In security surveillance at the perimeter of critical infrastructure, such as airports and power plants, approaching objects have to be detected and classified. Especially important is to distinguish between humans, animals and vehicles. In this paper, micro-Doppler data (from movement of internal parts of the target) have been collected with a small radar. From time-velocity diagrams of the data, physical features have been extracted and used in a Boosting classifier to distinguish between the classes "human", "animal" and "man-made object". This type of classifier has received much attention lately, but not in radar micro-Doppler classification. The classification result on the current data reaches 90% correct classification with this classifier. The ability to distinguish between humans and animals is good on this data. This classifier type gives insight into the classifier and the utilized features, and is easy to use. A comparison with a SVM (Support Vector Machine) classifier, which is common for micro-Doppler, has also been performed. © 2017 Institution of Engineering and Technology. All rights reserved.

Radar (RAdio Detection And Ranging) uses radio waves to detect the presence of a target and measure its position and other properties. This sensor has found many civilian and military applications due to advantages such as possible large surveillance areas and operation day and night and in all weather. The contributions of this thesis are within applied signal processing for radar in two somewhat separate research areas: 1) radar with array antennas and 2) radar with micro-Doppler measurements.

Radar with array antennas: An array antenna consists of several small antennas in the same space as a single large antenna. Compared to a traditional single-antenna radar, an array antenna radar gives higher flexibility, higher capacity, several radar functions simultaneously and increased reliability, and makes new types of signal processing possible which give new functions and higher performance.

The contributions on array antenna radar in this thesis are in three different problem areas. The first is High Resolution DOA (Direction Of Arrival) Estimation (HRDE) as applied to radar and using real measurement data. HRDE is useful in several applications, including radar applications, to give new functions and improve the performance. The second problem area is suppression of interference (clutter, direct path jamming and scattered jamming) which often is necessary in order to detect and localize the target. The thesis presents various results on interference signal properties, antenna geometry and subarray design, and on interference suppression methods. The third problem area is measurement techniques for which the thesis suggests two measurement designs, one for radar-like measurements and one for scattered signal measurements.

Radar with micro-Doppler measurements: There is an increasing interest and need for safety, security and military surveillance at short distances. Tasks include detecting targets, such as humans, animals, cars, boats, small aircraft and consumer drones; classifying the target type and target activity; distinguishing between target individuals; and also predicting target intention. An approach is to employ micro-Doppler radar to perform these tasks. Micro-Doppler is created by the movement of internal parts of the target, like arms and legs of humans and animals, wheels of cars and rotors of drones.

Using micro-Doppler, this thesis presents results on feature extraction for classification; on classification of targets types (humans, animals and man-made objects) and human gaits; and on information in micro-Doppler signatures for re-identification of the same human individual. It also demonstrates the ability to use different kinds of radars for micro-Doppler measurements. The main conclusion about micro-Doppler radar is that it should be possible to use for safety, security and military surveillance applications.

We have designed an experiment for low-cost indoor measurements of rank and other properties of direct and scattered signals with radar interference suppression in mind. The signal rank is important also in many other applications, for example, DOA (Direction of Arrival) estimation, estimation of the number of and location of transmitters in electronic warfare, and increasing the capacity in wireless communications. In real radar applications, such measurements can be very expensive, for example, involving airborne radars with array antennas. We have performed the measurements in an anechoic chamber with several transmitters, a receiving array antenna, and a moving reflector. Our experiment takes several aspects into account: transmitted signals with different correlation, decorrelation of the signals during the acquisition interval, covariance matrix estimation, noise eigenvalue spread, calibration, near-field compensation, scattering in a rough surface, and good control of the influencing factors. With our measurements we have observed rank, DOA spectrum, and eigenpatterns of direct and scattered signals. The agreement of our measured properties with theoretic and simulated results in the literature shows that our experiment is realistic and sound. The detailed description of our experiment could serve as help for conducting other well-controlled experiments. © Copyright 2016 Svante Björklund et al.

In security surveillance at the perimeter of critical infrastructure, such as airports and power plants, approaching objects have to be detected and classified. Especially important is to distinguish between humans, animals and vehicles. In this paper, micro-Doppler data (from movement of internal parts of the target) have been collected with a small radar of a low complexity and cost-effective type. From time-velocity diagrams of the data, some physical features have been extracted and used in a support vector machine classifier to distinguish between the classes "human", "animal" and "man-made object". Both the type of radar and the classes are suitable for perimeter protection. The classification result are rather good, 77% correct classification. Particularly interesting is the surprisingly good ability to distinguish between humans and animals. This also indicates that we can choose to have limitations in the radar and still solve the classification task.

