also present preliminary results from a new dual domain tracker that operates simultaneously in both the pixel and modulation domains.

A new approach for the clear air velocity estimation in weather radar is presented. A combination of nonparametric with parametric spectral analysis allows us to identify and extract multiple processes caused by different scatterer types within a single radar resolution volume. An example of clear air observed using an S-band dual polarization radar is presented. Heretofore, migrating birds and wind-blown insects that are mixed within each resolution volume caused such data to be unusable for meteorological interpretation. In this paper, we construct power spectral densities of polarimetric variables. We use the polarimetric spectral densities to differentiate the scatterer types within the observed radar resolution volume. We demonstrate how our combination of non-parametric and parametric spectral analysis can be used to retrieve the true wind velocity in situations with severe contamination by biological scatterers.

We present a new particle filter (PF) algorithm, which uses a mathematical tool known as Galerkin's projection method to generate the proposal distribution. By definition, Galerkin's method is a numerical approach to approximate the solution of a partial differential equation. By leveraging this method with L2theory and the FFT, this new proposal is fundamentally different to various local linearization or Kalman filter based proposals. We apply this algorithm to a bearings-only tracking problem. As shown in the theory and indicated by our simulations, this proposal renders more support from the true posterior distribution, thereby significantly enhances the estimation accuracy compared to standard bootstrap filters. In addition, because of this improved proposal distribution, the new particle filter can achieve a given level of performance with less sample size.

Dirty paper code (DPC) can be used in a number of communication network applications; broadcast channels, multiuser interference channels and ISI channels to name a few. We study various implementation bottlenecks and issues with implementing a DPC pre-coder based on nested trellis technique. The aim is to achieve a practical hardware realization of the precoder for wireless LAN/DSL applications. We describe the architectural development process and realization of the precoder on a Xilinx Virtex 2V8000 FPGA. To the best of our knowledge this is the first reported DPC pre-coder hardware implementation.

We present a new multi-rate architecture for decoding irregular LDPC codes in IEEE 802.16e WiMax standard. The proposed architecture utilizes the value-reuse property of offset min-sum, block-serial scheduling of computations and turbo decoding message passing algorithm. The decoder has the following advantages: 55% savings in memory, reduction of routers by 50%, and increase of throughput by 2times when compared to the recent state-of-the-art decoder architectures.

We present a new multi-rate architecture for decoding block LDPC codes in IEEE 802.11n standard. The proposed architecture utilizes the value-reuse property of offset min-sum, block-serial scheduling of computations and turbo decoding message passing algorithm. Techniques of data-forwarding and out-of-order processing are used to deal with the irregularity of the codes. The decoder has the following advantages when compared to recent state-of-the-art architectures: 55% savings in memory, reduction of routers by 50% and increase of throughput by 2times.

The research Weather Surveillance Radar-1988 Doppler locally operated by the National Severe Storms Laboratory in Norman, OK, has the unique capability of collecting massive volumes of Level I time-series data over many hours, which provides a rich environment for evaluating our new postprocessing algorithms. In this letter, an approach of identifying tornado vortices in Doppler spectra is proposed and investigated using eigen analysis, cluster estimation, and fuzzy logic technique.

The adaptive Volterra filter has been successfully applied in nonlinear acoustic echo cancellation (AEC) systems and nonlinear line echo cancellation systems, but its applications are limited by its required computational complexity and slow convergence rate, especially for systems with long memory length. In this paper, by leveraging a multi-channel configuration of the Volterra filter and the sampling theory for nonlinear systems, we extend linear sub-band delay-less adaptive filter techniques to develop an efficient sub-band implementation of the adaptive Volterra filter. The developed sub-band configuration of the adaptive Volterra filter can greatly improve the convergence rate and reduce the computational complexity of nonlinear echo cancellers, which is shown by analyses and simulations.

In this paper, we present a new particle filtering (PF) algorithm for visual target tracking where Galerkin's projection method is used to generate the proposal distribution. Galerkin's method is a numerical approach to approximate the solution of a partial differential equation (PDE). By leveraging this method in concert with L2theory and the FFT, we obtain a new proposal which directly approximates the true state posterior distribution and is fundamentally different from various local linearizations or Kalman filter-based proposals. We apply this improved PF algorithm to track a human head in a video sequence. As predicted by theory and demonstrated by our experimental results, this new algorithm is highly effective for tracking targets which exhibit complex kinematics. The new proposal distribution given here captures the high probability area in the state space, thereby gleaning increased support from the true posterior distribution.

Single carrier digital communication systems such as M-PSK and M-QAM require the use of carrier synchronization in frequency and phase. In this paper, we present new optimizations that simplify the core operations involved in the synchronization process, namely the implementation of the numerically controlled oscillator (NCO) and the phase detector. These mathematical optimizations are generic in nature and can be applied to various DSP implementations such as fixed-point and floating-point implementations. One of the benefits of these optimizations is that look up tables for calculating typical trigonometry functions such as the complex exponential and arc tangent are not required. Results of some example implementations on the Texas Instruments fixed point DSP TMS320VC54x and the Analog Devices floating point DSP SHARC2106x are reported in the results section of this paper.

