Tarun Soni

Performance evaluation of 2-D adaptive prediction filters for detection of small objects in image data

This work studies the performance of dimensional least mean square (TDLMS) adaptive filters as prewhitening filters for the detection of small objects in image data. The object of interest is assumed to have a very small spatial spread and is obscured by correlated clutter of much larger spatial extent. The correlated clutter is predicted and subtracted from the input signal, leaving components of the spatially small signal in the residual output. The receiver operating characteristics of a detection system augmented by a TDLMS prewhitening filter are plotted using Monte-Carlo techniques. It is shown that such a detector has better operating characteristics than a conventional matched filter in the presence of correlated clutter. For very low signal-to-background ratios, TDLMS-based detection systems show a considerable reduction in the number of false alarms. The output energy in both the residual and prediction channels of such filters is shown to be dependent on the correlation length of the various components in the input signal. False alarm reduction and detection gains obtained by using this detection scheme on thermal infrared sensor data with known object positions is presented.

Enhancement of images using the 2-D LMS adaptive algorithm

The use of adaptive filters for the enhancement of images is studied. In particular, the enhancement of images where the region of interest has a small spatial extent compared to the noise is considered. A two stage approach for enhancing the desired signal is presented. At each stage, a two dimensional adaptive filter employing the least mean square (LMS) algorithm is used. By properly choosing the adaptation step size, in the first stage the colored noise is whitened, and in the second stage the desired signal is recovered from the white noise. Procedures for selecting the step size are discussed. Computer simulations to support the results are also presented.<<ETX>>

Tracking model of an adaptive lattice filter for a linear chirp FM signal in noise

The paper studies the behavior of the partial correlation (PARCOR) coefficients and the output misadjustment of the stochastic gradient adaptive lattice filter in response to a complex linear chirp FM signal in white Gaussian noise. Analytic expressions for the optimal PARCOR coefficients of the filter are derived. Analytic as well as iterative models for a three-stage filter are also derived. The analytic expressions show that the tracking and convergence properties of the filter are separate phenomena. Simulation results also show that the spectral contents of the PARCOR coefficients for the stochastic gradient update algorithm consist of a stationary and a linearly swept component. A single-stage model is developed to explain this behavior. Finally, output misadjustment plots for the filter show that an optimum value for the forgetting factor can be obtained to minimize the misadjustment, but the value required to achieve local minimum misadjustment varies with each stage of the filter. It is shown that in applications where the input has a high signal-to-noise ratio (SNR), the misadjustment decreases rapidly at each successive stage, thus implying that relatively short filter lengths are sufficient to provide effective tracking. >

Adaptive interference suppression for CDMA based PCN systems

A number of different techniques for the suppression of narrowband interference due to overlay in code division multiple access (CDMA) systems have previously been studied. The relative performances of three such techniques for a fading channel, multi-user CDMA system are described. Personal communication network systems using suppression filters based on the transversal least mean square (LMS) and the lattice and transform domain algorithms are studied. Bit error rates are found by simulations for different suppression filters. The overlay signal is assumed to be narrowband binary phase shift keying (BPSK) and the performance of these systems is studied for varying BPSK carrier frequency offsets. Since both the lattice and LMS filters converge to the Wiener filter, their relative performance is also studied during the on/off transient of the interference. It is found that the LMS and recursive least square (RLS) perform similarly under stationary interference. The RLS is less susceptible to changes in the interference due to its faster convergence.<<ETX>>

Adaptive whitening filters for small target detection

This paper studies the performance of the two dimensional least mean square adaptive filter as a prewhitening filter for detection systems. In two dimensional infrared sensor data, the clutter is correlated and much wider in spatial extent than the signal of interest. The two dimensional adaptive filter can be trained to adapt and predict the clutter, thereby enabling the error channel output to contain the signal of interest in white noise. Performance of the adaptive prewhitener, in terms of local signal to clutter ratios(LSCR) and the gain obtained is described. The gain in LSCR due to this augmenting filter, is shown to depend on the statistics of the background clutter, in particular on the local mean. It is shown that, as the amount of color in the background clutter increases, the performance of the conventional matched filter performance degrades much more than the performance of a detector based on the augmenting prewhitener.

