Improved Feedback Loop for Adaptive Arrays

This paper is concerned with the problem of time constants in adaptive arrays. The paper presents a improved form of an adaptive array feedback loop, which has the property that its time constants are fixed. This property is an advantage over the well-known least mean square (LMS) loop, for which time constants depend on received signal power. Fixed time constants are of interest because they simplify dynamic range problems for adaptive arrays in communication and radar systems.     

Angle-of-Arrival Estimation Using an Adaptive Antenna Array

A direction-finding technique is presented that is capable of simultaneously estimating the arrival angles of multiple signals. Pulsed as well as continuous signals can be handled with the signal form only approximately specified. An adaptive antenna array is used as a processing device in the estimation technique. The effect of input signal and feedback loop parameters upon estimate bias is discussed.     

Rapid convergence by cascading Applebaum adaptive arrays

An adaptive array architecture is described which has improved convergence speed over the conventional Applebaum array when the eigenvalue spread of the input signal covariance matrix is large. The architecture uses N+1 Applebaum adaptive arrays in a two-layer cascaded configuration. The gain constants in the first layer are set so that large interfering sources are quickly nulled, but small interfering sources are suppressed more slowly. Since the first layer removes the large interfering signals, the gain constant for the second layer can be set to a large value to quickly null the smaller interferers. The adaptation time is examined for several combinations of signal levels and array sizes. It is shown that, in many signal environments, the computational requirements for the cascaded array compare favorably with those of conventional sample matrix inversion (SMI) methods for large arrays     

Reference Loop Phase Shift in an N-Element Adaptive Array

Adaptive arrays based on the LMS algorithm require the generation of a reference signal which is usually derived from the array output. A particular problem associated with this technique is that of a phase shift in the reference signal loop. The effects of this phase shift on the performance of an N-element adaptive array are discussed. It is shown that a reference loop phase shift causes the array weights to cycle, thereby frequency translating the signals at the output. The weight-cycling frequency is related to various system parameters of an N-element array. In particular, it is observed that the cycling frequency increases as the number of antennas (N) increases.     

Effect of Single-Bit Digitization in Adaptive Array Control Loops

The properties of an adaptive array antenna, including transient response rate and control loop noise, depend on the intensity of the external noise field. The dependence can be reduced by 1-bit digitization of the real and imaginary parts of the signals from which the envelopes in the control loops are formed. This can be done without degrading the performance of the adaptive array.     

The Effect of Integrator Pole Position on the Performance of an Adaptive Array

The LMS adaptive array requires an integrator in each weight feedback control loop. In practice the integrator is often replaced by a low-pass filter, i.e., by a filter with a single pole at s = - ? (where s is complex frequency). The effect of this pole position on array performance is examined. It is shown that to obtain optimal performance from the array, ? must be less than k?2, where k is the loop gain and ?2 is the thermal noise power per element. When at exceeds k?2, the output signal-to-inter ference-plus-noise ratio from the array is degraded for intermediate values of interference power.     

The Effect of Differential Time Delays in the LMS Feedback Loop

The effect of differential time delay in the feedback loops of an LMS adaptive array is examined. Differential time delay is shown to have two effects on array performance. First, it causes the weights to oscillate during weight transients. Second, it degrades the output signal-to-interference-plus-noise ratio (SINR) from the array. Weight oscillation occurs when the phase shifts in the LMS loop are not matched at the signal carrier frequency. SINR degradation depends on signal bandwidth: the wider the bandwidth, the larger the degradation.     

Weight Jitter Phenomena in Adaptive Array Control Loops

An analysis of weight jitter in adaptive array control loops is presented. lt is shown that steady state weight jitter and statistical dependence between input signal and weight processes are related factors. A Markov characterization for the first-order adaptive array loop is presented and Fokker-Planck techniques are used to obtain the steady state probability density of the weight process for the case of a one-loop array. From this density the mean and the variance of the weight process are obtained and the dependence of these statistics upon loop and input signal parameters is explored.     