In this paper we propose a novel image restoration method that effectively combines a particle filter with wavelet shrinkage to achieve robust performance against inhomogeneous noise mixtures. Specifically, the particle filter acts to suppress outlier-rich components of the noise while, in a subsequent step, the wavelet domain shrinkage attenuates any remaining, less heavily tailed noise components. We present late breaking preliminary examples demonstrating excellent rejection of salt-and-pepper like Cauchy noise mixed with additive white Gaussian noise (AWGN). Although limited in scope, these preliminary results suggest that the combination of particle filters with more traditional restoration techniques is a powerful approach that can provide a new dimension of flexibility for addressing noise mixtures involving difficult nonlinear and non-Gaussian components.

Sequential importance sampling (SIS), also known as particle filtering, has drawn increasing attention recently due to its superior performance in nonlinear and non-Gaussian dynamic problems. In the SIS framework, estimation accuracy depends strongly on the choice of proposal distribution. In this paper we propose a novel SIS algorithm called PF-SP-PEKF that is based on a state partition technique and a parallel bank of extended Kalman filters designed to improve the accuracy of the proposal distribution. Our results show that this new approach yields a significantly improved estimate of the state, enabling the new particle filter to effectively track human subjects in a video sequence where the standard condensation filter fails to maintain track lock. Moreover, because of the improved proposal distribution, the new filter can achieve a given level of performance using fewer particles than its conventional SIS counterparts.

Recent research efforts based on a joint code-decoder design methodology have shown that it is possible to construct structured LDPC (low density parity check) codes without any performance degradation. An interesting new data independence property between the two classes of messages viz. check to bit and bit to check, involved in decoding, is observed. This property is a result of the specific structuring of the parity check matrix. By exploiting this property, we propose an architecture in which the computation of messages is synchronized such that each class of message is consumed immediately by the computational unit for another class of message. The internal memory of the check to bit units is increased in tune with the storage requirement of the check to bit messages. The separate memories for check to bit and bit to check messages are eliminated. This approach has memory savings of 75% and reduces the overall memory accesses by 66%.

This paper discusses a real-time digital signal processor (DSP)-based hierarchical neural network classifier capable of classifying both analog and digital modulation signals. A high-performance DSP processor, namely the TMS320C6701, is utilized to implement different kinds of classifiers including a hierarchical neural network classifier. A total of 31 statistical signal features are extracted and used to classify 11 modulation signals plus white noise. The modulation signals include CW, AM, FM, SSB, FSK2, FSK4, PSK2, PSK4, OOK, QAM16, and QAM32. A classification hierarchy is introduced and the genetic algorithm is employed to obtain the most effective set of features at each level of the hierarchy. The classification results and the number of operations on the DSP processor indicate the effectiveness of the introduced hierarchical neural network classifier in terms of both classification rate and processing time.

This paper investigates the collaboration of three numeral recognition techniques that will detect unconstrained handwritten numerals. Our research focuses on a feature-based approach, a method using eigenimages, and a structural analysis approach using wavelet transforms. These approaches will generate an estimate for the input numeral and a corresponding confidence value that in combination, yield a final decision. This paper shows that each individual method performs with a detection rate of better than 80%, and by combining the detection strategies, detection rates up to 94% are observed.

While inverters currently do exist, the main focus of this investigation is to provide a DSP based design that will significantly reduce the cost of the current system. The proposed approach relies on an efficient pushpull DC-DC converter, a digital signal processor (DSP) control mechanism for the DC-AC conversion, and a rigorous cost reduction approach for this inverter. A low cost Texas Instruments, TMS320LF2407 DSP provides the control for the DC-AC converter allowing compliance to the total harmonic distortion (THD) specification of less that 5%. Moreover, the DSP allows convenient communication between the fuel cell and the inverter. The results section of this paper presents our laboratory data. A new closed-loop control scheme is also introduced which reduces the distortion caused by non-linear loads.

At Texas Instruments, integrated circuits in production are continually becoming more power efficient and faster. The time associated with testing these designs, say with a with Taradyne test platform, represents a large cost of the overall price of these integrated circuits. This paper discusses the design and layout of a compact mixed signal printed circuit board using a digital signal processor (DSP) that can be used in the test environment. In general, we live in an analog world, but the processing and transfer of information is typically digital. Mixed signal designs offer the best of both worlds since these designs can take advantage of the processing power of a DSP, while operating in close proximity to the analog signals. The paper presents a discussion of our design along with a picture of our mixed signal board; more detailed discussions and pictures may be viewed at http://ee.tamu.edu//spl sim/mbyeary/mixedsignal.html.