Recursive estimation techniques for detection of small objects in infrared image data

A recursive detection scheme for point targets in infrared images is described. Estimation of the background noise is done using a weighted autocorrelation matrix update method and the detection statistic is calculated using a recursive technique. A weighting factor allows the algorithm to have finite memory and deal with nonstationary noise characteristics. The detection statistic is created by using a matched filter for colored noise, using the estimated noise autocorrelation matrix. The relationship between the weighting factor, the nonstationarity of the noise and the probability of detection is described. Some results on one- and two-dimensional infrared images are presented.<<ETX>>

SIGNAL ENHANCEMENT IN NOISE AND CLUTTER CORRUPTED IMAGES USING ADAPTIVE PREDICTIVE FILTERING TECHNIQUES

The recovery of an original image from its corrupted version is of importance in a number of applications. The detection of small and dim targets is one such problem, requiring the enhancement of target signals and suppression of noise and clutter in the image. Conventional methods like matched filtering require a priori knowledge of the target intensity spread function, the clutter correlation characteristics, etc. These techniques are difficult to implement if the image is nonstationary. This paper describes an adaptive clutter whitening technique which increases signal detectability in colored noise and clutter. Signal enhancement is based on the intrinsic differences in the spatial extent of the target relative to the clutter. An adaptive spatial filter is used to whiten the clutter present in the image. The output of such an adaptive spatial filter, termed the adaptive clutter whitener (ACW), is then passed on to a matched filter based detector. The receiver operating characteristics are found using Monte- Carlo simulation techniques, both for the ACW augmented matched filter detector and a conventional matched filter detector. It is seen that for highly correlated clutter, the ACW augmented detector has a better ROC than one without a prewhitening filter.

Detection of point objects in spatially correlated clutter using two dimensional adaptive prediction filtering

The performance of a two-dimensional least-mean-square adaptive filter as a prewhitening filter for the detection of small signals in infrared image data is studied. The spatially broad clutter with long correlation length is seen to be narrowband in the two dimensional frequency domain. This narrowband clutter is predicted and subtracted from the input, leaving the spatially small signal in the residual output. The output energy in the residual and prediction channels of such a filter is seen to depend on the correlation length of the various components in the input signal, thus permitting the separation of short correlation targets from the longer correlation clutter. False alarm improvements and detection gains obtained by using this detection scheme on thermal infrared sensor data with known target points are presented.<<ETX>>

A compact array processor based on self-timed simultaneous bidirectional signalling

A bundled self-timed simultaneous bidirectional signaling protocol is used to remove the clock dependency and minimize latency in the communication network of an array processor. The use of the same data for bidirectional data transfer effectively doubles the I/O bandwidth of such a communication network. This also permits making the input data transfer cycles independent of the output data transfer cycles, thus decoupling the data and resulting waves in a wavefront array processor. As a vehicle to demonstrate the merit of this protocol and to compare it to the more conventional wavefront array based processing (WAP) protocols, the design of a two-dimensional array processing structure for matrix multiplication using such a protocol is described. A computation element based on a bit-serial multiplier accumulator is chosen with a data dependent computation time. This permits the design of an array where neither the computation nor the communication speeds are bound by any clock speeds. It is shown that such an approach reduces the latency of the array structure without increasing I/O pin count.<<ETX>>

Time-varying behavior of the PARCOR coefficients of lattice filters with nonstationary AR inputs

The authors describe the behavior of the partial correlation coefficients of lattice filters based on the recursive least squares (RLS) algorithm in the presence of a nonstationary input. The input is an autoregressive (AR) process and the coefficients of the generating filter are allowed to change with time, leading to a time-varying autocorrelation function at the input. For such an input the optimal Wiener-Hopf coefficients of a lattice filter are found. These are compared with the expected PARCOR coefficients of the RLS lattice filter, which are a function of the weighting parameter. It is shown that the PARCOR coefficients of the RLS lattice filter have two terms and tend to the Wiener-Hopf optimal weights asymptotically.<<ETX>>