A flexible processor for a digital adaptive array radar

A digital beamforming processor for an adaptive array radar is described. The functionality and the architecture of the processor are strongly driven by a goal of achieving adaptive null depths in the 60-dB to 70-dB range, which necessitates substantial preprocessing of each channel. In particular, conversion to baseband quadrature channels is accomplished digitally using a single A/D converter per channel, and FIR (finite impulse response) equalizing filters are employed in each channel to match channel transfer functions. The processor is highly modular, and this not only distributes the total processing load, but also the I/O (input/output) bandwidth requirement. This is accomplished by distributing the adaptive beamforming algorithm systolically across a linear array of processing nodes. The processor is expandable to a different number of channels and sufficiently flexible to be applied to other problems of an array signal processing nature. Experimental data presented demonstrate that the processor is capable of supporting channel-to-channel cancellation of interfering signals to the level of -65 dB     

新一代歼击机超机动飞行的动态逆控制

 根据反馈线性化理论, 讨论了神经网络自适应非线性动态逆控制设计。首先根据时标分离原则, 采用动态逆方法设计快回路和慢回路控制器; 其次提出基于模型逆的神经网络非线性直接自适应控制方案, 设计一种在线神经网络用于补偿模型逆误差。仿真表明, 该控制方案具有较好的自适应能力和鲁棒性。     

CFAR integration processors in randomly arriving impulseinterference

Time diversity transmission is often used to circumvent the high probability of a deep fade on a single transmission which may result in loss of the signal. One way to combat deep fades is to postdetection integrate the received observations from each range resolution cell. The false alarm rate of the postdetection integrator (PI) is extremely sensitive to randomly arriving impulse interference. Such interfering pulses may be unintentionally generated by nearby radars or intentionally generated by pulse jammers seeking to destroy the visibility of the radar. The binary integrator (PI) which uses an M-out-of-L decision rule is insensitive to at most M-1 interfering pulses. We consider the adaptive implementation of the PI and BI detectors for constant false alarm rate (CFAR) operation. We show that the CFAR BI detector when the “AND” (L-out-of-L) decision rule is used exhibits more robust false alarm control properties in the presence of impulse interference at the expense of severe detection loss when no interference is present. The CFAR adaptive PI (API) detector is proposed to alleviate this problem. The CFAR API detector implements an adaptive censoring algorithm which determines and censors with high probability the interference samples thereby achieving robust false alarm control in the presence of interference and optimum detection performance in the absence of interference     

Application of Adaptive Arrays to Suppress Strong Jammers in the Presence of Weak Signals

The purpose of this paper is to demonstrate that an adaptive array can be used to acquire weak signals, whose direction and timing are unknown, in an environment of stronger jammers. Specifically, it is shown that in an environment of one weak signal and one strong jammer, the adaptive array output suppresses the strong jammer below the weak signal by roughly the same amount that the jammer exceeded the signal before adaptation.     

Effects of finite weight resolution and calibration errors on theperformance of adaptive array antennas

Adaptive antennas are now used to increase the spectral efficiency in mobile telecommunication systems. A model of the received carrier-to-interference plus noise ratio (CINR) in the adaptive antenna beamformer output is derived, assuming that the weighting units are implemented in hardware, The finite resolution of weights and calibration is shown to reduce the CINR. When hardware weights are used, the phase or amplitude step size in the weights can be so large that it affects the maximum achievable CINR. It is shown how these errors makes the interfering signals “leak” through the beamformer and we show how the output CINR is dependent on power of the input signals. The derived model is extended to include the limited dynamic range of the receivers, by using a simulation model. The theoretical and simulated results are compared with measurements on an adaptive array antenna testbed receiver, designed for the GSM-1800 system. The theoretical model was used to find the performance limiting part in the testbed as the 1 dB resolution in the weight magnitude. Furthermore, the derived models are used in illustrative examples and can be used for system designers to balance the phase and magnitude resolution and the calibration requirements of future adaptive array antennas     

