Proximity ranging is very important in many applications. Ultrasonic sensors have proven to be very cost effcctive tools for this purpose. Most of the currently available time-of-flight based acoustic proximity ranging systems use the conventional matched filter based time delay estimation approaches to measure short distances between prox-irnity objects. However, in the presence of strong and closely spaced secondary echoes, the aforernentioned matched filter based algorithms tend to fail or suffer from severe pertformanee degradations due to their poor resolutions. In this paper, a computation-ally efficient time delay estimation algorithm, referred to as Multi-PEARS (Multi-echo Parameter Estimation for Acoustic Ranging Systems), is described for the joint proxim-ity range estimation and secondary echo mitigation. The effectiveness of the proposed algorithm is demonstrated by both numerical and experimental examples.

Space-time adaptive processing (STAP) is an effective method for detecting moving target in airborne/ spaceborne radar. However, the detection of fast air moving targets from high speed platform is challenged by the range walk of both the clutter and moving targets. It is well known that keystone formatting can be used to compensate for the range walk of multiple moving targets simultaneously without using the knowledge of the motion parameters. However, in the presence of serious Doppler ambiguity of fast air moving targets, distribution of the clutter will be affected by the keystone formatting matched to the ambiguity number of targets, and as a result the STAP performance will degrade. In this paper, a novel STAP method is proposed for the detection of fast air moving targets from high speed platform, which can deal with the range walk of both clutter and targets. Effectiveness of the new method is verified via simulation examples.

High-resolution inverse synthetic aperture radar (ISAR) imaging and recognition of ship target is very important for many applications. Although the principle of ISAR imaging of ship target on the sea is the same as that of flying target in the sky, the former usually has more complex motion (fluctuation with the oceanic waves) than the latter, which makes the motion compensation very difficult. However, the change in phase chirp rate caused by the complex motion of ships will deteriorate the azimuth focusing quality. In this paper, we first model the complex motion of ship target with cubic phase terms (parameterised on chirp rate and its change rate), then a new ISAR imaging method, referred to as TC-DechirpClean, is proposed, which estimates the chirp rate and the change rate of chirp rate of all scatters in the time– chirp distribution plane. Both numerical and experimental results are provided to demonstrate the performance of the proposed method.

We present an adaptive detection (AD) for broadband communication in the presence of co-channel interference (CCI). In the case of imperfect channel estimation, a robust AD (RAD) is proposed to maintain the good performance. We also show that both AD and RAD can be further improved by using an iterative scheme, referred to as iterative AD (IAD) and iterative RAD (IRAD), respectively. Numerical examples show that all the proposed methods outperform the conventional maximum likelihood sequence estimator (MLSE) significantly, and that they are also computationally more efficient.

A new algorithm for filling sparse aperture synthetic aperture radar (SAR)/inverse SAR (ISAR) data, which applies for widely gapped apertures, is proposed in this letter. An Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT)-based parametric approach is first used to estimate the power distribution with the sparse data.With the estimated power spectrum as prior information, by minimizing a weighted norm as a constraint, the full aperture data can be estimated. Although the algorithm is proposed for the sparse aperture interpolation in SAR/ISAR, it can be applied to other gapped data spectral estimation problems as well. Both numerical and experimental examples are provided to demonstrate the performance of the proposed algorithm.