The effect of the subarray design in a side-looking moving radar with a planar antenna on some clutter properties which are important for suppressing the clutter with STAP (Space-Time Adaptive Processing) is investigated by simulations. These properties are interference DOA Doppler Spectrum (DDS) and clutter rank. The conclusions from the work is that irregular antennas give less clutter in the DDS and that larger subarrays give lower clutter rank.

In bistatic radar with transmitter and receiver geographically separated the interference from ground clutter and the direct path signal transmitter-receiver will be strong and must be suppressed in order to detect the target. We apply FT-STAP (Fast-Time Space Time Adaptive Processing) to the suppression, which is unusual, and compare with conventional ST-STAP (Slow-Time STAP) by simulations in order to see whether FT-STAP is an alternative to ST-STAP. The performance of FT-STAP is much worse than of ST-STAP. We give an explanation for this, which also may be the basis for large improvements of FT-STAP. Moreover, we suggest two new performance measures.

Safety and security applications benefit from better situational awareness. Radar micro-Doppler signatures from an observed target carry information about the target's activity, and have potential to improve situational awareness. This article describes, compares, and discusses two methods to classify human activity based on radar micro-Doppler data. The first method extracts physically interpretable features from the time-velocity domain such as the main cycle time and properties of the envelope of the micro-Doppler spectra and use these in the classification. The second method derives its features based on the components with the most energy in the cadence-velocity domain (obtained as the Fourier transform of the time-velocity domain). Measurements from a field trial show that the two methods have similar activity classification performance. It is suggested that target base velocity and main limb cadence frequency are indirect features of both methods, and that they do often alone suffice to discriminate between the studied activities. This is corroborated by experiments with a reduced feature set. This opens up for designing new more compact feature sets. Moreover, weaknesses of the methods and the impact of non-radial motion are discussed.

In moving radar, e.g. airborne radar, the clutter from land and sea needs to be suppressed in order to detect the target. One approach to total cancellation of the clutter is Displaced Phase Center Antenna (DPCA). DPCA assumes that the antenna elements are positioned on a line parallel to the velocity vector of the radar platform so that the elements can take each others positions at different points of times. In a previous paper we saw that it is possible with other antenna element positions, e.g. in three dimensions, for a total cancellation of the clutter. We arrived at a theoretical condition for this. In this new paper we extend the condition with rotating array antennas. We also formulate an optimization problem for, besides the clutter cancellation, also maximizing the target signal.

Terrain-scattered interference (TSI), that is, jammer signals reflected on the earth's surface, is a significant problem to military airborne radar. In auxiliary beam TSI suppression, the TSI in the main radar beam is estimated by a single or several auxiliary beams and is subtracted from the main beam channel. The signal to subtract is the auxiliary beam signals fed through an estimate of the ‘reflection system’, which describes scattering on the surface. The authors first present results on the structure of this TSI suppression, on the estimation of the reflection system and on the quality of the estimate. Then the authors derive theoretical expressions for the signal-to-interference plus noise ratio (SINR) and the remaining TSI power for a single auxiliary beam. Since the SINR is directly connected to the radar performance, it can be seen what factors affect the performance and how. It was noted that when the estimated reflection system is missing one or more delays of the true system, the TSI filter cannot suppress the TSI signal completely. This phenomenon, which is called ‘TSI leakage’, has a very large impact on the performance. The SINR cannot be kept constant. Instead, an ‘SINR improvement’ can be defined.

Radar micro-Doppler signatures (MDS) of humans are created by movements of body parts, such as legs and arms. MDSs can be used in security applications to detect humans and classify their type and activity. Target association and tracking, which can facilitate the classification, become easier if it is possible to distinguish between human individuals by their MDSs. By this we mean to recognize the same individual in a short time frame but not to establish the identity of the individual. In this paper we perform a statistical experiment in which six test persons are able to distinguish between walking human individuals from their MDSs. From this we conclude that there is information in the MDSs of the humans to distinguish between different individuals, which also can be used by a machine. Based on the results of the best test persons we also discuss features in the MDSs that could be utilized to make this processing possible.