An LMS Adaptive Array for Multipath Fading Reduction

Multipath fading often poses a serious hindrance in radiocommunication. The application of a least-mean-square (LMS)adaptive array to the problem of multipath fading reduction is discussed. However, it is known that multipath components are in general correlated with one another. We examine the effect of the correlation on the performance of the LMS adaptive array. When the correlation coefficient does not equal or approximate 1, the LMS adaptive array suppresses the multipath signals significantly by nulling. On the other hand, when the correlation coefficient nearly equals 1, the LMS adaptive array prevents the output signalpower from decreasing. Therefore, the LMS adaptive array mayreduce the multipath fading effectively for any correlation coefficient value. A reference signal in the LMS adaptive array is also discussed. It is shown that synchronization in the referencesignal generation must be extremely accurate. Moreover, aprocessor configuration is proposed which may generate thereference signal with the required accuracy.     

Upper Bound of a Loop Gain in a Power Inversion Adaptive Array

A loop gain is an important parameter in a power inversion adaptive array. The upper bound value of the loop gain is obtained which prevents the array suppressing a desired signal seriously. This result is very useful for determining the loop gain.     

The Effect of Interfering Signals on the Performance of Angle of Arrival Estimates

The performance of angle of arrival estimates using an array in the presence of interfering signals is evaluated using the Cramer-Rao bound. To do this, a model for interference is developed which presents the interference as narrowband, temporally white, but spatially correlated, noise. The bound is evaluated and it is shown to depend upon the ambiguity function of the array and spatial correlation matrix of the noise plus interference. Motivation of the model in the context of air traffic control and sonar surveillance is presented.     

Performance of a Modified Applebaum Adaptive Array

The performance of a modified Applebaum adaptive array is studied. The new array is obtained by removing the desired signal component from the output signal fed back to the correlator of an Applebaum type adaptive array. Various signal scenarios, including single desired signals or multiple simultaneous desired signals are examined. The new array is less sensitive to beam pointing errors and does not cause power inversion of desired signals. In the case of multiple simultaneous desired signals, the new array does not degrade the SNRs of strong desired signals.     

An Lms Adaptive Array Using a Pilot Signal

A least mean square (LMS) adaptive array requires a reference signal. When the desired signal contains a pilot signal, it may be used as the reference signal. In this paper the steady-state performance of an LMS adaptive array in which the pilot signal is used as the reference signal is examined. It is shown that the LMS adaptive array occasionally suppresses the desired signal. The loop gain, which is an important parameter, is also considered.     

An Adaptive Array Signal Processing Algorithm for Communications

An algorithm is described for initial synchronization in a communication system with a digital adaptive array. This algorithm can also be used for message extraction. A set of consecutive complex video samples of the array output is processed to obtain optimum adaptive array weights, based on a least mean square (LMS) error criterion. This computation is performed for each of the possible alternative signals which may be present during an observation interval. The correct synchronization time or message symbol is selected as the one which yields the minimum LMS error. Assuming orthogonality of the alternative codes, a probability distribution for the output of this processor has been derived.     

Multistatic adaptive pulse compression

A new technique denoted as multistatic adaptive pulse compression (MAPC) is introduced which exploits recent work on adaptive pulse compression (APC) in order to jointly separate and pulse compress the concurrently received return signals from K proximate multistatic radars operating (i.e., transmitting) within the same spectrum. For the return signal from a single pulse of a monostatic radar, APC estimates the particular receive filter for a given range cell in a Bayesian sense reiteratively by employing the matched filter estimates of the surrounding range cell values as a priori knowledge in order to place temporal (i.e., range) nulls at the relative ranges occupied by large targets and thereby suppress range sidelobes to the level of the noise. The MAPC approach generalizes the APC concept by jointly estimating the particular receive filter for each range cell associated with each of several concurrently-received radar return signals occupying the same spectrum. As such, MAPC is found to enable shared-spectrum multistatic operation and is shown to yield substantial performance improvement in the presence of multiple spectrum-sharing radars as compared with both standard matched filters and standard least-squares mismatched filters